Tag Archives: security

Cloudflare Account Permissions, how to use them, and best practices

Post Syndicated from Joseph So original http://blog.cloudflare.com/permissions-best-practices/

Cloudflare Account Permissions, how to use them, and best practices

Cloudflare Account Permissions, how to use them, and best practices

In the dynamic landscape of modern web applications and organizations, access control is critical. Defining who can do what within your Cloudflare account ensures security and efficient workflow management. In order to help meet your organizational needs, whether you are a single developer, a small team, or a larger enterprise, we’re going to cover two changes that we have developed to make it easier to do user management, and best practices on how to use these features, alongside existing features in order to scope everything appropriately into your account, in order to ensure security while you are working with others.

What are roles?

In the preceding year, Cloudflare has expanded our list of roles available to everyone from 1 to over 60, and we are continuing to build out more, better roles. We have also made domain scoping a capability for all users. This prompts the question, what are roles, and why do they exist?

Roles are a set of permissions that exist in a bundle with a name. Every API call that is made to Cloudflare has a required set of permissions, otherwise an API call will return with a 403. We generally group permissions into a role to allow access to a set of capabilities that allow the use of a Cloudflare Product, or that represent everything needed to fulfill a job function.

As of today, we have two entities that we can assign roles: the first entity is a user, representing a profile, or an actor, which we generally require an email address to represent.

The second entity is a token, which represents delegation of a subset of a user’s permissions to be used for programmatic purposes.

What is scope?

Permissions are useless without an appropriate actor, and a scope. For every action a user can take, they must be directed to the appropriate resource, which is what we refer to as a scope.

When a user first signs up to Cloudflare, they are provided a Cloudflare user, as well as an account. Accounts can be shared with others. Accounts act as our traditional resource boundary, and granting permissions at the account level means that those permissions apply to all zones, and all other resources within the account.

Within accounts however, there are zones, R2 Buckets, Workers, and other resources. We are working on expanding the types of scopes that can be set, and as of now, you can scope access to a selective number of zones, or create tokens that only allow access into specified R2 buckets.

While our list of available roles is going to continue to grow, I want to go into some detail about how to use the roles right now, and how to use them to their full potential

What are the different types of roles and scopes we have today?

For most of the Cloudflare users and use cases out there, our traditional role model at the account level is the easiest to use. These roles can be viewed by selecting the scope of All domains.

Cloudflare Account Permissions, how to use them, and best practices

As of today, there are 40+ Roles available at the account level. These provide access to a capability across the whole account, with no further scoping. Once these roles are provided to a user, they are able to complete a limited set of actions across any zones in your account. We intend to cover off more capabilities in this list, and will continue to add more roles.

When you want to grant access to a specific zone, or list of zones, the best way to go about that is to use a domain scoped role.

Cloudflare Account Permissions, how to use them, and best practices

A single domain can be added similar to the above, and granting explicit scope to a domain implicitly denies access to other domains.

If you are looking to grant access to multiple domains simultaneously, in order to represent all staging zones for example, you can place them into a domain group. These can be revisited and edited within the Configurations → Lists page under Manage Account.

Cloudflare Account Permissions, how to use them, and best practices

Best practices for creating domain groups is to group a set of similar domains together, such that you can reuse it for every user on your account.

Other best practices when assigning memberships

It is always best practice to explicitly define what you are granting access to, as Cloudflare’s permissioning system defers to a fail closed design. If you do not grant access to it, they will not have access to it.

We model all the different types of roles in an additive capacity, and we’re going to move forward with creating more capability specific roles. Multiple roles can be assigned given a scope. We recommend against explicitly “excluding” objects, because it can lead to some complex permission processing.

An example of this may be your organization’s billing administrator. You may want to grant them both Billing and Analytics, but exclude them from web administration activities by explicitly granting them those two roles.

Exciting changes you will see from us soon include the capability to “stack” multiple sets of policies on top of one another. We are currently rolling this out, and some users will already have the ability to define a set of permissions for one set of domains, and an increased set of permissions for another.

This will come in handy if you are managing multiple environments within one account, and want to grant differing levels of access to say a development and staging domain.

We also recognize that Cloudflare has many resources beyond Accounts and zones, and we are currently experimenting with adding scoping to other objects. As of today, you can specify R2 Tokens to only access certain buckets, and I look forward to adding this capability to more resources.

Best practices when delegating access to tokens

Memberships and users tend to use Cloudflare in an interactive capacity, but many organizations use Cloudflare programmatically.

A new capability we are rolling out to all users soon is the capability to limit API access: on your account as a whole, or on a per-user basis.

All programmatic access to Cloudflare at this time is managed at a per-user basis, representing a delegation of that user’s access to their set of accounts. Programmatic access is always bounded by a user’s access, and many of our user’s service accounts have a wide set of access that is split into context specific tokens.

As a Super administrator, if you want to restrict programmatic access to your account, this toggle will become available on the members page.

We recommend keeping this functionality turned off, unless you explicitly want to grant the ability to use the API to specific users, which can also be controlled via a dropdown per user. We have seen some organizations use this capability to centralize the creation of API Tokens into a single service user.

Cloudflare Account Permissions, how to use them, and best practices
Cloudflare Account Permissions, how to use them, and best practices

Cloudflare recommends the use of API Tokens wherever possible. API tokens have the ability to be scoped down to a smaller subset of a user’s access, instead of granting access to all of it.

When building out a set of permissions for an API Token, we have the same scoping capability that was visible in membership roles.

Account Scoping:

Cloudflare Account Permissions, how to use them, and best practices

Domain/Zone Scoping:

Cloudflare Account Permissions, how to use them, and best practices

Cloudflare’s roles are meant to provide the flexibility to provide the least amount of privilege possible, in order to keep your Cloudflare resources safe. Recent improvements have included a number of capability specific roles, as well as the ability to lock down API Access. Future improvements will include the ability to grant multiple policies to individual users, as well as more scopes.

What’s next

All users are able to use our new roles, and there will be several rolling improvements, including the capability to lock down API access, as well as assign multiple policies to users.

2023 H1 IRAP report is now available on AWS Artifact for Australian customers

Post Syndicated from Patrick Chang original https://aws.amazon.com/blogs/security/2023-h1-irap-report-is-now-available-on-aws-artifact-for-australian-customers/

Amazon Web Services (AWS) is excited to announce that a new Information Security Registered Assessors Program (IRAP) report (2023 H1) is now available through AWS Artifact. An independent Australian Signals Directorate (ASD) certified IRAP assessor completed the IRAP assessment of AWS in August 2023.

The new IRAP report includes an additional six AWS services, as well as the new AWS Local Zone in Perth, that are now assessed at the PROTECTED level under IRAP. This brings the total number of services assessed at the PROTECTED level to 145.

The following are the six newly assessed services:

For the full list of services, see the IRAP tab on the AWS Services in Scope by Compliance Program page.

AWS has developed an IRAP documentation pack to assist Australian government agencies and their partners to plan, architect, and assess risk for their workloads when they use AWS Cloud services.

We developed this pack in accordance with the Australian Cyber Security Centre (ACSC) Cloud Security Guidance and Cloud Assessment and Authorisation framework, which addresses guidance within the Australian Government Information Security Manual (ISM), the Department of Home Affairs’ Protective Security Policy Framework (PSPF), and the Digital Transformation Agency Secure Cloud Strategy.

The IRAP pack on AWS Artifact also includes newly updated versions of the AWS Consumer Guide and the whitepaper Reference Architectures for ISM PROTECTED Workloads in the AWS Cloud.

Reach out to your AWS representatives to let us know which additional services you would like to see in scope for upcoming IRAP assessments. We strive to bring more services into scope at the PROTECTED level under IRAP to support your requirements.

 
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Want more AWS Security news? Follow us on Twitter.

Patrick Chang

Patrick Chang

Patrick is the Asia Pacific and Japan (APJ) Audit Lead at AWS. He leads security audits, certifications, and compliance programs across the APJ region. Patrick is a technology risk and audit professional with over a decade of experience. He is passionate about delivering assurance programs that build trust with customers and provide them assurance on cloud security.

How to implement cryptographic modules to secure private keys used with IAM Roles Anywhere

Post Syndicated from Edouard Kachelmann original https://aws.amazon.com/blogs/security/how-to-implement-cryptographic-modules-to-secure-private-keys-used-with-iam-roles-anywhere/

AWS Identity and Access Management (IAM) Roles Anywhere enables workloads that run outside of Amazon Web Services (AWS), such as servers, containers, and applications, to use X.509 digital certificates to obtain temporary AWS credentials and access AWS resources, the same way that you use IAM roles for workloads on AWS. Now, IAM Roles Anywhere allows you to use PKCS #11–compatible cryptographic modules to help you securely store private keys associated with your end-entity X.509 certificates.

Cryptographic modules allow you to generate non-exportable asymmetric keys in the module hardware. The cryptographic module exposes high-level functions, such as encrypt, decrypt, and sign, through an interface such as PKCS #11. Using a cryptographic module with IAM Roles Anywhere helps to ensure that the private keys associated with your end-identity X.509 certificates remain in the module and cannot be accessed or copied to the system.

In this post, I will show how you can use PKCS #11–compatible cryptographic modules, such as YubiKey 5 Series and Thales ID smart cards, with your on-premises servers to securely store private keys. I’ll also show how to use those private keys and certificates to obtain temporary credentials for the AWS Command Line Interface (AWS CLI) and AWS SDKs.

Cryptographic modules use cases

IAM Roles Anywhere reduces the need to manage long-term AWS credentials for workloads running outside of AWS, to help improve your security posture. Now IAM Roles Anywhere has added support for compatible PKCS #11 cryptographic modules to the credential helper tool so that organizations that are currently using these (such as defense, government, or large enterprises) can benefit from storing their private keys on their security devices. This mitigates the risk of storing the private keys as files on servers where they can be accessed or copied by unauthorized users.

Note: If your organization does not implement PKCS #11–compatible modules, IAM Roles Anywhere credential helper supports OS certificate stores (Keychain Access for macOS and Cryptography API: Next Generation (CNG) for Windows) to help protect your certificates and private keys.

Solution overview

This authentication flow is shown in Figure 1 and is described in the following sections.

Figure 1: Authentication flow using crypto modules with IAM Roles Anywhere

Figure 1: Authentication flow using crypto modules with IAM Roles Anywhere

How it works

As a prerequisite, you must first create a trust anchor and profile within IAM Roles Anywhere. The trust anchor will establish trust between your public key infrastructure (PKI) and IAM Roles Anywhere, and the profile allows you to specify which roles IAM Roles Anywhere assumes and what your workloads can do with the temporary credentials. You establish trust between IAM Roles Anywhere and your certificate authority (CA) by creating a trust anchor. A trust anchor is a reference to either AWS Private Certificate Authority (AWS Private CA) or an external CA certificate. For this walkthrough, you will use the AWS Private CA.

The one-time initialization process (step “0 – Module initialization” in Figure 1) works as follows:

  1. You first generate the non-exportable private key within the secure container of the cryptographic module.
  2. You then create the X.509 certificate that will bind an identity to a public key:
    1. Create a certificate signing request (CSR).
    2. Submit the CSR to the AWS Private CA.
    3. Obtain the certificate signed by the CA in order to establish trust.
  3. The certificate is then imported into the cryptographic module for mobility purposes, to make it available and simple to locate when the module is connected to the server.

After initialization is done, the module is connected to the server, which can then interact with the AWS CLI and AWS SDK without long-term credentials stored on a disk.

To obtain temporary security credentials from IAM Roles Anywhere:

  1. The server will use the credential helper tool that IAM Roles Anywhere provides. The credential helper works with the credential_process feature of the AWS CLI to provide credentials that can be used by the CLI and the language SDKs. The helper manages the process of creating a signature with the private key.
  2. The credential helper tool calls the IAM Roles Anywhere endpoint to obtain temporary credentials that are issued in a standard JSON format to IAM Roles Anywhere clients via the API method CreateSession action.
  3. The server uses the temporary credentials for programmatic access to AWS services.

Alternatively, you can use the update or serve commands instead of credential-process. The update command will be used as a long-running process that will renew the temporary credentials 5 minutes before the expiration time and replace them in the AWS credentials file. The serve command will be used to vend temporary credentials through an endpoint running on the local host using the same URIs and request headers as IMDSv2 (Instance Metadata Service Version 2).

Supported modules

The credential helper tool for IAM Roles Anywhere supports most devices that are compatible with PKCS #11. The PKCS #11 standard specifies an API for devices that hold cryptographic information and perform cryptographic functions such as signature and encryption.

I will showcase how to use a YubiKey 5 Series device that is a multi-protocol security key that supports Personal Identity Verification (PIV) through PKCS #11. I am using YubiKey 5 Series for the purpose of demonstration, as it is commonly accessible (you can purchase it at the Yubico store or Amazon.com) and is used by some of the world’s largest companies as a means of providing a one-time password (OTP), Fast IDentity Online (FIDO) and PIV for smart card interface for multi-factor authentication. For a production server, we recommend using server-specific PKCS #11–compatible hardware security modules (HSMs) such as the YubiHSM 2, Luna PCIe HSM, or Trusted Platform Modules (TPMs) available on your servers.

Note: The implementation might differ with other modules, because some of these come with their own proprietary tools and drivers.

Implement the solution: Module initialization

You need to have the following prerequisites in order to initialize the module:

Following are the high-level steps for initializing the YubiKey device and generating the certificate that is signed by AWS Private Certificate Authority (AWS Private CA). Note that you could also use your own public key infrastructure (PKI) and register it with IAM Roles Anywhere.

To initialize the module and generate a certificate

  1. Verify that the YubiKey PIV interface is enabled, because some organizations might disable interfaces that are not being used. To do so, run the YubiKey Manager CLI, as follows:
    ykman info

    The output should look like the following, with the PIV interface enabled for USB.

    Figure 2:YubiKey Manager CLI showing that the PIV interface is enabled

    Figure 2:YubiKey Manager CLI showing that the PIV interface is enabled

  2. Use the YubiKey Manager CLI to generate a new RSA2048 private key on the security module in slot 9a and store the associated public key in a file. Different slots are available on YubiKey, and we will use the slot 9a that is for PIV authentication purpose. Use the following command to generate an asymmetric key pair. The private key is generated on the YubiKey, and the generated public key is saved as a file. Enter the YubiKey management key to proceed:
    ykman ‐‐device 123456 piv keys generate 9a pub-yubi.key

  3. Create a certificate request (CSR) based on the public key and specify the subject that will identify your server. Enter the user PIN code when prompted.
    ykman --device 123456 piv certificates request 9a --subject 'CN=server1-demo,O=Example,L=Boston,ST=MA,C=US' pub-yubi.key csr.pem

  4. Submit the certificate request to AWS Private CA to obtain the certificate signed by the CA.
    aws acm-pca issue-certificate \
    --certificate-authority-arn arn:aws:acm-pca:<region>:<accountID>:certificate-authority/<ca-id> \
    --csr fileb://csr.pem \
    --signing-algorithm "SHA256WITHRSA" \
    --validity Value=365,Type="DAYS"

  5. Copy the certificate Amazon Resource Number (ARN), which should look as follows in your clipboard:
    {
    "CertificateArn": "arn:aws:acm-pca:<region>:<accountID>:certificate-authority/<ca-id>/certificate/<certificate-id>"
    }

  6. Export the new certificate from AWS Private CA in a certificate.pem file.
    aws acm-pca get-certificate \
    --certificate-arn arn:aws:acm-pca:<region>:<accountID>:certificate-authority/<ca-id>/certificate/<certificate-id> \
    --certificate-authority-arn arn:aws:acm-pca: <region>:<accountID>:certificate-authority/<ca-id> \
    --query Certificate \
    --output text > certificate.pem

  7. Import the certificate file on the module by using the YubiKey Manager CLI or through the YubiKey Manager UI. Enter the YubiKey management key to proceed.
    ykman --device 123456 piv certificates import 9a certificate.pem

The security module is now initialized and can be plugged into the server.

Configuration to use the security module for programmatic access

The following steps will demonstrate how to configure the server to interact with the AWS CLI and AWS SDKs by using the private key stored on the YubiKey or PKCS #11–compatible device.

To use the YubiKey module with credential helper

  1. Download the credential helper tool for IAM Roles Anywhere for your operating system.
  2. Install the p11-kit package. Most providers (including opensc) will ship with a p11-kit “module” file that makes them discoverable. Users shouldn’t need to specify the PKCS #11 “provider” library when using the credential helper, because we use p11-kit by default.

    If your device library is not supported by p11-kit, you can install that library separately.

  3. Verify the content of the YubiKey by using the following command:
    ykman --device 123456 piv info

    The output should look like the following.

    Figure 3: YubiKey Manager CLI output for the PIV information

    Figure 3: YubiKey Manager CLI output for the PIV information

    This command provides the general status of the PIV application and content in the different slots such as the certificates installed.

  4. Use the credential helper command with the security module. The command will require at least:
    • The ARN of the trust anchor
    • The ARN of the target role to assume
    • The ARN of the profile to pull policies from
    • The certificate and/or key identifiers in the form of a PKCS #11 URI

You can use the certificate flag to search which slot on the security module contains the private key associated with the user certificate.

To specify an object stored in a cryptographic module, you should use the PKCS #11 URI that is defined in RFC7512. The attributes in the identifier string are a set of search criteria used to filter a set of objects. See a recommended method of locating objects in PKCS #11.

In the following example, we search for an object of type certificate, with the object label as “Certificate for Digital Signature”, in slot 1. The pin-value attribute allows you to directly use the pin to log into the cryptographic device.

pkcs11:type=cert;object=Certificate%20for%20Digital%20Signature;id=%01?pin-value=123456

From the folder where you have installed the credential helper tool, use the following command. Because we only have one certificate on the device, we can limit the filter to the certificate type in our PKCS #11 URI.

./aws_signing_helper credential-process
--profile-arn arn:aws:rolesanywhere:<region>:<accountID>:profile/<profileID>
--role-arn arn:aws:iam::<accountID>:role/<assumedRole> 
--trust-anchor-arn arn:aws:rolesanywhere:<region>:<accountID>:trust-anchor/<trustanchorID>
--certificate pkcs11:type=cert?pin-value=<PIN>

If everything is configured correctly, the credential helper tool will return a JSON that contains the credentials, as follows. The PIN code will be requested if you haven’t specified it in the command.

Please enter your user PIN:
  			{
                    "Version":1,
                    "AccessKeyId": <String>,
                    "SecretAccessKey": <String>,
                    "SessionToken": <String>,
                    "Expiration": <Timestamp>
                 }

To use temporary security credentials with AWS SDKs and the AWS CLI, you can configure the credential helper tool as a credential process. For more information, see Source credentials with an external process. The following example shows a config file (usually in ~/.aws/config) that sets the helper tool as the credential process.

[profile server1-demo]
credential_process = ./aws_signing_helper credential-process --profile-arn <arn-for-iam-roles-anywhere-profile> --role-arn <arn-for-iam-role-to-assume> --trust-anchor-arn <arn-for-roles-anywhere-trust-anchor> --certificate pkcs11:type=cert?pin-value=<PIN> 

You can provide the PIN as part of the credential command with the option pin-value=<PIN> so that the user input is not required.

If you prefer not to store your PIN in the config file, you can remove the attribute pin-value. In that case, you will be prompted to enter the PIN for every CLI command.

You can use the serve and update commands of the credential helper mentioned in the solution overview to manage credential rotation for unattended workloads. After the successful use of the PIN, the credential helper will store it in memory for the duration of the process and not ask for it anymore.

Auditability and fine-grained access

You can audit the activity of servers that are assuming roles through IAM Roles Anywhere. IAM Roles Anywhere is integrated with AWS CloudTrail, a service that provides a record of actions taken by a user, role, or an AWS service in IAM Roles Anywhere.

To view IAM Roles Anywhere activity in CloudTrail

  1. In the AWS CloudTrail console, in the left navigation menu, choose Event history.
  2. For Lookup attributes, filter by Event source and enter rolesanywhere.amazonaws.com in the textbox. You will find all the API calls that relate to IAM Roles Anywhere, including the CreateSession API call that returns temporary security credentials for workloads that have been authenticated with IAM Roles Anywhere to access AWS resources.
    Figure 4: CloudTrail Events filtered on the “IAM Roles Anywhere” event source

    Figure 4: CloudTrail Events filtered on the “IAM Roles Anywhere” event source

  3. When you review the CreateSession event record details, you can find the assumed role ID in the form of <PrincipalID>:<serverCertificateSerial>, as in the following example:
    Figure 5: Details of the CreateSession event in the CloudTrail console showing which role is being assumed

    Figure 5: Details of the CreateSession event in the CloudTrail console showing which role is being assumed

  4. If you want to identify API calls made by a server, for Lookup attributes, filter by User name, and enter the serverCertificateSerial value from the previous step in the textbox.
    Figure 6: CloudTrail console events filtered by the username associated to our certificate on the security module

    Figure 6: CloudTrail console events filtered by the username associated to our certificate on the security module

    The API calls to AWS services made with the temporary credentials acquired through IAM Roles Anywhere will contain the identity of the server that made the call in the SourceIdentity field. For example, the EC2 DescribeInstances API call provides the following details:

    Figure 7: The event record in the CloudTrail console for the EC2 describe instances call, with details on the assumed role and certificate CN.

    Figure 7: The event record in the CloudTrail console for the EC2 describe instances call, with details on the assumed role and certificate CN.

Additionally, you can include conditions in the identity policy for the IAM role to apply fine-grained access control. This will allow you to apply a fine-grained access control filter to specify which server in the group of servers can perform the action.

To apply access control per server within the same IAM Roles Anywhere profile

  1. In the IAM Roles Anywhere console, select the profile used by the group of servers, then select one of the roles that is being assumed.
  2. Apply the following policy, which will allow only the server with CN=server1-demo to list all buckets by using the condition on aws:SourceIdentity.
    {
      "Version":"2012-10-17",
      "Statement":[
        {
                "Sid": "VisualEditor0",
                "Effect": "Allow",
                "Action": "s3:ListBuckets",
                "Resource": "*",
                "Condition": {
                    "StringEquals": {
                        "aws:SourceIdentity": "CN=server1-demo"
                    }
                }
            }
      ]
    }

Conclusion

In this blog post, I’ve demonstrated how you can use the YubiKey 5 Series (or any PKCS #11 cryptographic module) to securely store the private keys for the X.509 certificates used with IAM Roles Anywhere. I’ve also highlighted how you can use AWS CloudTrail to audit API actions performed by the roles assumed by the servers.

To learn more about IAM Roles Anywhere, see the IAM Roles Anywhere and Credential Helper tool documentation. For configuration with Thales IDPrime smart card, review the credential helper for IAM Roles Anywhere GitHub page.

If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, start a new thread on the AWS Identity and Access Management re:Post or contact AWS Support.

Want more AWS Security news? Follow us on Twitter.

Author

Edouard Kachelmann

Edouard is an Enterprise Senior Solutions Architect at Amazon Web Services. Based in Boston, he is a passionate technology enthusiast who enjoys working with customers and helping them build innovative solutions to deliver measurable business outcomes. Prior to his work at AWS, Edouard worked for the French National Cybersecurity Agency, sharing his security expertise and assisting government departments and operators of vital importance. In his free time, Edouard likes to explore new places to eat, try new French recipes, and play with his kids.

Manage roles and entitlements with PBAC using Amazon Verified Permissions

Post Syndicated from Abhishek Panday original https://aws.amazon.com/blogs/devops/manage-roles-and-entitlements-with-pbac-using-amazon-verified-permissions/

Traditionally, customers have used role-based access control (RBAC) to manage entitlements within their applications. The application controls what users can do, based on the roles they are assigned. But, the drive for least privilege has led to an exponential growth in the number of roles. Customers can address this role explosion by moving authorization logic out of the application code, and implementing a policy-based access control (PBAC) model that augments RBAC with attribute-based access control (ABAC).

In this blog post, we cover roles and entitlements, how they are applicable in apps authorization decisions, how customers implement roles and authorization in their app today, and how to shift to a centralized PBAC model by using Amazon Verified Permissions.

Describing roles and entitlements, approaches and challenges of current implementations

In RBAC models, a user’s entitlements are assigned based on job role. This role could be that of a developer, which might grant permissions to affect code in the pipeline of an app. Entitlements represent the features, functions, and resources a user has permissions to access. For example, a customer might be able to place orders or view pets in a pet store application, or a store owner might be entitled to review orders made from their store.

The combination of roles assigned to a user and entitlements granted to these roles determines what a human user can do within your application. Traditionally, application access has all been handled in code by hard coding roles that users can be assigned and mapping those roles directly to a set of actions on resources. However, as the need to apply more granular access control grows (as with least privilege), so do the number of required hard-coded roles that are assigned to users to obtain this level of granularity. This problem is frequently called role explosion, where role definitions grow exponentially which requires additional overhead from your teams to manage and audit roles effectively. For example, the code to authorize request to get details of an order has multiple if/else statements, as shown in the following sample.


boolean userAuthorizedForOrder (Order order, User user){
    if (user.storeId == user.storeID) {
        if (user.roles.contains("store-owner-roles") {            // store owners can only access orders for their own stores  
            return true; 
        } else if (user.roles.contains("store-employee")) {
            if (isStoreOpen(current_time)) {                      // Only allow access for the order to store-employees when
                return true                                       // store is open 
            }
        }
    } else {
        if (user.roles("customer-service-associate") &amp;&amp;           // Only allow customer service associates to orders for cases 
                user.assignedShift(current_time)) &amp;&amp;              // they are assinged and only during their assigned shift
                user.currentCase.order.orderId == order.orderId
         return true;
    }
    return false; 
}

This problem introduces several challenges. First, figuring out why a permission was granted or denied requires a closer look at the code. Second, adding a permission requires code changes. Third, audits can be difficult because you either have to run a battery of tests or explore code across multiple files to demonstrate access controls to auditors. Though there might be additional considerations, these three challenges have led many app owners to begin looking at PBAC methods to address the granularity problem. You can read more about the foundations of PBAC models in Policy-based access control in application development with Amazon Verified Permissions. By shifting to a PBAC model, you can reduce role growth to meet your fine-grained permissions needs. You can also externalize authorization logic from code, develop granular permissions based on roles and attributes, and reduce the time that you spend refactoring code for changes to authorization decisions or reading through the code to audit authorization logic.

In this blog, we demonstrate implementing permissions in a PBAC model through a demo application. The demo application uses Cognito groups to manage role assignment, Verified Permissions to implement entitlements for the roles. The approach restricts the resources that a role can access using attribute-based conditions. This approach works well in usecases when you already have a system in place to manage role assignment and you can define resources that a user may access by matching attributes of the user with attributes of the resource.

Demo app

Let’s look at a sample pet store app. The app is used by 2 types of users – end users and store owners. The app enables end users to search and order pets. The app allows store owners to list orders for the store. This sample app is available for download and local testing on the aws-samples/avp-petstore-sample Github repository. The app is a web app built by using AWS Amplify, Amazon API-Gateway, Amazon Cognito, and Amazon Verified Permissions. The following diagram is a high-level illustration of the app’s architecture.

Architectural Diagram

Steps

  1. The user logs in to the application, and is re-directed to Amazon Cognito to sign-in and obtain a JWT token.
  2. When user take an action (eg. ListOrders) in the application, the application calls Amazon API-Gateway to process the request.
  3. Amazon API-Gateway forwards the request to a lambda function, that call Amazon Verified Permissions to authorize the action. If the authorization results in deny, the lambda returns Unauthorized back to the application.
  4. If the authorization succeed, the application continues to execute the action.

RBAC policies in action

In this section, we focus on building RBAC permissions for the sample pet store app. We will guide you through building RBAC by using Verified Permissions and by focusing on a role for store owners, who are allowed to view all orders for a store. We use Verified Permissions to manage the permissions granted to this role and Amazon Cognito to manage role assignments.

We model the store owner role in Amazon Cognito as a user group called Store-Owner-Role. When a user is assigned the store owner role, the user is added to the “Store-Owner-Role” user group. You can create the users and users groups required to follow along with the sample application by visiting managing users and groups in Amazon Cognito.

After users are assigned to the store owner role, you can enforce that they can list all orders in the store by using the following RBAC policy. The policy provides access to any user in the Store-Owner-Role to perform the ListOrders and GetStoreInventory actions on any resource.

permit (
         principal in MyApplication::Group::"Store-Owner-Role",
         action in [
              MyApplication::Action::"GetStoreInventory",
              MyApplication::Action::"ListOrders"
         ],
         resource
);

Based on the policy we reviewed – the store owner will receive a Success! when they attempt to list existing orders.

Eve is permitted to list orders

This example further demonstrates the division of responsibility between the identity provider (Amazon Cognito) and Verified Permissions. The identity provider (IdP) is responsible for managing roles and memberships in roles. Verified Permissions is responsible for managing policies that describe what those roles are permitted to do. As demonstrated above, you can use this process to add roles without needing to change code.

Using PBAC to help reduce role explosion

Up until the point of role explosion, RBAC has worked well as the sole authorization model. Unfortunately, we have heard from customers that this model does not scale well because of the challenge of role explosion. Role explosion happens when you have hundreds or thousands of roles, and managing and auditing those roles becomes challenging. In extreme cases, you might have more roles than the number of users in your organization. This happens primarily because organizations keep creating more roles, with each role granting access to a smaller set of resources in an effort to follow the principle of least privilege.

Let’s understand the problem of role explosion through our sample pet store app. The pet store app is now being sold as a SaaS product to pet stores in other locations. As a result, the app needs additional access controls to ensure that each store owner can view only the orders from their own store. The most intuitive way to implement these access controls was to create an additional role for each location, which would restrict the scope of access for a store owner to their respective store’s orders. For example, a role named petstore-austin would allow access only to resources in the Austin, Texas store. RBAC models allow developers to predefine sets of permissions that can be used in an application, and ABAC models allow developers to adapt those permissions to the context of the request (such as the client, the resource, and the method used). The adoption of both RBAC and ABAC models leads to an explosion of either roles or attribute-based rules as the number of store locations increases.

To solve this problem, you can combine RBAC and ABAC policies into a PBAC model. RBAC policies determines the actions the user can take. Augmenting these policies with ABAC policies allows you to control the resouces they can take those actions on. For example, you can scope down the resources a user can access based on identity attributes, such as department or business unit, region, and management level. This approach mitigates role explosion because you need to have only a small number of predefined roles, and access is controlled based on attributes. You can use Verified Permissions to combine RBAC and ABAC models in the form of Cedar policies to build this PBAC solution.

We can demonstrate this solution in the sample pet store app by modifying the policy we created earlier and adding ABAC conditions. The conditions specify that users can only ListOrders of the store they own. The store a store owner owns is represented in Amazon Cognito by employmentStoreCode. This policy now expands on the granularity of access of the original RBAC policy without leading to numerous RBAC policies.

permit (
         principal in MyApplication::Group::"Store-Owner-Role",
         action in [
              MyApplication::Action::"GetStoreInventory",
              MyApplication::Action::"ListOrders"
          ],
          resource
) when { 
          principal.employmentStoreCode == resource.storeId 
};

We demonstrate that our policy restricts access for store owners to the store they own, by creating a user – eve – who is assigned the Store-Owner-Role and owns petstore-london. When Eve lists orders for the petstore-london store, she gets a success response, indicating she has permissions to list orders.
Eve is permitted to list orders for petstore-london

Next, when even tries to list orders for the petstore-seattle store, she gets a Not Authorized response. She is denied access as she does not own petstore-seattle.

Eve is not permitted to list orders for petstore-seattle

Step-by-step walkthrough of trying the Demo App

If you want to go through the demo of our sample pet store app, we recommend forking it from aws-samples/avp-petstore-sample Github repo and going through this process in README.md to ensure hands-on familiarity.

We will first walk through setting up permissions using only RBAC for the sample pet store application. Next, we will see how you can use PBAC to implement least priveledge as the application scales.

Implement RBAC based Permissions

We describe setting up policies to implement entitlements for the store owner role in Verified Permissions.

    1. Navigate to the AWS Management Console, search for Verified Permissions, and select the service to go to the service page.
    2. Create new policy store to create a container for your policies. You can create an Empty Policy Store for the purpose of the walk-through.
    3. Navigate to Policies in the navigation pane and choose Create static policy.
    4. Select Next and paste in the following Cedar policy and select Save.
permit (
        principal in MyApplication::Group::"Store-Owner-Role",
        action in [
               MyApplication::Action::"GetStoreInventory",
               MyApplication::Action::"ListOrders"
         ],
         resource
);
  1. You need to get users and assign the Store-Owner-Role to them. In this case, you will use Amazon Cognito as the IdP and the role can be assigned there. You can create users and groups in Cognito by following the below steps.
    1. Navigate to Amazon Cognito from the AWS Management Console, and select the user group created for the pet store app.
    2. Creating a user by clicking create user and create a user with user name eve
    3. Navigate to the Groups section and create a group called Store-Owner-Role .
    4. Add eve to the Store-Owner-Role group by clicking Add user to Group, selecting eve and clicking the Add.
  2. Now that you have assigned the Store-Owner-Role to the user, and Verified Permissions has a permit policy granting entitlements based on role membership, you can log in to the application as the user – eve – to test functionality. When choosing List All Orders, you can see the approval result in the app’s output.

Implement PBAC based Permissions

As the company grows, you want to be able to limit GetOrders access to a specific store location so that you can follow least privilege. You can update your policy to PBAC by adding an ABAC condition to the existing permit policy. You can add a condition in the policy that restricts listing orders to only those stores the user owns.

Below is the walk-though of updating the application

    1. Navigate to the Verified Permissions console and update the policy to the below.
permit (
         principal in MyApplication::Group::"Store-Owner-Role",
         action in [
              MyApplication::Action::"GetStoreInventory",
              MyApplication::Action::"ListOrders"
          ],
          resource
) when { 
          principal.employmentStoreCode == resource.storeId 
};
  1. Navigate to the Amazon Cognito console, select the user eve and click “Edit” in the user attributes section to update the “custom:employmentStoreCode”. Set the attribute value to “petstore-london” as eve owns the petstore-london location
  2. You can demonstrate that eve can only list orders of “petstore-london” by following the below steps
    1. We want to make sure that latest changes to the user attributed are passed to the application in the identity token. We will refresh the identity token, by logging out of the app and logging in again as Eve. Navigate back to the application and logout as eve.
    2. In the application, we set the Pet Store Identifier as petstore-london and click the List All Orders. The result is success!, as Eve is authorized to list orders of the store she owns.
    3. Next, we change the Pet Store Identifier to petstore-seattle and and click the List All Orders. The result is Not Authorized, as Eve is authorized to list orders of stores she does not owns.

Clean Up section

You can cleanup the resources that were created in this blog by following these steps.

Conclusion

In this post, we reviewed what roles and entitlements are as well as how they are used to manage user authorization in your app. We’ve also covered RBAC and ABAC policy examples with respect to the demo application, avp-petstore-sample, that is available to you via AWS Samples for hands-on testing. The walk-through also covered our example architecture using Amazon Cognito as the IdP and Verified Permissions as the centralized policy store that assessed authorization results based on the policies set for the app. By leveraging Verified Permissions, we could use PBAC model to define fine-grained access while preventing role explosion. For more information about Verified Permissions, see the Amazon Verified Permissions product details page and Resources page.

Abhishek Panday

Abhishek is a product manager in the Amazon Verified Permissions team. He has been working with the AWS for more than two years, and has been at Amazon for more than five years. Abhishek enjoys working with customers to understand the customer’s challenges and building products to solve those challenges. Abhishek currently lives in Seattle and enjoys playing soccer, hiking, and cooking Indian cuisines.

Jeremy Ware

Jeremy is a Security Specialist Solutions Architect focused on Identity and Access Management. Jeremy and his team enable AWS customers to implement sophisticated, scalable, and secure IAM architecture and Authentication workflows to solve business challenges. With a background in Security Engineering, Jeremy has spent many years working to raise the Security Maturity gap at numerous global enterprises. Outside of work, Jeremy loves to explore the mountainous outdoors participate in sports such as Snowboarding, Wakeboarding, and Dirt bike riding.

Making Content Security Policies (CSPs) easy with Page Shield

Post Syndicated from Michael Tremante original http://blog.cloudflare.com/making-content-security-policies-csps-easy-with-page-shield/

Making Content Security Policies (CSPs) easy with Page Shield

Making Content Security Policies (CSPs) easy with Page Shield

Modern web applications are complex, often loading JavaScript libraries from tens of different sources and submitting data to just as many. This leads to a vast attack surface area and many attack types that hackers may leverage to target the user browser directly. Magecart, a category of supply chain attack, is a good example.

To combat this, browser vendors (Google, Microsoft, Mozilla, etc.) have agreed on a standard that allows application owners to control browser behavior from a security perspective. This standard is called Content Security Policies (CSPs). Content Security Policies are implemented by application owners as a specially formatted HTTP response header that the browser then parses and enforces. This header can be used, for example, to enforce loading of JavaScript libraries only from a specific set of URLs. CSPs are good as they reduce the attack surface, but are hard to implement and manage, especially in a fast-paced development environment.

Starting today, Page Shield, our client-side security product, supports all major CSP directives. We’ve also added better reporting, automated suggestions, and Page Shield specific user roles, making CSPs much easier to manage.

If you are a Page Shield enterprise customer, log in to your dashboard to make use of the new features immediately.

Page Shield policies

Let’s say you just built a web application. To keep it simple, you used a number of services to implement specific features: Stripe for your checkout and Zendesk for your chat system.

These two systems require you to “embed” JavaScript files in your application. Once done, these widgets will also submit data back to their respective endpoints — for example, the Zendesk servers if someone interacts with the Zendesk chat widget.

You also load a JavaScript file that you built for some simple interactions in your web application. This file is hosted directly on your server under your site’s own domain, let’s say example.com.

You know that no other files should be loaded, and with a security first mindset, you wish to enforce that only these files (and no other files!) can get executed by your users directly in the browser environment. This avoids a potential compromise to be effective as browsers will refuse to execute unwanted code.

You can achieve this by using Page Shield policies, an abstraction on top of Content Security Policies (CSPs) with the goal of making CSPs easy. This system allows you to adopt a positive security model by letting you define what is allowed, and block everything else by default.

To do this we need to follow a few simple steps. First, log in to Cloudflare and head over to the relevant zone → Security → Page Shield → Policies → Create policy. We are presented with the following page:

Making Content Security Policies (CSPs) easy with Page Shield

Insert a policy name (e.g., my website policy) and select, if needed, using our standard wirefilter syntax, where you want the policy to be applied. For example, if we only wanted the policy to be applied on our checkout pages, where there is a higher risk of data being leaked, we can select:

If incoming requests match… URI Path contains “checkout”

In the UI this would be represented like this:

Making Content Security Policies (CSPs) easy with Page Shield

This filtering allows you to focus on the portions of your site that matter most, or, at the same time, test your policies on specific subsets of your traffic.

Next, we need to define where scripts are allowed to be loaded from and where they are allowed to send data to. There are two directives we can use for this: the script-src (Scripts) directive and the connect-src (Connections) directive.

For Stripe, at time of writing, scripts will be loaded from the following URL:

https://checkout.stripe.com

This same URL is also used to submit data back to Stripes’ system. Zendesk is similar, but for simplicity we will focus on Stripe only in this example. You also load a JavaScript file from your own site, example.com mentioned earlier.

From the Add a new directive dropdown, select Scripts:

Making Content Security Policies (CSPs) easy with Page Shield

Once added, the script directive will show:

Making Content Security Policies (CSPs) easy with Page Shield

This is where the magic begins. If Page Shield has been enabled on your site, you may notice it may have already detected the JavaScript files your site is loading, and will suggest them to you as a simple list of checkboxes. No more chasing developer team members to understand what is being loaded by your site.

Building the directive becomes a simple checklist exercise. The builder does allow you to decide if you wish to allow scripts from entire domains, or drill down to specific URLs only. In a normal circumstance, you should expect to allow all detected scripts. For Stripe, the directive configuration would look like the following:

Making Content Security Policies (CSPs) easy with Page Shield

A preview of the directive is shown below the builder.

One more example: remember that you also load a script from your own site? That is being identified under the example.com entry in the list. However, loading scripts from the same source is very common and to strike a good balance between simplicity and security, CSPs allow a shortcut keyword: self, available at the top of the builder. Our final policy will look like this:

Making Content Security Policies (CSPs) easy with Page Shield

And those are the basics. Simply repeat the steps for the connect-src (Connections) directive (where data should be sent to) and deploy the policy either in LOG (for testing) or ALLOW (enforcing). Your users will be a lot safer as a result.

Better policy suggestions

The suggestions engine shown above is now a lot better, making it easier to build Page Shield policies. We’ve added full support for the connect-src (Connections) directive in addition to script-src (Scripts) directive, and we now customize the suggestions based on where you wish to deploy the policy.

So for example, if you select to deploy the policy on your checkout pages only, shown as:

If incoming requests match… URI Path contains “checkout”

In the example above, the list of suggestions will automatically update to show you suggestions for scripts or connections seen on those pages, only allowing you to minimize the size of the policy. This is important as CSPs often tend to grow very large, causing performance implications.

Additionally, the builder will try to optimize the policy further for you by allowing you to easily select the correct level of precision in your ALLOW list. For example, if you are loading hundreds of scripts from a specific destination, it will propose you to allow the hostname rather than all script URLs.

All major CSP directives are now supported

Before today, we only supported the script-src (Scripts) directive, allowing you to define where scripts are allowed to be loaded from. Starting today, we support all major directives. Note however, that we only support suggestions for script-src and connect-src. Suggestions for the other directives are on the roadmap.

The full list of supported directives with relevant keywords is shown in the table below:

Directive Smart suggestions Description
script-src Define where JavaScript files are allowed to be loaded from.
connect-src Define where data can be sent to.
default-src Default behavior to apply.
img-src Define where images are allowed to be loaded from.
style-src Define where style sheets (CSS) are allowed to be loaded from.
font-src Define where font files are allowed to be loaded from.
object-src Define where objects (HTML) are allowed to be loaded from.
media-src Define where media files are allowed to be loaded from (e.g. mp4)
child-src Define where web workers and nested browser contexts are allowed to be loaded from.
form-action Define where forms should be allowed to post data to.
worker-src Define where workers are allowed to be loaded from.
base-uri Define what URLs can be used in a document base element.
manifest-src Define which manifests can be applied.
frame-src Define what URLs can be embedded in HTML iframes.
frame-ancestors Define which parent sources can embed the given page in an HTML iframe (opposite of frame-src).

Additionally, we also support the upgrade-insecure-requests directive. This is a special keyword that will force the browser to automatically convert all HTTP URLs to HTTPs. This feature is similar to our “Always Use HTTPS”, but forces the browser to upgrade the requests rather than using our proxy to perform the similar behavior. Both features can work in conjunction.

The official Mozilla CSP documentation is a great resource for additional details on each CSP directive.

Import your existing CSP policies today

A lot of customers adopting Page Shield have asked if they could import their existing CSP policies into the product. We’ve now taken a first step to make this experience possible by allowing you to paste an existing CSP directly in the policy interface:

Making Content Security Policies (CSPs) easy with Page Shield

Once pasted and imported, the system will automatically parse all found directives into the builder allowing you to subsequently edit them as required. If your policy contains deprecated directives, or directives not currently supported, an appropriate error message will be displayed allowing you to edit before trying again.

Improved violation reporting

Once you have deployed a Page Shield policy, it is important to identify its behavior: is it implemented correctly? Did you miss something?

Coincidentally, this is another aspect that is hard to manage. CSPs allows you to define an endpoint where browsers will submit violation reports (errors). However, the volume of errors can be substantial especially for large applications, requiring a whole new set of logging pipelines and infrastructure to be implemented.

Page Shield does all this for you out of the box.

With the new support for all major directives, we now also improved violation reporting to show you which directive is causing any potential issues (the first column in the screenshot below). This is provided in the policy overview screen:

Making Content Security Policies (CSPs) easy with Page Shield

Domain insights

In this release, we also took the opportunity to improve our resource details page by adding domain insights. Domain name WHOIS info is often a very good indicator of the potential maliciousness of a JavaScript resource or connection endpoint. For example, data being sent to a newly registered domain should cause some concern. We’ve also exposed any categorisations we have available for any given domain, allowing you to more quickly review the data without having to navigate to our Security Center or Cloudflare Radar.

Making Content Security Policies (CSPs) easy with Page Shield

Page Shield user permissions

One final thing. If you only need specific team members to look at or deploy policies with Cloudflare Page Shield, this is now possible. Two new user roles have been implemented in the dashboard: Page Shield (write/read) and Page Shield (read). You can find these roles available when inviting new users (or editing existing users) to the Cloudflare dashboard.

Start using Page Shield today

Most of the features discussed in this post are only available to Page Shield enterprise add-on customers, and you can find additional details in our developer documentation. However, Page Shield is available to all users on the Pro plan and above with a limited set of functionality. Turning on Page Shield is as simple as a single click.

Head over now to the dashboard and turn it on, and let us know what you think.

Stay tuned for our next Page Shield post where we will discuss how PCI DSS 4.0 client side requirements are easy to satisfy with Page Shield.

AWS achieves HDS certification in two additional Regions

Post Syndicated from Janice Leung original https://aws.amazon.com/blogs/security/aws-achieves-hds-certification-in-two-additional-regions-2/

Amazon Web Services (AWS) is pleased to announce that two additional AWS Regions—Middle East (UAE) and Europe (Zurich)—have been granted the Health Data Hosting (Hébergeur de Données de Santé, HDS) certification, increasing the scope to 20 global AWS Regions.

The Agence Française de la Santé Numérique (ASIP Santé), the French governmental agency for health, introduced the HDS certification to strengthen the security and protection of personal health data. By achieving this certification, AWS demonstrates our commitment to adhere to the heightened expectations for cloud service providers.

The following 20 Regions are in scope for this certification:

  • US East (Ohio)
  • US East (Northern Virginia)
  • US West (Northern California)
  • US West (Oregon)
  • Asia Pacific (Jakarta)
  • Asia Pacific (Seoul)
  • Asia Pacific (Mumbai)
  • Asia Pacific (Singapore)
  • Asia Pacific (Sydney)
  • Asia Pacific (Tokyo)
  • Canada (Central)
  • Europe (Frankfurt)
  • Europe (Ireland)
  • Europe (London)
  • Europe (Milan)
  • Europe (Paris)
  • Europe (Stockholm)
  • Europe (Zurich)
  • Middle East (UAE)
  • South America (São Paulo)

The HDS certification demonstrates that AWS provides a framework for technical and governance measures that secure and protect personal health data, governed by French law. Our customers who handle personal health data can continue to manage their workloads in HDS-certified Regions with confidence.

Independent third-party auditors evaluated and certified AWS on September 8, 2023. The Certificate of Compliance demonstrating AWS compliance status is available on the Agence du Numérique en Santé (ANS) website and AWS Artifact. AWS Artifact is a self-service portal for on-demand access to AWS compliance reports. Sign in to AWS Artifact in the AWS Management Console, or learn more at Getting Started with AWS Artifact.

For up-to-date information, including when additional Regions are added, see the AWS Compliance Programs page and choose HDS.

AWS strives to continuously meet your architectural and regulatory needs. If you have questions or feedback about HDS compliance, reach out to your AWS account team.

To learn more about our compliance and security programs, see AWS Compliance Programs. As always, we value your feedback and questions; reach out to the AWS Compliance team through the Contact Us page.

If you have feedback about this post, submit comments in the Comments section below.

Want more AWS Security news? Follow us on Twitter.

Author

Janice Leung

Janice is a Security Assurance Audit Program Manager at AWS, based in New York. She leads security audits across Europe and previously worked in security assurance and technology risk management in the financial industry for 11 years.

Automatically detect and block low-volume network floods

Post Syndicated from Bryan Van Hook original https://aws.amazon.com/blogs/security/automatically-detect-and-block-low-volume-network-floods/

In this blog post, I show you how to deploy a solution that uses AWS Lambda to automatically manage the lifecycle of Amazon VPC Network Access Control List (ACL) rules to mitigate network floods detected using Amazon CloudWatch Logs Insights and Amazon Timestream.

Application teams should consider the impact unexpected traffic floods can have on an application’s availability. Internet-facing applications can be susceptible to traffic that some distributed denial of service (DDoS) mitigation systems can’t detect. For example, hit-and-run events are a popular approach that use short-lived floods that reoccur at random intervals. Each burst is small enough to go unnoticed by mitigation systems, but still occur often enough and are large enough to be disruptive. Automatically detecting and blocking temporary sources of invalid traffic, combined with other best practices, can strengthen the resiliency of your applications and maintain customer trust.

Use resilient architectures

AWS customers can use prescriptive guidance to improve DDoS resiliency by reviewing the AWS Best Practices for DDoS Resiliency. It describes a DDoS-resilient reference architecture as a guide to help you protect your application’s availability.

The best practices above address the needs of most AWS customers; however, in this blog we cover a few outlier examples that fall outside normal guidance. Here are a few examples that might describe your situation:

  • You need to operate functionality that isn’t yet fully supported by an AWS managed service that takes on the responsibility of DDoS mitigation.
  • Migrating to an AWS managed service such as Amazon Route 53 isn’t immediately possible and you need an interim solution that mitigates risks.
  • Network ingress must be allowed from a wide public IP space that can’t be restricted.
  • You’re using public IP addresses assigned from the Amazon pool of public IPv4 addresses (which can’t be protected by AWS Shield) rather than Elastic IP addresses.
  • The application’s technology stack has limited or no support for horizontal scaling to absorb traffic floods.
  • Your HTTP workload sits behind a Network Load Balancer and can’t be protected by AWS WAF.
  • Network floods are disruptive but not significant enough (too infrequent or too low volume) to be detected by your managed DDoS mitigation systems.

For these situations, VPC network ACLs can be used to deny invalid traffic. Normally, the limit on rules per network ACL makes them unsuitable for handling truly distributed network floods. However, they can be effective at mitigating network floods that aren’t distributed enough or large enough to be detected by DDoS mitigation systems.

Given the dynamic nature of network traffic and the limited size of network ACLs, it helps to automate the lifecycle of network ACL rules. In the following sections, I show you a solution that uses network ACL rules to automatically detect and block infrastructure layer traffic within 2–5 minutes and automatically removes the rules when they’re no longer needed.

Detecting anomalies in network traffic

You need a way to block disruptive traffic while not impacting legitimate traffic. Anomaly detection can isolate the right traffic to block. Every workload is unique, so you need a way to automatically detect anomalies in the workload’s traffic pattern. You can determine what is normal (a baseline) and then detect statistical anomalies that deviate from the baseline. This baseline can change over time, so it needs to be calculated based on a rolling window of recent activity.

Z-scores are a common way to detect anomalies in time-series data. The process for creating a Z-score is to first calculate the average and standard deviation (a measure of how much the values are spread out) across all values over a span of time. Then for each value in the time window calculate the Z-score as follows:

Z-score = (value – average) / standard deviation

A Z-score exceeding 3.0 indicates the value is an outlier that is greater than 99.7 percent of all other values.

To calculate the Z-score for detecting network anomalies, you need to establish a time series for network traffic. This solution uses VPC flow logs to capture information about the IP traffic in your VPC. Each VPC flow log record provides a packet count that’s aggregated over a time interval. Each flow log record aggregates the number of packets over an interval of 60 seconds or less. There isn’t a consistent time boundary for each log record. This means raw flow log records aren’t a predictable way to build a time series. To address this, the solution processes flow logs into packet bins for time series values. A packet bin is the number of packets sent by a unique source IP address within a specific time window. A source IP address is considered an anomaly if any of its packet bins over the past hour exceed the Z-score threshold (default is 3.0).

When overall traffic levels are low, there might be source IP addresses with a high Z-score that aren’t a risk. To mitigate against false positives, source IP addresses are only considered to be an anomaly if the packet bin exceeds a minimum threshold (default is 12,000 packets).

Let’s review the overall solution architecture.

Solution overview

This solution, shown in Figure 1, uses VPC flow logs to capture information about the traffic reaching the network interfaces in your public subnets. CloudWatch Logs Insights queries are used to summarize the most recent IP traffic into packet bins that are stored in Timestream. The time series table is queried to identify source IP addresses responsible for traffic that meets the anomaly threshold. Anomalous source IP addresses are published to an Amazon Simple Notification Service (Amazon SNS) topic. A Lambda function receives the SNS message and decides how to update the network ACL.

Figure 1: Automating the detection and mitigation of traffic floods using network ACLs

Figure 1: Automating the detection and mitigation of traffic floods using network ACLs

How it works

The numbered steps that follow correspond to the numbers in Figure 1.

  1. Capture VPC flow logs. Your VPC is configured to stream flow logs to CloudWatch Logs. To minimize cost, the flow logs are limited to particular subnets and only include log fields required by the CloudWatch query. When protecting an endpoint that spans multiple subnets (such as a Network Load Balancer using multiple availability zones), each subnet shares the same network ACL and is configured with a flow log that shares the same CloudWatch log group.
  2. Scheduled flow log analysis. Amazon EventBridge starts an AWS Step Functions state machine on a time interval (60 seconds by default). The state machine starts a Lambda function immediately, and then again after 30 seconds. The Lambda function performs steps 3–6.
  3. Summarize recent network traffic. The Lambda function runs a CloudWatch Logs Insights query. The query scans the most recent flow logs (5-minute window) to summarize packet frequency grouped by source IP. These groupings are called packet bins, where each bin represents the number of packets sent by a source IP within a given minute of time.
  4. Update time series database. A time series database in Timestream is updated with the most recent packet bins.
  5. Use statistical analysis to detect abusive source IPs. A Timestream query is used to perform several calculations. The query calculates the average bin size over the past hour, along with the standard deviation. These two values are then used to calculate the maximum Z-score for all source IPs over the past hour. This means an abusive IP will remain flagged for one hour even if it stopped sending traffic. Z-scores are sorted so that the most abusive source IPs are prioritized. If a source IP meets these two criteria, it is considered abusive.
    1. Maximum Z-score exceeds a threshold (defaults to 3.0).
    2. Packet bin exceeds a threshold (defaults to 12,000). This avoids flagging source IPs during periods of overall low traffic when there is no need to block traffic.
  6. Publish anomalous source IPs. Publish a message to an Amazon SNS topic with a list of anomalous source IPs. The function also publishes CloudWatch metrics to help you track the number of unique and abusive source IPs over time. At this point, the flow log summarizer function has finished its job until the next time it’s invoked from EventBridge.
  7. Receive anomalous source IPs. The network ACL updater function is subscribed to the SNS topic. It receives the list of anomalous source IPs.
  8. Update the network ACL. The network ACL updater function uses two network ACLs called blue and green. This verifies that the active rules remain in place while updating the rules in the inactive network ACL. When the inactive network ACL rules are updated, the function swaps network ACLs on each subnet. By default, each network ACL has a limit of 20 rules. If the number of anomalous source IPs exceeds the network ACL limit, the source IPs with the highest Z-score are prioritized. CloudWatch metrics are provided to help you track the number of source IPs blocked, and how many source IPs couldn’t be blocked due to network ACL limits.

Prerequisites

This solution assumes you have one or more public subnets used to operate an internet-facing endpoint.

Deploy the solution

Follow these steps to deploy and validate the solution.

  1. Download the latest release from GitHub.
  2. Upload the AWS CloudFormation templates and Python code to an S3 bucket.
  3. Gather the information needed for the CloudFormation template parameters.
  4. Create the CloudFormation stack.
  5. Monitor traffic mitigation activity using the CloudWatch dashboard.

Let’s review the steps I followed in my environment.

Step 1. Download the latest release

I create a new directory on my computer named auto-nacl-deploy. I review the releases on GitHub and choose the latest version. I download auto-nacl.zip into the auto-nacl-deploy directory. Now it’s time to stage this code in Amazon Simple Storage Service (Amazon S3).

Figure 2: Save auto-nacl.zip to the auto-nacl-deploy directory

Figure 2: Save auto-nacl.zip to the auto-nacl-deploy directory

Step 2. Upload the CloudFormation templates and Python code to an S3 bucket

I extract the auto-nacl.zip file into my auto-nacl-deploy directory.

Figure 3: Expand auto-nacl.zip into the auto-nacl-deploy directory

Figure 3: Expand auto-nacl.zip into the auto-nacl-deploy directory

The template.yaml file is used to create a CloudFormation stack with four nested stacks. You copy all files to an S3 bucket prior to creating the stacks.

To stage these files in Amazon S3, use an existing bucket or create a new one. For this example, I used an existing S3 bucket named auto-nacl-us-east-1. Using the Amazon S3 console, I created a folder named artifacts and then uploaded the extracted files to it. My bucket now looks like Figure 4.

Figure 4: Upload the extracted files to Amazon S3

Figure 4: Upload the extracted files to Amazon S3

Step 3. Gather information needed for the CloudFormation template parameters

There are six parameters required by the CloudFormation template.

Template parameter Parameter description
VpcId The ID of the VPC that runs your application.
SubnetIds A comma-delimited list of public subnet IDs used by your endpoint.
ListenerPort The IP port number for your endpoint’s listener.
ListenerProtocol The Internet Protocol (TCP or UDP) used by your endpoint.
SourceCodeS3Bucket The S3 bucket that contains the files you uploaded in Step 2. This bucket must be in the same AWS Region as the CloudFormation stack.
SourceCodeS3Prefix The S3 prefix (folder) of the files you uploaded in Step 2.

For the VpcId parameter, I use the VPC console to find the VPC ID for my application.

Figure 5: Find the VPC ID

Figure 5: Find the VPC ID

For the SubnetIds parameter, I use the VPC console to find the subnet IDs for my application. My VPC has public and private subnets. For this solution, you only need the public subnets.

Figure 6: Find the subnet IDs

Figure 6: Find the subnet IDs

My application uses a Network Load Balancer that listens on port 80 to handle TCP traffic. I use 80 for ListenerPort and TCP for ListenerProtocol.

The next two parameters are based on the Amazon S3 location I used earlier. I use auto-nacl-us-east-1 for SourceCodeS3Bucket and artifacts for SourceCodeS3Prefix.

Step 4. Create the CloudFormation stack

I use the CloudFormation console to create a stack. The Amazon S3 URL format is https://<bucket>.s3.<region>.amazonaws.com/<prefix>/template.yaml. I enter the Amazon S3 URL for my environment, then choose Next.

Figure 7: Specify the CloudFormation template

Figure 7: Specify the CloudFormation template

I enter a name for my stack (for example, auto-nacl-1) along with the parameter values I gathered in Step 3. I leave all optional parameters as they are, then choose Next.

Figure 8: Provide the required parameters

Figure 8: Provide the required parameters

I review the stack options, then scroll to the bottom and choose Next.

Figure 9: Review the default stack options

Figure 9: Review the default stack options

I scroll down to the Capabilities section and acknowledge the capabilities required by CloudFormation, then choose Submit.

Figure 10: Acknowledge the capabilities required by CloudFormation

Figure 10: Acknowledge the capabilities required by CloudFormation

I wait for the stack to reach CREATE_COMPLETE status. It takes 10–15 minutes to create all of the nested stacks.

Figure 11: Wait for the stacks to complete

Figure 11: Wait for the stacks to complete

Step 5. Monitor traffic mitigation activity using the CloudWatch dashboard

After the CloudFormation stacks are complete, I navigate to the CloudWatch console to open the dashboard. In my environment, the dashboard is named auto-nacl-1-MitigationDashboard-YS697LIEHKGJ.

Figure 12: Find the CloudWatch dashboard

Figure 12: Find the CloudWatch dashboard

Initially, the dashboard, shown in Figure 13, has little information to display. After an hour, I can see the following metrics from my sample environment:

  • The Network Traffic graph shows how many packets are allowed and rejected by network ACL rules. No anomalies have been detected yet, so this only shows allowed traffic.
  • The All Source IPs graph shows how many total unique source IP addresses are sending traffic.
  • The Anomalous Source Networks graph shows how many anomalous source networks are being blocked by network ACL rules (or not blocked due to network ACL rule limit). This graph is blank unless anomalies have been detected in the last hour.
  • The Anomalous Source IPs graph shows how many anomalous source IP addresses are being blocked (or not blocked) by network ACL rules. This graph is blank unless anomalies have been detected in the last hour.
  • The Packet Statistics graph can help you determine if the sensitivity should be adjusted. This graph shows the average packets-per-minute and the associated standard deviation over the past hour. It also shows the anomaly threshold, which represents the minimum number of packets-per-minute for a source IP address to be considered an anomaly. The anomaly threshold is calculated based on the CloudFormation parameter MinZScore.

    anomaly threshold = (MinZScore * standard deviation) + average

    Increasing the MinZScore parameter raises the threshold and reduces sensitivity. You can also adjust the CloudFormation parameter MinPacketsPerBin to mitigate against blocking traffic during periods of low volume, even if a source IP address exceeds the minimum Z-score.

  • The Blocked IPs grid shows which source IP addresses are being blocked during each hour, along with the corresponding packet bin size and Z-score. This grid is blank unless anomalies have been detected in the last hour.
     
Figure 13: Observe the dashboard after one hour

Figure 13: Observe the dashboard after one hour

Let’s review a scenario to see what happens when my endpoint sees two waves of anomalous traffic.

By default, my network ACL allows a maximum of 20 inbound rules. The two default rules count toward this limit, so I only have room for 18 more inbound rules. My application sees a spike of network traffic from 20 unique source IP addresses. When the traffic spike begins, the anomaly is detected in less than five minutes. Network ACL rules are created to block the top 18 source IP addresses (sorted by Z-score). Traffic is blocked for about 5 minutes until the flood subsides. The rules remain in place for 1 hour by default. When the same 20 source IP addresses send another traffic flood a few minutes later, most traffic is immediately blocked. Some traffic is still allowed from two source IP addresses that can’t be blocked due to the limit of 18 rules.

Figure 14: Observe traffic blocked from anomalous source IP addresses

Figure 14: Observe traffic blocked from anomalous source IP addresses

Customize the solution

You can customize the behavior of this solution to fit your use case.

  • Block many IP addresses per network ACL rule. To enable blocking more source IP addresses than your network ACL rule limit, change the CloudFormation parameter NaclRuleNetworkMask (default is 32). This sets the network mask used in network ACL rules and lets you block IP address ranges instead of individual IP addresses. By default, the IP address 192.0.2.1 is blocked by a network ACL rule for 192.0.2.1/32. Setting this parameter to 24 results in a network ACL rule that blocks 192.0.2.0/24. As a reminder, address ranges that are too wide might result in blocking legitimate traffic.
  • Only block source IPs that exceed a packet volume threshold. Use the CloudFormation parameter MinPacketsPerBin (default is 12,000) to set the minimum packets per minute. This mitigates against blocking source IPs (even if their Z-score is high) during periods of overall low traffic when there is no need to block traffic.
  • Adjust the sensitivity of anomaly detection. Use the CloudFormation parameter MinZScore to set the minimum Z-score for a source IP to be considered an anomaly. The default is 3.0, which only blocks source IPs with packet volume that exceeds 99.7 percent of all other source IPs.
  • Exclude trusted source IPs from anomaly detection. Specify an allow list object in Amazon S3 that contains a list of IP addresses or CIDRs that you want to exclude from network ACL rules. The network ACL updater function reads the allow list every time it handles an SNS message.

Limitations

As covered in the preceding sections, this solution has a few limitations to be aware of:

  • CloudWatch Logs queries can only return up to 10,000 records. This means the traffic baseline can only be calculated based on the observation of 10,000 unique source IP addresses per minute.
  • The traffic baseline is based on a rolling 1-hour window. You might need to increase this if a 1-hour window results in a baseline that allows false positives. For example, you might need a longer baseline window if your service normally handles abrupt spikes that occur hourly or daily.
  • By default, a network ACL can only hold 20 inbound rules. This includes the default allow and deny rules, so there’s room for 18 deny rules. You can increase this limit from 20 to 40 with a support case; however, it means that a maximum of 18 (or 38) source IP addresses can be blocked at one time.
  • The speed of anomaly detection is dependent on how quickly VPC flow logs are delivered to CloudWatch. This usually takes 2–4 minutes but can take over 6 minutes.

Cost considerations

CloudWatch Logs Insights queries are the main element of cost for this solution. See CloudWatch pricing for more information. The cost is about 7.70 USD per GB of flow logs generated per month.

To optimize the cost of CloudWatch queries, the VPC flow log record format only includes the fields required for anomaly detection. The CloudWatch log group is configured with a retention of 1 day. You can tune your cost by adjusting the anomaly detector function to run less frequently (the default is twice per minute). The tradeoff is that the network ACL rules won’t be updated as frequently. This can lead to the solution taking longer to mitigate a traffic flood.

Conclusion

Maintaining high availability and responsiveness is important to keeping the trust of your customers. The solution described above can help you automatically mitigate a variety of network floods that can impact the availability of your application even if you’ve followed all the applicable best practices for DDoS resiliency. There are limitations to this solution, but it can quickly detect and mitigate disruptive sources of traffic in a cost-effective manner. Your feedback is important. You can share comments below and report issues on GitHub.

 
If you have feedback about this post, submit comments in the Comments section below.

Want more AWS Security news? Follow us on Twitter.

Bryan Van Hook

Bryan Van Hook

Bryan is a Senior Security Solutions Architect at AWS. He has over 25 years of experience in software engineering, cloud operations, and internet security. He spends most of his time helping customers gain the most value from native AWS security services. Outside of his day job, Bryan can be found playing tabletop games and acoustic guitar.

Reduce the security and compliance risks of messaging apps with AWS Wickr

Post Syndicated from Anne Grahn original https://aws.amazon.com/blogs/security/reduce-the-security-and-compliance-risks-of-messaging-apps-with-aws-wickr/

Effective collaboration is central to business success, and employees today depend heavily on messaging tools. An estimated 3.09 billion mobile phone users access messaging applications (apps) to communicate, and this figure is projected to grow to 3.51 billion users in 2025.

This post highlights the risks associated with messaging apps and describes how you can use enterprise solutions — such as AWS Wickr — that combine end-to-end encryption with data retention to drive positive security and business outcomes.

The business risks of messaging apps

Evolving threats, flexible work models, and a growing patchwork of data protection and privacy regulations have made maintaining secure and compliant enterprise messaging a challenge.

The use of third-party apps for business-related messages on both corporate and personal devices can make it more difficult to verify that data is being adequately protected and retained. This can lead to business risk, particularly in industries with unique record-keeping requirements. Organizations in the financial services industry, for example, are subject to rules that include Securities and Exchange Commission (SEC) Rule 17a-4 and Financial Industry Regulatory Authority (FINRA) Rule 3120, which require them to preserve all pertinent electronic communications.

A recent Gartner report on the viability of mobile bring-your-own-device (BYOD) programs noted, “It is now logical to assume that most financial services organizations with mobile BYOD programs for regulated employees could be fined due to a lack of compliance with electronic communications regulations.”

In the public sector, U.S. government agencies are subject to records requests under the Freedom of Information Act (FOIA) and various state sunshine statutes. For these organizations, effectively retaining business messages is about more than supporting security and compliance—it’s about maintaining public trust.

Securing enterprise messaging

Enterprise-grade messaging apps can help you protect communications from unauthorized access and facilitate desired business outcomes.

Security — Critical security protocols protect messages and files that contain sensitive and proprietary data — such as personally identifiable information, protected health information, financial records, and intellectual property — in transit and at rest to decrease the likelihood of a security incident.

Control — Administrative controls allow you to add, remove, and invite users, and organize them into security groups with restricted access to features and content at their level. Passwords can be reset and profiles can be deleted remotely, helping you reduce the risk of data exposure stemming from a lost or stolen device.

Compliance — Information can be preserved in a customer-controlled data store to help meet requirements such as those that fall under the Federal Records Act (FRA) and National Archives and Records Administration (NARA), as well as SEC Rule 17a-4 and Sarbanes-Oxley (SOX).

Marrying encryption with data retention

Enterprise solutions bring end-to-end encryption and data retention together in support of a comprehensive approach to secure messaging that balances people, process, and technology.

End-to-end encryption

Many messaging apps offer some form of encryption, but not all of them use end-to-end encryption. End-to-end encryption is a secure communication method that protects data from unauthorized access, interception, or tampering as it travels from one endpoint to another.

In end-to-end encryption, encryption and decryption take place locally, on the device. Every call, message, and file is encrypted with unique keys and remains indecipherable in transit. Unauthorized parties cannot access communication content because they don’t have the keys required to decrypt the data.

Encryption in transit compared to end-to-end encryption

Encryption in transit encrypts data over a network from one point to another (typically between one client and one server); data might remain stored in plaintext at the source and destination storage systems. End-to-end encryption combines encryption in transit and encryption at rest to secure data at all times, from being generated and leaving the sender’s device, to arriving at the recipient’s device and being decrypted.

“Messaging is a critical tool for any organization, and end-to-end encryption is the security technology that provides organizations with the confidence they need to rely on it.” — CJ Moses, CISO and VP of Security Engineering at AWS

Data retention

While data retention is often thought of as being incompatible with end-to-end encryption, leading enterprise-grade messaging apps offer both, giving you the option to configure a data store of your choice to retain conversations without exposing them to outside parties. No one other than the intended recipients and your organization has access to the message content, giving you full control over your data.

How AWS can help

AWS Wickr is an end-to-end encrypted messaging and collaboration service that was built from the ground up with features designed to help you keep internal and external communications secure, private, and compliant. Wickr protects one-to-one and group messaging, voice and video calling, file sharing, screen sharing, and location sharing with 256-bit Advanced Encryption Standard (AES) encryption, and provides data retention capabilities.

Figure 1: How Wickr works

Figure 1: How Wickr works

With Wickr, each message gets a unique AES private encryption key, and a unique Elliptic-curve Diffie–Hellman (ECDH) public key to negotiate the key exchange with recipients. Message content — including text, files, audio, or video — is encrypted on the sending device (your iPhone, for example) using the message-specific AES key. This key is then exchanged via the ECDH key exchange mechanism, so that only intended recipients can decrypt the message.

“As former employees of federal law enforcement, the intelligence community, and the military, Qintel understands the need for enterprise-federated, secure communication messaging capabilities. When searching for our company’s messaging application we evaluated the market thoroughly and while there are some excellent capabilities available, none of them offer the enterprise security and administrative flexibility that Wickr does.”
Bill Schambura, CEO at Qintel

Wickr network administrators can configure and apply data retention to both internal and external communications in a Wickr network. This includes conversations with guest users, external teams, and other partner networks, so you can retain messages and files sent to and from the organization to help meet internal, legal, and regulatory requirements.

Figure 2: Data retention process

Figure 2: Data retention process

Data retention is implemented as an always-on recipient that is added to conversations, not unlike the blind carbon copy (BCC) feature in email. The data-retention process participates in the key exchange, allowing it to decrypt messages. The process can run anywhere: on-premises, on an Amazon Elastic Compute Cloud (Amazon EC2) instance, or at a location of your choice.

Wickr is a Health Insurance Portability and Accountability Act of 1996 (HIPAA)-eligible service, helping healthcare organizations and medical providers to conduct secure telehealth visits, send messages and files that contain protected health information, and facilitate real-time patient care.

Wickr networks can be created through the AWS Management Console, and workflows can be automated with Wickr bots. Wickr is currently available in the AWS US East (Northern Virginia), AWS GovCloud (US-West), AWS Canada (Central), and AWS Europe (London) Regions.

Keep your messages safe

Employees will continue to use messaging apps to chat with friends and family, and boost productivity at work. While many of these apps can introduce risks if not used properly in business settings, Wickr combines end-to-end encryption with data-retention capabilities to help you achieve security and compliance goals. Incorporating Wickr into a comprehensive approach to secure enterprise messaging that includes clear policies and security awareness training can help you to accelerate collaboration, while protecting your organization’s data.

To learn more and get started, visit the AWS Wickr webpage, or contact us.

Want more AWS Security news? Follow us on Twitter.

Anne Grahn

Anne Grahn

Anne is a Senior Worldwide Security GTM Specialist at AWS, based in Chicago. She has more than a decade of experience in the security industry, and focuses on effectively communicating cybersecurity risk. She maintains a Certified Information Systems Security Professional (CISSP) certification.

Tanvi Jain

Tanvi Jain

Tanvi is a Senior Technical Product Manager at AWS, based in New York. She focuses on building security-first features for customers, and is passionate about improving collaboration by building technology that is easy to use, scalable, and interoperable.

Generate machine learning insights for Amazon Security Lake data using Amazon SageMaker

Post Syndicated from Jonathan Nguyen original https://aws.amazon.com/blogs/security/generate-machine-learning-insights-for-amazon-security-lake-data-using-amazon-sagemaker/

Amazon Security Lake automatically centralizes the collection of security-related logs and events from integrated AWS and third-party services. With the increasing amount of security data available, it can be challenging knowing what data to focus on and which tools to use. You can use native AWS services such as Amazon QuickSight, Amazon OpenSearch, and Amazon SageMaker Studio to visualize, analyze, and interactively identify different areas of interest to focus on, and prioritize efforts to increase your AWS security posture.

In this post, we go over how to generate machine learning insights for Security Lake using SageMaker Studio. SageMaker Studio is a web integrated development environment (IDE) for machine learning that provides tools for data scientists to prepare, build, train, and deploy machine learning models. With this solution, you can quickly deploy a base set of Python notebooks focusing on AWS Security Hub findings in Security Lake, which can also be expanded to incorporate other AWS sources or custom data sources in Security Lake. After you’ve run the notebooks, you can use the results to help you identify and focus on areas of interest related to security within your AWS environment. As a result, you might implement additional guardrails or create custom detectors to alert on suspicious activity.

Prerequisites

  1. Specify a delegated administrator account to manage the Security Lake configuration for all member accounts within your organization.
  2. Security Lake has been enabled in the delegated administrator AWS account.
  3. As part of the solution in this post, we focus on Security Hub as a data source. AWS Security Hub must be enabled for your AWS Organizations. When enabling Security Lake, select All log and event sources to include AWS Security Hub findings.
  4. Configure subscriber query access to Security Lake. Security Lake uses AWS Lake Formation cross-account table sharing to support subscriber query access. Accept the resource share request in the subscriber AWS account in AWS Resource Access Manager (AWS RAM). Subscribers with query access can query the data that Security Lake collects. These subscribers query Lake Formation tables in an Amazon Simple Storage Service (Amazon S3) bucket with Security Lake data using services such as Amazon Athena.

Solution overview

Figure 1 that follows depicts the architecture of the solution.

Figure 1 SageMaker machine learning insights architecture for Security Lake

Figure 1 SageMaker machine learning insights architecture for Security Lake

The deployment builds the architecture by completing the following steps:

  1. A Security Lake is set up in an AWS account with supported log sources — such as Amazon VPC Flow Logs, AWS Security Hub, AWS CloudTrail, and Amazon Route53 — configured.
  2. Subscriber query access is created from the Security Lake AWS account to a subscriber AWS account.

    Note: See Prerequisite #4 for more information.

  3. The AWS RAM resource share request must be accepted in the subscriber AWS account where this solution is deployed.

    Note: See Prerequisite #4 for more information.

  4. A resource link database in Lake Formation is created in the subscriber AWS account and grants access for the Athena tables in the Security Lake AWS account.
  5. VPC is provisioned for SageMaker with IGW, NAT GW, and VPC endpoints for the AWS services used in the solution. IGW and NAT are required to install external open-source packages.
  6. A SageMaker Domain for SageMaker Studio is created in VPCOnly mode with a single SageMaker user profile that is tied to a dedicated AWS Identity and Access Management (IAM) role.
  7. A dedicated IAM role is created to restrict access to create and access the presigned URL for the SageMaker Domain from a specific CIDR for accessing the SageMaker notebook.
  8. An AWS CodeCommit repository containing Python notebooks is used for the AI and ML workflow by the SageMaker user-profile.
  9. An Athena workgroup is created for the Security Lake queries with an S3 bucket for output location (access logging configured for the output bucket).

Deploy the solution

You can deploy the SageMaker solution by using either the AWS Management Console or the AWS Cloud Development Kit (AWS CDK).

Option 1: Deploy the solution with AWS CloudFormation using the console

Use the console to sign in to your subscriber AWS account and then choose the Launch Stack button to open the AWS CloudFormation console pre-loaded with the template for this solution. It takes approximately 10 minutes for the CloudFormation stack to complete.

Select this image to open a link that starts building the CloudFormation stack

Option 2: Deploy the solution by using the AWS CDK

You can find the latest code for the SageMaker solution in the SageMaker machine learning insights GitHub repository, where you can also contribute to the sample code. For instructions and more information on using the AWS CDK, see Get Started with AWS CDK.

To deploy the solution by using the AWS CDK

  1. To build the app when navigating to the project’s root folder, use the following commands:
    npm install -g aws-cdk-lib
    npm install

  2. Update IAM_role_assumption_for_sagemaker_presigned_url and security_lake_aws_account default values in source/lib/sagemaker_domain.ts with their respective appropriate values.
  3. Run the following commands in your terminal while authenticated in your subscriber AWS account. Be sure to replace <INSERT_AWS_ACCOUNT> with your account number and replace <INSERT_REGION> with the AWS Region that you want the solution deployed to.
    cdk bootstrap aws://<INSERT_AWS_ACCOUNT>/<INSERT_REGION>
    cdk deploy

Post deployment steps

Now that you’ve deployed the SageMaker solution, you must grant the SageMaker user profile in the subscriber AWS account query access to your Security Lake. You can Grant permission for the SageMaker user profile to Security Lake in Lake Formation in the subscriber AWS account.

Grant permission to the Security Lake database

  1. Copy the SageMaker user-profile Amazon resource name (ARN) arn:aws:iam::<account-id>:role/sagemaker-user-profile-for-security-lake
  2. Go to Lake Formation in the console.
  3. Select the amazon_security_lake_glue_db_us_east_1 database.
  4. From the Actions Dropdown, select Grant.
  5. In Grant Data Permissions, select SAML Users and Groups.
  6. Paste the SageMaker user profile ARN from Step 1.
  7. In Database Permissions, select Describe and then Grant.

Grant permission to Security Lake – Security Hub table

  1. Copy the SageMaker user-profile ARN arn:aws:iam:<account-id>:role/sagemaker-user-profile-for-security-lake
  2. Go to Lake Formation in the console.
  3. Select the amazon_security_lake_glue_db_us_east_1 database.
  4. Choose View Tables.
  5. Select the amazon_security_lake_table_us_east_1_sh_findings_1_0 table.
  6. From Actions Dropdown, select Grant.
  7. In Grant Data Permissions, select SAML Users and Groups.
  8. Paste the SageMaker user-profile ARN from Step 1.
  9. In Table Permissions, select Describe and then Grant.

Launch your SageMaker Studio application

Now that you have granted permissions for a SageMaker user-profile, we can move on to launching the SageMaker application associated to that user-profile.

  1. Navigate to the SageMaker Studio domain in the console.
  2. Select the SageMaker domain security-lake-ml-insights-<account-id>.
  3. Select the SageMaker user profile sagemaker-user-profile-for-security-lake.
  4. Select the Launch drop-down and select Studio
    Figure 2 SageMaker domain user-profile AWS console screen

    Figure 2: SageMaker domain user-profile AWS console screen

Clone Python notebooks

You’ll work primarily in the SageMaker user profile to create a data-science app to work in. As part of the solution deployment, we’ve created Python notebooks in CodeCommit that you will need to clone.

To clone the Python notebooks

  1. Navigate to CloudFormation in the console.
  2. In the Stacks section, select the SageMakerDomainStack.
  3. Select to the Outputs tab/
  4. Copy the value for sagemakernotebookmlinsightsrepositoryURL. (For example: https://git-codecommit.us-east-1.amazonaws.com/v1/repos/sagemaker_ml_insights_repo)
  5. Go back to your SageMaker app.
  6. In Studio, in the left sidebar, choose the Git icon (identified by a diamond with two branches), then choose Clone a Repository.
    Figure 3 SageMaker clone CodeCommit repository

    Figure 3: SageMaker clone CodeCommit repository

  7. Paste the CodeCommit repository link from Step 4 under the Git repository URL (git). After you paste the URL, select Clone “https://git-codecommit.us-east-1.amazonaws.com/v1/repos/sagemaker_ml_insights_repo”, then select Clone.

    NOTE: If you don’t select from the auto-populated drop-down, SageMaker won’t be able to clone the repository.

    Figure 4 SageMaker clone CodeCommit URL

    Figure 4: SageMaker clone CodeCommit URL

Generating machine learning insights using SageMaker Studio

You’ve successfully pulled the base set of Python notebooks into your SageMaker app and they can be accessed at sagemaker_ml_insights_repo/notebooks/tsat/. The notebooks provide you with a starting point for running machine learning analysis using Security Lake data. These notebooks can be expanded to existing native or custom data sources being sent to Security Lake.

Figure 5: SageMaker cloned Python notebooks

Figure 5: SageMaker cloned Python notebooks

Notebook #1 – Environment setup

The 0.0-tsat-environ-setup notebook handles the installation of the required libraries and dependencies needed for the subsequent notebooks within this blog. For our notebooks, we use an open-source Python library called Kats, which is a lightweight, generalizable framework to perform time series analysis.

  1. Select the 0.0-tsat-environ-setup.ipynb notebook for the environment setup.

    Note: If you have already provisioned a kernel, you can skip steps 2 and 3.

  2. In the right-hand corner, select No Kernel
  3. In the Set up notebook environment pop-up, leave the defaults and choose Select.
    Figure 6 SageMaker application environment settings

    Figure 6: SageMaker application environment settings

  4. After the kernel has successfully started, choose the Terminal icon to open the image terminal.
    Figure 7: SageMaker application terminal

    Figure 7: SageMaker application terminal

  5. To install open-source packages from https instead of http, you must update the sources.list file. After the terminal opens, send the following commands:
    cd /etc/apt
    sed -i 's/http:/https:/g' sources.list

  6. Go back to the 0.0-tsat-environ-setup.ipynb notebook and select the Run drop-down and select Run All Cells. Alternatively, you can run each cell independently, but it’s not required. Grab a coffee! This step will take about 10 minutes.

    IMPORTANT: If you complete the installation out of order or update the requirements.txt file, you might not be able to successfully install Kats and you will need to rebuild your environment by using a net-new SageMaker user profile.

  7. After installing all the prerequisites, check the Kats version to determine if it was successfully installed.
    Figure 8: Kats installation verification

    Figure 8: Kats installation verification

  8. Install PyAthena (Python DB API client for Amazon Athena) which is used to query your data in Security Lake.

You’ve successfully set up the SageMaker app environment! You can now load the appropriate dataset and create a time series.

Notebook #2 – Load data

The 0.1-load-data notebook establishes the Athena connection to query data in Security Lake and creates the resulting time series dataset. The time series dataset will be used for subsequent notebooks to identify trends, outliers, and change points.

  1. Select the 0.1-load-data.ipynb notebook.
  2. If you deployed the solution outside of us-east-1, update the con details to the appropriate Region. In this example, we’re focusing on Security Hub data within Security Lake. If you want to change the underlying data source, you can update the TABLE value.
    Figure 9: SageMaker notebook load Security Lake data settings

    Figure 9: SageMaker notebook load Security Lake data settings

  3. In the Query section, there’s an Athena query to pull specific data from Security Hub, this can be expanded as needed to a subset or can include all products within Security Hub. The query below pulls Security Hub information after 01:00:00 1/1/2022 from the products listed in productname.
    Figure 10: SageMaker notebook Athena query

    Figure 10: SageMaker notebook Athena query

  4. After the values have been updated, you can create your time series dataset. For this notebook, we recommend running each cell individually instead of running all cells at once so you can get a bit more familiar with the process. Select the first cell and choose the Run icon.
    Figure 11: SageMaker run Python notebook code

    Figure 11: SageMaker run Python notebook code

  5. Follow the same process as Step 4 for the subsequent cells.

    Note: If you encounter any issues with querying the table, make sure you completed the post-deployment step for Grant permission to Security Lake – Security Hub table.

You’ve successfully loaded your data and created a timeseries! You can now move on to generating machine learning insights from your timeseries.

Notebook #3 – Trend detector

The 1.1-trend-detector.ipynb notebook handles trend detection in your data. Trend represents a directional change in the level of a time series. This directional change can be either upward (increase in level) or downward (decrease in level). Trend detection helps detect a change while ignoring the noise from natural variability. Each environment is different, and trends help us identify where to look more closely to determine why a trend is positive or negative.

  1. Select 1.1-trend-detector.ipynb notebook for trend detection.
  2. Slopes are created to identify the relationship between x (time) and y (counts).
    Figure 12: SageMaker notebook slope view

    Figure 12: SageMaker notebook slope view

  3. If the counts are increasing with time, then it’s considered a positive slope and the reverse is considered a negative slope. A positive slope isn’t necessarily a good thing because in an ideal state we would expect counts of a finding type to come down with time.
    Figure 13: SageMaker notebook trend view

    Figure 13: SageMaker notebook trend view

  4. Now you can plot the top five positive and negative trends to identify the top movers.
    Figure 14: SageMaker notebook trend results view

    Figure 14: SageMaker notebook trend results view

Notebook #4 – Outlier detection

The 1.2-outlier-detection.ipynb notebook handles outlier detection. This notebook does a seasonal decomposition of the input time series, with additive or multiplicative decomposition as specified (default is additive). It uses a residual time series by either removing only trend or both trend and seasonality if the seasonality is strong. The intent is to discover useful, abnormal, and irregular patterns within data sets, allowing you to pinpoint areas of interest.

  1. To start, it detects points in the residual that are over 5 times the inter-quartile range.
  2. Inter-quartile range (IQR) is the difference between the seventy-fifth and twenty-fifth percentiles of residuals or the spread of data within the middle two quartiles of the entire dataset. IQR is useful in detecting the presence of outliers by looking at values that might lie outside of the middle two quartiles.
  3. The IQR multiplier controls the sensitivity of the range and decision of identifying outliers. By using a larger value for the iqr_mult_thresh parameter in OutlierDetector, outliers would be considered data points, while a smaller value would identify data points as outliers.

    Note: If you don’t have enough data, decrease iqr_mult_thresh to a lower value (for example iqr_mult_thresh=3).

    Figure 15: SageMaker notebook outlier setting

    Figure 15: SageMaker notebook outlier setting

  4. Along with outlier detection plots, investigation SQL will be displayed as well, which can help with further investigation of the outliers.

    In the diagram that follows, you can see that there are several outliers in the number of findings, related to failed AWS Firewall Manager policies, which have been identified by the vertical red lines within the line graph. These are outliers because they deviate from the normal behavior and number of findings on a day-to-day basis. When you see outliers, you can look at the resources that might have caused an unusual increase in Firewall Manager policy findings. Depending on the findings, it could be related to an overly permissive or noncompliant security group or a misconfigured AWS WAF rule group.

    Figure 16: SageMaker notebook outlier results view

    Figure 16: SageMaker notebook outlier results view

Notebook #5 – Change point detection

The 1.3-changepoint-detector.ipynb notebook handles the change point detection. Change point detection is a method to detect changes in a time series that persist over time, such as a change in the mean value. To detect a baseline to identify when several changes might have occurred from that point. Change points occur when there’s an increase or decrease to the average number of findings within a data set.

  1. Along with identifying change points within the data set, the investigation SQL is generated to further investigate the specific change point if applicable.

    In the following diagram, you can see there’s a change point decrease after July 27, 2022, with confidence of 99.9 percent. It’s important to note that change points differ from outliers, which are sudden changes in the data set observed. This diagram means there was some change in the environment that resulted in an overall decrease in the number of findings for S3 buckets with block public access being disabled. The change could be the result of an update to the CI/CD pipelines provisioning S3 buckets or automation to enable all S3 buckets to block public access. Conversely, if you saw a change point that resulted in an increase, it could mean that there was a change that resulted in a larger number of S3 buckets with a block public access configuration consistently being disabled.

    Figure 17: SageMaker changepoint detector view

    Figure 17: SageMaker changepoint detector view

By now, you should be familiar with the set up and deployment for SageMaker Studio and how you can use Python notebooks to generate machine learning insights for your Security Lake data. You can take what you’ve learned and start to curate specific datasets and data sources within Security Lake, create a time series, detect trends, and identify outliers and change points. By doing so, you can answer a variety of security-related questions such as:

  • CloudTrail

    Is there a large volume of Amazon S3 download or copy commands to an external resource? Are you seeing a large volume of S3 delete object commands? Is it possible there’s a ransomware event going on?

  • VPC Flow Logs

    Is there an increase in the number of requests from your VPC to external IPs? Is there an increase in the number of requests from your VPC to your on-premises CIDR? Is there a possibility of internal or external data exfiltration occurring?

  • Route53

    Which resources are making DNS requests that they haven’t typically made within the last 30–45 days? When did it start? Is there a potential command and control session occurring on an Amazon Elastic Compute Cloud (Amazon EC2) instance?

It’s important to note that this isn’t a solution to replace Amazon GuardDuty, which uses foundational data sources to detect communication with known malicious domains and IP addresses and identify anomalous behavior, or Amazon Detective, which provides customers with prebuilt data aggregations, summaries, and visualizations to help security teams conduct faster and more effective investigations. One of the main benefits of using Security Lake and SageMaker Studio is the ability to interactively create and tailor machine learning insights specific to your AWS environment and workloads.

Clean up

If you deployed the SageMaker machine learning insights solution by using the Launch Stack button in the AWS Management Console or the CloudFormation template sagemaker_ml_insights_cfn, do the following to clean up:

  1. In the CloudFormation console for the account and Region where you deployed the solution, choose the SageMakerML stack.
  2. Choose the option to Delete the stack.

If you deployed the solution by using the AWS CDK, run the command cdk destroy.

Conclusion

Amazon Security Lake gives you the ability to normalize and centrally store your security data from various log sources to help you analyze, visualize, and correlate appropriate security logs. You can then use this data to increase your overall security posture by implementing additional security guardrails or take appropriate remediation actions within your AWS environment.

In this blog post, you learned how you can use SageMaker to generate machine learning insights for your Security Hub findings in Security Lake. Although the example solution focuses on a single data source within Security Lake, you can expand the notebooks to incorporate other native or custom data sources in Security Lake.

There are many different use-cases for Security Lake that can be tailored to fit your AWS environment. Take a look at this blog post to learn how you can ingest, transform and deliver Security Lake data to Amazon OpenSearch to help your security operations team quickly analyze security data within your AWS environment. In supported Regions, new Security Lake account holders can try the service free for 15 days and gain access to its features.

 
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Want more AWS Security news? Follow us on Twitter.

Jonathan Nguyen

Jonathan Nguyen

Jonathan is a Principal Security Architect at AWS. His background is in AWS security, with a focus on threat detection and incident response. He helps enterprise customers develop a comprehensive AWS security strategy, deploy security solutions at scale, and train customers on AWS security best practices.

Madhunika Reddy Mikkili

Madhunika Reddy Mikkili

Madhunika is a Data and Machine Learning Engineer with the AWS Professional Services Shared Delivery Team. She is passionate about helping customers achieve their goals through the use of data and machine learning insights. Outside of work, she loves traveling and spending time with family and friends.

Application Security Report: Q2 2023

Post Syndicated from Michael Tremante original http://blog.cloudflare.com/application-security-report-q2-2023/

Application Security Report: Q2 2023

Application Security Report: Q2 2023

Cloudflare has a unique vantage point on the Internet. From this position, we are able to see, explore, and identify trends that would otherwise go unnoticed. In this report we are doing just that and sharing our insights into Internet-wide application security trends.

This report is the third edition of our Application Security Report. The first one was published in March 2022, with the second published earlier this year in March, and this is the first to be published on a  quarterly basis.

Since the last report, our network is bigger and faster: we are now processing an average of 46 million HTTP requests/second and 63 million at peak. We consistently handle approximately 25 million DNS queries per second. That's around 2.1 trillion DNS queries per day, and 65 trillion queries a month. This is the sum of authoritative and resolver requests served by our infrastructure. Summing up both HTTP and DNS requests, we get to see a lot of malicious traffic. Focusing on HTTP requests only, in Q2 2023 Cloudflare blocked an average of 112 billion cyber threats each day, and this is the data that powers this report.

But as usual, before we dive in, we need to define our terms.

Definitions

Throughout this report, we will refer to the following terms:

  • Mitigated traffic: any eyeball HTTP* request that had a “terminating” action applied to it by the Cloudflare platform. These include the following actions: BLOCK, CHALLENGE, JS_CHALLENGE and MANAGED_CHALLENGE. This does not include requests that had the following actions applied: LOG, SKIP, ALLOW. In contrast to last year, we now exclude requests that had CONNECTION_CLOSE and FORCE_CONNECTION_CLOSE actions applied by our DDoS mitigation system, as these technically only slow down connection initiation. They also accounted for a relatively small percentage of requests. Additionally, we improved our calculation regarding the CHALLENGE type actions to ensure that only unsolved challenges are counted as mitigated. A detailed description of actions can be found in our developer documentation.
  • Bot traffic/automated traffic: any HTTP* request identified by Cloudflare’s Bot Management system as being generated by a bot. This includes requests with a bot score between 1 and 29 inclusive. This has not changed from last year’s report.
  • API traffic: any HTTP* request with a response content type of XML or JSON. Where the response content type is not available, such as for mitigated requests, the equivalent Accept content type (specified by the user agent) is used instead. In this latter case, API traffic won’t be fully accounted for, but it still provides a good representation for the purposes of gaining insights.

Unless otherwise stated, the time frame evaluated in this post is the 3 month period from April 2023 through June 2023 inclusive.

Finally, please note that the data is calculated based only on traffic observed across the Cloudflare network and does not necessarily represent overall HTTP traffic patterns across the Internet.

* When referring to HTTP traffic we mean both HTTP and HTTPS.

  Global traffic insights

Mitigated daily traffic stable at 6%, spikes reach 8%

Although daily mitigated HTTP requests decreased by 2 percentage points to 6% on average from 2021 to 2022, days with larger than usual malicious activity can be clearly seen across the network. One clear example is shown in the graph below: towards the end of May 2023, a spike reaching nearly 8% can be seen. This is attributable to large DDoS events and other activity that does not follow standard daily or weekly cycles and is a constant reminder that large malicious events can still have a visible impact at a global level, even at Cloudflare scale.

Application Security Report: Q2 2023

75% of mitigated HTTP requests were outright BLOCKed. This is a 6 percentage point decrease compared to the previous report. The majority of other requests are mitigated with the various CHALLENGE type actions, with managed challenges leading with ~20% of this subset.

Shields up: customer configured rules now biggest contributor to mitigated traffic

In our previous report, our automated DDoS mitigation system accounted for, on average, more than 50% of mitigated traffic. Over the past two quarters, due to both increased WAF adoption, but most likely organizations better configuring and locking down their applications from unwanted traffic, we’ve seen a new trend emerge, with WAF mitigated traffic surpassing DDoS mitigation. Most of the increase has been driven by WAF Custom Rule BLOCKs rather than our WAF Managed Rules, indicating that these mitigations are generated by customer configured rules for business logic or related purposes. This can be clearly seen in the chart below.

Application Security Report: Q2 2023

Note that our WAF Managed Rules mitigations (yellow line) are negligible compared to overall WAF mitigated traffic also indicating that customers are adopting positive security models by allowing known good traffic as opposed to blocking only known bad traffic. Having said that, WAF Managed Rules mitigations reached as much as 1.5 billion/day during the quarter.

Our DDoS mitigation is, of course, volumetric and the amount of traffic matching our DDoS layer 7 rules should not be underestimated, especially given that we are observing a number of novel attacks and botnets being spun up across the web. You can read a deep dive on DDoS attack trends in our Q2 DDoS threat report.

Aggregating the source of mitigated traffic, the WAF now accounts for approximately 57% of all mitigations. Tabular format below with other sources for reference.

Application Security Report: Q2 2023

Source

Percentage %

WAF

57%

DDoS Mitigation

34%

IP Reputation

6%

Access Rules

2%

Other

1%

Application owners are increasingly relying on geo location blocks

Given the increase in mitigated traffic from customer defined WAF rules, we thought it would be interesting to dive one level deeper and better understand what customers are blocking and how they are doing it. We can do this by reviewing rule field usage across our WAF Custom Rules to identify common themes. Of course, the data needs to be interpreted correctly, as not all customers have access to all fields as that varies by contract and plan level, but we can still make some inferences based on field “categories”. By reviewing all ~7M WAF Custom Rules deployed across the network and focusing on main groupings only, we get the following field usage distribution:

Field

Used in percentage % of rules

Geolocation fields

40%

HTTP URI

31%

IP address

21%

Other HTTP fields (excluding URI)

34%

Bot Management fields

11%

IP reputation score

4%

Notably, 40% of all deployed WAF Custom Rules use geolocation-related fields to make decisions on how to treat traffic. This is a common technique used to implement business logic or to exclude geographies from which no traffic is expected and helps reduce attack surface areas. While these are coarse controls which are unlikely to stop a sophisticated attacker, they are still efficient at reducing the attack surface.

Another notable observation is the usage of Bot Management related fields in 11% of WAF Custom Rules. This number has been steadily increasing over time as more customers adopt machine learning-based classification strategies to protect their applications.

Old CVEs are still exploited en masse

Contributing ~32% of WAF Managed Rules mitigated traffic overall, HTTP Anomaly is still the most common attack category blocked by the WAF Managed Rules. SQLi moved up to second position, surpassing Directory Traversal with 12.7% and 9.9% respectively.

If we look at the start of April 2023, we notice the DoS category far exceeding the HTTP Anomaly category. Rules in the DoS category are WAF layer 7 HTTP signatures that are sufficiently specific to match (and block) single requests without looking at cross request behavior and that can be attributed to either specific botnets or payloads that cause denial of service (DoS). Normally, as is the case here, these requests are not part of “distributed” attacks, hence the lack of the first “D” for “distributed” in the category name.

Application Security Report: Q2 2023

Tabular format for reference (top 10 categories):

Source

Percentage %

HTTP Anomaly

32%

SQLi

13%

Directory Traversal

10%

File Inclusion

9%

DoS

9%

XSS

9%

Software Specific

7%

Broken Authentication

6%

Common Injection

3%

CVE

1%

Zooming in, and filtering on the DoS category only, we find that most of the mitigated traffic is attributable to one rule: 100031 / ce02fd… (old WAF and new WAF rule ID respectively). This rule, with a description of “Microsoft IIS – DoS, Anomaly:Header:Range – CVE:CVE-2015-1635” pertains to a CVE dating back to 2015 that affected a number of Microsoft Windows components resulting in remote code execution*. This is a good reminder that old CVEs, even those dating back more than 8 years, are still actively exploited to compromise machines that may be unpatched and still running vulnerable software.

* Due to rule categorisation, some CVE specific rules are still assigned to a broader category such as DoS in this example. Rules are assigned to a CVE category only when the attack payload does not clearly overlap with another more generic category.

Application Security Report: Q2 2023

Another interesting observation is the increase in Broken Authentication rule matches starting in June. This increase is also attributable to a single rule deployed across all our customers, including our FREE users: “WordPress – Broken Access Control, File Inclusion”. This rule is blocking attempts to access wp-config.php – the WordPress default configuration file which is normally found in the web server document root directory, but of course should never be accessed directly via HTTP.

On a similar note, CISA/CSA recently published a report highlighting the 2022 Top Routinely Exploited Vulnerabilities. We took this opportunity to explore how each CVE mentioned in CISA’s report was reflected in Cloudflare’s own data. The CISA/CSA discuss 12 vulnerabilities that malicious cyber actors routinely exploited in 2022. However, based on our analysis, two CVEs mentioned in the CISA report are responsible for the vast majority of attack traffic we have seen in the wild: Log4J and Atlassian Confluence Code Injection. Our data clearly suggests a major difference in exploit volume between the top two and the rest of the list. The following chart compares the attack volume (in logarithmic scale) of the top 6 vulnerabilities of the CISA list according to our logs.

Application Security Report: Q2 2023

Bot traffic insights

Cloudflare’s Bot Management continues to see significant investment as the addition of JavaScript Verified URLs for greater protection against browser-based bots, Detection IDs are now available in Custom Rules for additional configurability, and an improved UI for easier onboarding. For self-serve customers, we’ve added the ability to “Skip” Super Bot Fight Mode rules and support for WordPress Loopback requests, to better integrate with our customers’ applications and give them the protection they need.

Our confidence in the Bot Management classification output remains very high. If we plot the bot scores across the analyzed time frame, we find a very clear distribution, with most requests either being classified as definitely bot (score below 30) or definitely human (score greater than 80), with most requests actually scoring less than 2 or greater than 95. This equates, over the same time period, to 33% of traffic being classified as automated (generated by a bot). Over longer time periods we do see the overall bot traffic percentage stable at 29%, and this reflects the data shown on Cloudflare Radar.

Application Security Report: Q2 2023

On average, more than 10% of non-verified bot traffic is mitigated

Compared to the last report, non-verified bot HTTP traffic mitigation is currently on a downward trend (down 6 percentage points). However, the Bot Management field usage within WAF Custom Rules is non negligible, standing at 11%. This means that there are more than 700k WAF Custom Rules deployed on Cloudflare that are relying on bot signals to perform some action. The most common field used is cf.client.bot, an alias to cf.bot_management.verified_bot which is powered by our list of verified bots and allows customers to make a distinction between “good” bots and potentially “malicious”  non-verified ones.

Enterprise customers have access to the more powerful cf.bot_management.score which provides direct access to the score computed on each request, the same score used to generate the bot score distribution graph in the prior section.

Application Security Report: Q2 2023

The above data is also validated by looking at what Cloudflare service is mitigating unverified bot traffic. Although our DDoS mitigation system is automatically blocking HTTP traffic across all customers, this only accounts for 13% of non-verified bot mitigations. On the other hand, WAF, and mostly customer defined rules, account for 77% of such mitigations, much higher than mitigations across all traffic (57%) discussed at the start of the report. Note that Bot Management is specifically called out but refers to our “default” one-click rules, which are counted separately from the bot fields used in WAF Custom Rules.

Application Security Report: Q2 2023

Tabular format for reference:

Source

Percentage %

WAF

77%

DDoS Mitigation

13%

IP reputation

5%

Access Rules

3%

Other

1%

API traffic insights

The growth of overall API traffic observed by Cloudflare is not slowing down. Compared to last quarter, we are now seeing 58% of total dynamic traffic be classified as API related. This is a 3 percentage point increase as compared to Q1.

Application Security Report: Q2 2023

Our investment in API Gateway is also following a similar growth trend. Over the last quarter we have released several new API security features.

First, we’ve made API Discovery easier to use with a new inbox view. API Discovery inventories your APIs to prevent shadow IT and zombie APIs, and now customers can easily filter to show only new endpoints found by API Discovery. Saving endpoints from API Discovery places them into our Endpoint Management system.

Next, we’ve added a brand new API security feature offered only at Cloudflare: the ability to control API access by client behavior. We call it Sequence Mitigation. Customers can now create positive or negative security models based on the order of API paths accessed by clients. You can now ensure that your application’s users are the only ones accessing your API instead of brute-force attempts that ignore normal application functionality. For example, in a banking application you can now enforce that access to the funds transfer endpoint can only be accessed after a user has also accessed the account balance check endpoint.

We’re excited to continue releasing API security and API management features for the remainder of 2023 and beyond.

65% of global API traffic is generated by browsers

The percentage of API traffic generated by browsers has remained very stable over the past quarter. With this statistic, we are referring to HTTP requests that are not serving HTML based content that will be directly rendered by the browser without some preprocessing, such as those more commonly known as AJAX calls which would normally serve JSON based responses.

Application Security Report: Q2 2023

HTTP Anomalies are the most common attack vector on API endpoints

Just like last quarter, HTTP Anomalies remain the most common mitigated attack vector on API traffic. SQLi injection attacks, however, are non negligible, contributing approximately 11% towards the total mitigated traffic, closely followed by XSS attacks, at around 9%.

Application Security Report: Q2 2023

Tabular format for reference (top 5):

Source

Percentage %

HTTP Anomaly

64%

SQLi

11%

XSS

9%

Software Specific

5%

Command Injection

4%

Looking forward

As we move our application security report to a quarterly cadence, we plan to deepen some of the insights and to provide additional data from some of our newer products such as Page Shield, allowing us to look beyond HTTP traffic, and explore the state of third party dependencies online.

Stay tuned and keep an eye on Cloudflare Radar for more frequent application security reports and insights.

An August reading list about online security and 2023 attacks landscape

Post Syndicated from João Tomé original http://blog.cloudflare.com/an-august-reading-list-about-online-security-and-2023-attacks-landscape/

An August reading list about online security and 2023 attacks landscape

An August reading list about online security and 2023 attacks landscape

In 2023, cybersecurity continues to be in most cases a need-to-have for those who don’t want to take chances on getting caught in a cyberattack and its consequences. Attacks have gotten more sophisticated, while conflicts (online and offline, and at the same time) continue, including in Ukraine. Governments have heightened their cyber warnings and put together strategies, including around critical infrastructure (including health and education). All of this, at a time when there were never so many online risks, but also people online — over five billion in July 2023, 64.5% of the now eight billion that are the world’s total population.

Here we take a look at what we’ve been discussing in 2023, so far, in our Cloudflare blog related to attacks and online security in general, with several August reading list suggestions. From new trends, products, initiatives or partnerships, including AI service safety, to record-breaking blocked cyberattacks. On that note, our AI hub (ai.cloudflare.com) was just launched.

Throughout the year, Cloudflare has continued to onboard customers while they were being attacked, and we have provided protection to many others, including once.net, responsible for the 2023 Eurovision Song Contest online voting system — the European event reached 162 million people.

Our global network — a.k.a. Supercloud — gives us a unique vantage point. Cloudflare’s extensive scale also helps enhance security, with preventive services powered by machine learning, like our recent WAF attack scoring system to stop attacks before they become known or even malware.

Recently, we announced our presence in more than 300 cities across over 100 countries, with interconnections to over 12,000 networks and still growing. We provide services for around 20% of websites online and to millions of Internet properties.

Attacks increasing. A readiness and trust game

Let’s start with providing some context. There are all sorts of attacks, but they have been, generally speaking, increasing. In Q2 2023, Cloudflare blocked an average of 140 billion cyber threats per day. One year ago, when we wrote a similar blog post, it was 124 billion, a 13% increase year over year. Attackers are not holding back, with more sophisticated attacks rising, and sectors such as education or healthcare as the target.

Artificial intelligence (AI), like machine learning, is not new, but it has been trending in 2023, and certain capabilities are more generally available. This has raised concerns about the quality of deception and even AI hackers.

This year, governments have also continued to release reports and warnings. In 2022, the US Cybersecurity and Infrastructure Security Agency (CISA) created the Shields Up initiative in response to Russia's invasion of Ukraine. In March 2023, the Biden-Harris Administration released the National Cybersecurity Strategy aimed at securing the Internet.

The UK’s Cyber Strategy was launched at the end of 2022, and in March of this year, a strategy was released to specifically protect its National Health Service (NHS) from cyber attacks — in May it was time for the UK’s Ministry of Defence to do the same. In Germany, the new Digital Strategy is from 2022, but the Security Strategy arrived in June. A similar scenario is seen in Japan, Australia, and others.

That said, here are the reading suggestions related to more general country related attacks, but also policy and trust cybersecurity:

This blog post reports on Internet insights during the war in Europe, and discusses how Ukraine's Internet remained resilient in spite of dozens of attacks, and disruptions in three different stages of the conflict.

An August reading list about online security and 2023 attacks landscape
Application-layer cyber attacks in Ukraine rose 1,300% in early March 2022 compared to pre-war levels.

The White House’s National Cybersecurity Strategy asks the private sector to step up to fight cyber attacks. Cloudflare is ready (✍️)

The White House released in March 2023 the National Cybersecurity Strategy aimed at preserving and extending the open, free, global, interoperable, reliable, and securing the Internet. Cloudflare welcomed the Strategy, and the much-needed policy initiative, highlighting the need of defending critical infrastructure, where Zero Trust plays a big role. In the same month, Cloudflare announced its commitment to the 2023 Summit for Democracy. Also related to these initiatives, in March 2022, we launched our very own Critical Infrastructure Defense Project (CIDP), and in December 2022, Cloudflare launched Project Safekeeping, offering Zero Trust solutions to certain eligible entities in Australia, Japan, Germany, Portugal and the United Kingdom.

Secure by default: recommendations from the CISA’s newest guide, and how Cloudflare follows these principles to keep you secure (✍️)

In this April 2023 post we reviewed the “default secure” posture, and recommendations that were the focus of a recently published guide jointly authored by several international agencies. It had US, UK, Australia, Canada, Germany, Netherlands, and New Zealand contributions. Long story short, using all sorts of tools, machine learning and a secure-by-default and by-design approach, and a few principles, will make all the difference.

Nine years of Project Galileo and how the last year has changed it (✍️) + Project Galileo Report (✍️)

For the ninth anniversary of our Project Galileo in June 2023, the focus turned towards providing access to affordable cybersecurity tools and sharing our learnings from protecting the most vulnerable communities. There are also Project Galileo case studies and how it has made a difference, including to those in education and health, cultural, veterans’ services, Internet archives, and investigative journalism. A Cloudflare Radar Project Galileo report was also disclosed, with some highlights worth mentioning:

  • Between July 1, 2022, and May 5, 2023, Cloudflare mitigated 20 billion attacks against organizations protected under Project Galileo. This is an average of nearly 67.7 million cyber attacks per day over the last 10 months.
  • For LGBTQ+ organizations, we saw an average of 790,000 attacks mitigated per day over the last 10 months, with a majority of those classified as DDoS attacks.
  • Attacks targeting civil society organizations are generally increasing. We have broken down an attack aimed at a prominent organization, with the request volume climbing as high as 667,000 requests per second. Before and after this time the organization saw little to no traffic.
  • In Ukraine, spikes in traffic to organizations that provide emergency response and disaster relief coincide with bombings of the country over the 10-month period.

Project Cybersafe Schools: bringing security tools for free to small K-12 school districts in the US (✍️)

Already in August 2023, Cloudflare introduced an initiative aimed at small K-12 public school districts: Project Cybersafe Schools. Announced as part of the Back to School Safely: K-12 Cybersecurity Summit at the White House on August 7, Project Cybersafe Schools will support eligible K-12 public school districts with a package of Zero Trust cybersecurity solutions — for free, and with no time limit. In Q2 2023, Cloudflare blocked an average of 70 million cyber threats each day targeting the U.S. education sector, and a 47%  increase in DDoS attacks quarter-over-quarter.

Privacy concerns also go hand in hand with security online, and we’ve provided further details on this topic earlier this year in relation to our investment in security to protect data privacy. Cloudflare also achieved a new EU Cloud Code of Conduct privacy validation.

An August reading list about online security and 2023 attacks landscape
This is what a record-breaking DDoS attack (exceeding 71 million requests per second) looks like.

1. DDoS attacks & solutions

DDoS threat report for 2023 Q2 (✍️)

DDoS attacks (distributed denial-of-service) are not new, but they’re still one of the main tools used by attackers. In Q2 2023, Cloudflare witnessed an unprecedented escalation in DDoS attack sophistication, and our report delves into this phenomenon. Pro-Russian hacktivists REvil, Killnet and Anonymous Sudan joined forces to attack Western sites. Mitel vulnerability exploits surged by a whopping 532%, and attacks on crypto rocketed up by 600%. Also, more broadly, attacks exceeding three hours have increased by 103% quarter-over-quarter.

This blog post and the corresponding Cloudflare Radar report shed light on some of these trends. On the other hand, in our Q1 2023 DDoS threat report, a surge in hyper-volumetric attacks that leverage a new generation of botnets that are comprised of Virtual Private Servers (VPS) was observed.

Killnet and AnonymousSudan DDoS attack Australian university websites, and threaten more attacks — here’s what to do about it  (✍️)

In late March 2023, Cloudflare observed HTTP DDoS attacks targeting university websites in Australia. Universities were the first of several groups publicly targeted by the pro-Russian hacker group Killnet and their affiliate AnonymousSudan. This post not only shows a trend with these organized groups targeted attacks but also provides specific recommendations.

In January 2023, something similar was seen with increased cyberattacks to Holocaust educational websites protected by Cloudflare’s Project Galileo.

Uptick in healthcare organizations experiencing targeted DDoS attacks (✍️)

In early February 2023, Cloudflare, as well as other sources, observed an uptick in healthcare organizations targeted by a pro-Russian hacktivist group claiming to be Killnet. There was an increase in the number of these organizations seeking our help to defend against such attacks. Additionally, healthcare organizations that were already protected by Cloudflare experienced mitigated HTTP DDoS attacks.

Cloudflare mitigates record-breaking 71 million request-per-second DDoS attack (✍️)

Also in early February, Cloudflare detected and mitigated dozens of hyper-volumetric DDoS attacks, one of those that became a record-breaking one. The majority of attacks peaked in the ballpark of 50-70 million requests per second (rps) with the largest exceeding 71Mrps. This was the largest reported HTTP DDoS attack on record to date, more than 54% higher than the previous reported record of 46M rps in June 2022.

SLP: a new DDoS amplification vector in the wild (✍️)

This blog post from April 2023 highlights how researchers have published the discovery of a new DDoS reflection/amplification attack vector leveraging the SLP protocol (Service Location Protocol). The prevalence of SLP-based DDoS attacks is also expected to rise, but our automated DDoS protection system keeps Cloudflare customers safe.

Additionally, this year, also in April, a new and improved Network Analytics dashboard was introduced, providing security professionals insights into their DDoS attack and traffic landscape.

2. Application level attacks & WAF

The state of application security in 2023 (✍️)

For the second year in a row we published our Application Security Report. There’s a lot to unpack here, in a year when, according to Netcraft, Cloudflare became the most commonly used web server vendor within the top million sites (it has now a 22% market share). Here are some highlights:

  • 6% of daily HTTP requests (proxied by the Cloudflare network) are mitigated on average. It’s down two percentage points compared to last year.
  • DDoS mitigation accounts for more than 50% of all mitigated traffic, so it’s still the largest contributor to mitigated layer 7 (application layer) HTTP requests.
  • Compared to last year, however, mitigation by the Cloudflare WAF (Web Application Firewall) has grown significantly, and now accounts for nearly 41% of mitigated requests.
  • HTTP Anomaly (examples include malformed method names, null byte characters in headers, etc.) is the most frequent layer 7 attack vectors mitigated by the WAF.
  • 30% of HTTP traffic is automated (bot traffic). 55% of dynamic (non cacheable) traffic is API related. 65% of global API traffic is generated by browsers.
  • 16% of non-verified bot HTTP traffic is mitigated.
  • HTTP Anomaly surpasses SQLi (code injection technique used to attack data-driven applications) as the most common attack vector on API endpoints. Brute force account takeover attacks are increasing. Also, Microsoft Exchange is attacked more than WordPress.

How Cloudflare can help stop malware before it reaches your app (✍️)

In April 2023, we made the job of application security teams easier, by providing a content scanning engine integrated with our Web Application Firewall (WAF), so that malicious files being uploaded by end users, never reach origin servers in the first place. Since September 2022, our Cloudflare WAF became smarter in helping stop attacks before they are known.

Announcing WAF Attack Score Lite and Security Analytics for business customers  (✍️)

In March 2023, we announced that our machine learning empowered WAF and Security analytics view were made available to our Business plan customers, to help detect and stop attacks before they are known. In a nutshell: Early detection + Powerful mitigation = Safer Internet. Or:

early_detection = True
powerful_mitigation = True
safer_internet = early_detection and powerful_mitigation

An August reading list about online security and 2023 attacks landscape

3. Phishing (Area 1 and Zero Trust)

Phishing remains the primary way to breach organizations. According to CISA, 90% of cyber attacks begin with it. The FBI has been publishing Internet Crime Reports, and in the most recent, phishing continues to be ranked #1 in the top five Internet crime types. Reported phishing crimes and victim losses increased by 1038% since 2018, reaching 300,497 incidents in 2022. The FBI also referred to Business Email Compromise as the $43 billion problem facing organizations, with complaints increasing by 127% in 2022, resulting in $3.31 billion in related losses, compared to 2021.

In 2022, Cloudflare Area 1 kept 2.3 billion unwanted messages out of customer inboxes. This year, that number will be easily surpassed.

Introducing Cloudflare's 2023 phishing threats report (✍️)

In August 2023, Cloudflare published its first phishing threats report — fully available here. The report explores key phishing trends and related recommendations, based on email security data from May 2022 to May 2023.

Some takeaways include how attackers using deceptive links was the #1 phishing tactic — and how they are evolving how they get you to click and when they weaponize the link. Also, identity deception takes multiple forms (including business email compromise (BEC) and brand impersonation), and can easily bypass email authentication standards.

Cloudflare Area 1 earns SOC 2 report (✍️)

More than one year ago, Cloudflare acquired Area 1 Security, and with that we added to our Cloudflare Zero Trust platform an essential cloud-native email security service that identifies and blocks attacks before they hit user inboxes. This year, we’ve obtained one of the best ways to provide customers assurance that the sensitive information they send to us can be kept safe: a SOC 2 Type II report.

Back in January, during our CIO Week, Email Link Isolation was made generally available to all our customers. What is it? A safety net for the suspicious links that end up in inboxes and that users may click — anyone can click on the wrong link by mistake. This added protection turns Cloudflare Area 1 into the most comprehensive email security solution when it comes to protecting against malware, phishing attacks, etc. Also, in true Cloudflare fashion, it’s a one-click deployment.

Additionally, from the same week, Cloudflare combined capabilities from Area 1 Email Security and Data Loss Prevention (DLP) to provide complete data protection for corporate email, and also partnered with KnowBe4 to equip organizations with real-time security coaching to avoid phishing attacks.

How to stay safe from phishing (✍️)

Phishing attacks come in all sorts of ways to fool people. This high level “phish” guide, goes over the different types — while email is definitely the most common, there are others —, and provides some tips to help you catch these scams before you fall for them.

Top 50 most impersonated brands in phishing attacks and new tools you can use to protect your employees from them (✍️)

Here we go over arguably one of the hardest challenges any security team is constantly facing, detecting, blocking, and mitigating the risks of phishing attacks. During our Security Week in March, a Top 50 list of the most impersonated brands in phishing attacks was presented (spoiler alert: AT&T Inc., PayPal, and Microsoft are on the podium).

Additionally, it was also announced the expansion of the phishing protections available to Cloudflare One customers by automatically identifying — and blocking — so-called “confusable” domains. What is Cloudflare One? It’s our suite of products that provides a customizable, and integrated with what a company already uses, Zero Trust network-as-a-service platform. It’s built for that already mentioned ease of mind and fearless online use. Cloudflare One, along with the use of physical security keys, was what thwarted the sophisticated “Oktapus” phishing attack targeting Cloudflare employees last summer.

On the Zero Trust front, you can also find our recent PDF guide titled “Cloudflare Zero Trust: A roadmap for highrisk organizations”.

An August reading list about online security and 2023 attacks landscape

4. AI/Malware/Ransomware & other risks

We have shown in previous years the role of our Cloudflare Security Center to investigate threats, and the relevance of different types of risks, such as these two 2022 and 2021 examples: “Anatomy of a Targeted Ransomware Attack” and “Ransom DDoS attacks target a Fortune Global 500 company”. However, there are new risks in the 2023 horizon.

How to secure Generative AI applications (✍️)

Groundbreaking technology brings groundbreaking challenges. Cloudflare has experience protecting some of the largest AI applications in the world, and in this blog post there are some tips and best practices for securing generative AI applications. Success in consumer-facing applications inherently expose the underlying AI systems to millions of users, vastly increasing the potential attack surface.

Using the power of Cloudflare’s global network to detect malicious domains using machine learning  (✍️)

Taking into account the objective of preventing threats before they create havoc, here we go over that Cloudflare recently developed proprietary models leveraging machine learning and other advanced analytical techniques. These are able to detect security threats that take advantage of the domain name system (DNS), known as the phonebook of the Internet.

How sophisticated scammers and phishers are preying on customers of Silicon Valley Bank (✍️)

In order to breach trust and trick unsuspecting victims, threat actors overwhelmingly use topical events as lures. The news about what happened at Silicon Valley Bank earlier this year was one of the latest events to watch out for and stay vigilant against opportunistic phishing campaigns using SVB as the lure. At that time, Cloudforce One (Cloudflare’s threat operations and research team) significantly increased our brand monitoring focused on SVB’s digital presence.

How Cloudflare can help stop malware before it reaches your app (✍️)

In April 2023, Cloudflare launched a tool to make the job of application security teams easier, by providing a content scanning engine integrated with our Web Application Firewall (WAF), so that malicious files being uploaded by end users, never reach origin servers in the first place.

Analyze any URL safely using the Cloudflare Radar URL Scanner  (✍️)

Cloudflare Radar is our free platform for Internet insights. In March, our URL Scanner was launched, allowing anyone to analyze a URL safely. The report that it creates contains a myriad of technical details, including a phishing scan. Many users have been using it for security reasons, but others are just exploring what’s under-the-hood look at any webpage.

Unmasking the top exploited vulnerabilities of 2022 (✍️)

Last, but not least, already from August 2023, this blog post focuses on the most commonly exploited vulnerabilities, according to the Cybersecurity and Infrastructure Security Agency (CISA). Given Cloudflare’s role as a reverse proxy to a large portion of the Internet, we delve into how the Common Vulnerabilities and Exposures (CVEs) mentioned by CISA are being exploited on the Internet, and a bit of what has been learned.

If you want to learn about making a website more secure (and faster) while loading third-party tools like Google Analytics 4, Facebook CAPI, TikTok, and others, you can get to know our Cloudflare Zaraz solution. It reached general availability in July 2023.

Wrap up

“The Internet was not built for what it has become”.

This is how one of Cloudflare’s S-1 document sections begins. It is also commonly referenced in our blog to show how this remarkable experiment, the network of networks, wasn’t designed for the role it now plays in our daily lives and work. Security, performance and privacy are crucial in a time when anyone can be the target of an attack, threat, or vulnerability. While AI can aid in mitigating attacks, it also adds complexity to attackers' tactics.

With that in mind, as we've highlighted in this 2023 reading list suggestions/online attacks guide, prioritizing the prevention of detrimental attack outcomes remains the optimal strategy. Hopefully, it will make some of the attacks on your company go unnoticed or be consequences-free, or even transform them into interesting stories to share when you access your security dashboard.

If you're interested in exploring specific examples, you can delve into case studies within our hub, where you’ll find security related stories from different institutions. From a technology company like Sage, to the State of Arizona, or the Republic of Estonia Information Security Authority, and even Cybernews, a cybersecurity news media outlet.

And because the future of a private and secure Internet is also in our minds, it's worth mentioning that in March 2022, Cloudflare enabled post-quantum cryptography support for all our customers. The topic of post-quantum cryptography, designed to be secure against the threat of quantum computers, is quite interesting and worth some delving into, but even without knowing what it is, it’s good to know that protection is already here.

If you want to try some security features mentioned, the Cloudflare Security Center is a good place to start (free plans included). The same applies to our Zero Trust ecosystem (or Cloudflare One as our SASE, Secure Access Service Edge) that is available as self-serve, and also includes a free plan. This vendor-agnostic roadmap shows the general advantages of the Zero Trust architecture, and as we’ve seen, there’s also one focused on high risk organizations.

Be cautious. Be prepared. Be safe.

How AWS built the Security Guardians program, a mechanism to distribute security ownership

Post Syndicated from Ana Malhotra original https://aws.amazon.com/blogs/security/how-aws-built-the-security-guardians-program-a-mechanism-to-distribute-security-ownership/

Product security teams play a critical role to help ensure that new services, products, and features are built and shipped securely to customers. However, since security teams are in the product launch path, they can form a bottleneck if organizations struggle to scale their security teams to support their growing product development teams. In this post, we will share how Amazon Web Services (AWS) developed a mechanism to scale security processes and expertise by distributing security ownership between security teams and development teams. This mechanism has many names in the industry — Security Champions, Security Advocates, and others — and it’s often part of a shift-left approach to security. At AWS, we call this mechanism Security Guardians.

In many organizations, there are fewer security professionals than product developers. Our experience is that it takes much more time to hire a security professional than other technical job roles, and research conducted by (ISC)2 shows that the cybersecurity industry is short 3.4 million workers. When product development teams continue to grow at a faster rate than security teams, the disparity between security professionals and product developers continues to increase as well. Although most businesses understand the importance of security, frustration and tensions can arise when it becomes a bottleneck for the business and its ability to serve customers.

At AWS, we require the teams that build products to undergo an independent security review with an AWS application security engineer before launching. This is a mechanism to verify that new services, features, solutions, vendor applications, and hardware meet our high security bar. This intensive process impacts how quickly product teams can ship to customers. As shown in Figure 1, we found that as the product teams scaled, so did the problem: there were more products being built than the security teams could review and approve for launch. Because security reviews are required and non-negotiable, this could potentially lead to delays in the shipping of products and features.

Figure 1: More products are being developed than can be reviewed and shipped

Figure 1: More products are being developed than can be reviewed and shipped

How AWS builds a culture of security

Because of its size and scale, many customers look to AWS to understand how we scale our own security teams. To tell our story and provide insight, let’s take a look at the culture of security at AWS.

Security is a business priority

At AWS, security is a business priority. Business leaders prioritize building products and services that are designed to be secure, and they consider security to be an enabler of the business rather than an obstacle.

Leaders also strive to create a safe environment by encouraging employees to identify and escalate potential security issues. Escalation is the process of making sure that the right people know about the problem at the right time. Escalation encompasses “Dive Deep”, which is one of our corporate values at Amazon, because it requires owners and leaders to dive into the details of the issue. If you don’t know the details, you can’t make good decisions about what’s going on and how to run your business effectively.

This aspect of the culture goes beyond intention — it’s embedded in our organizational structure:

CISOs and IT leaders play a key role in demystifying what security and compliance represent for the business. At AWS, we made an intentional choice for the security team to report directly to the CEO. The goal was to build security into the structural fabric of how AWS makes decisions, and every week our security team spends time with AWS leadership to ensure we’re making the right choices on tactical and strategic security issues.

– Stephen Schmidt, Chief Security Officer, Amazon, on Building a Culture of Security

Everyone owns security

Because our leadership supports security, it’s understood within AWS that security is everyone’s job. Security teams and product development teams work together to help ensure that products are built and shipped securely. Despite this collaboration, the product teams own the security of their product. They are responsible for making sure that security controls are built into the product and that customers have the tools they need to use the product securely.

On the other hand, central security teams are responsible for helping developers to build securely and verifying that security requirements are met before launch. They provide guidance to help developers understand what security controls to build, provide tools to make it simpler for developers to implement and test controls, provide support in threat modeling activities, use mechanisms to help ensure that customers’ security expectations are met before launch, and so on.

This responsibility model highlights how security ownership is distributed between the security and product development teams. At AWS, we learned that without this distribution, security doesn’t scale. Regardless of the number of security experts we hire, product teams always grow faster. Although the culture around security and the need to distribute ownership is now well understood, without the right mechanisms in place, this model would have collapsed.

Mechanisms compared to good intentions

Mechanisms are the final pillar of AWS culture that has allowed us to successfully distribute security across our organization. A mechanism is a complete process, or virtuous cycle, that reinforces and improves itself as it operates. As shown in Figure 2, a mechanism takes controllable inputs and transforms them into ongoing outputs to address a recurring business challenge. At AWS, the business challenge that we’re facing is that security teams create bottlenecks for the business. The culture of security at AWS provides support to help address this challenge, but we needed a mechanism to actually do it.

Figure 2: AWS sees mechanisms as a complete process, or virtuous cycle

Figure 2: AWS sees mechanisms as a complete process, or virtuous cycle

“Often, when we find a recurring problem, something that happens over and over again, we pull the team together, ask them to try harder, do better – essentially, we ask for good intentions. This rarely works… When you are asking for good intentions, you are not asking for a change… because people already had good intentions. But if good intentions don’t work, what does? Mechanisms work.

 – Jeff Bezos, February 1, 2008 All Hands.

At AWS, we’ve learned that we can help solve the challenge of scaling security by distributing security ownership with a mechanism we call the Security Guardians program. Like other mechanisms, it has inputs and outputs, and transforms over time.

AWS distributes security ownership with the Security Guardians program

At AWS, the Security Guardians program trains, develops, and empowers developers to be security ambassadors, or Guardians, within the product teams. At a high level, Guardians make sure that security considerations for a product are made earlier and more often, helping their peers build and ship their product faster. They also work closely with the central security team to help ensure that the security bar at AWS is rising and the Security Guardians program is improving over time. As shown in Figure 3, embedding security expertise within the product teams helps products with Guardian involvement move through security review faster.

Figure 3: Security expertise is embedded in the product teams by Guardians

Figure 3: Security expertise is embedded in the product teams by Guardians

Guardians are informed, security-minded product builders who volunteer to be consistent champions of security on their teams and are deeply familiar with the security processes and tools. They provide security guidance throughout the development lifecycle and are stakeholders in the security of the products being shipped, helping their teams make informed decisions that lead to more secure, on-time launches. Guardians are the security points-of-contact for their product teams.

In this distributed security ownership model, accountability for product security sits with the product development teams. However, the Guardians are responsible for performing the first evaluation of a development team’s security review submission. They confirm the quality and completeness of the new service’s resources, design documents, threat model, automated findings, and penetration test readiness. The development teams, supported by the Guardian, submit their security review to AWS Application Security (AppSec) engineers for the final pre-launch review.

In practice, as part of this development journey, Guardians help ensure that security considerations are made early, when teams are assessing customer requests and the feature or product design. This can be done by starting the threat modeling processes. Next, they work to make sure that mitigations identified during threat modeling are developed. Guardians also play an active role in software testing, including security scans such as static application security testing (SAST) and dynamic application security testing (DAST). To close out the security review, security engineers work with Guardians to make sure that findings are resolved and the product is ready to ship.

Figure 4: Expedited security review process supported by Guardians

Figure 4: Expedited security review process supported by Guardians

Guardians are, after all, Amazonians. Therefore, Guardians exemplify a number of the Amazon Leadership Principles and often have the following characteristics:

  • They are exemplary practitioners for security ownership and empower their teams to own the security of their service.
  • They hold a high security bar and exercise strong security judgement, don’t accept quick or easy answers, and drive continuous improvement.
  • They advocate for security needs in internal discussions with the product team.
  • They are thoughtful yet assertive to make customer security a top priority on their team.
  • They maintain and showcase their security knowledge to their peers, continuously building knowledge from many different sources to gain perspective and to stay up to date on the constantly evolving threat landscape.
  • They aren’t afraid to have their work independently validated by the central security team.

Expected outcomes

AWS has benefited greatly from the Security Guardians program. We’ve had 22.5 percent fewer medium and high severity security findings generated during the security review process and have taken about 26.9 percent less time to review a new service or feature. This data demonstrates that with Guardians involved we’re identifying fewer issues late in the process, reducing remediation work, and as a result securely shipping services faster for our customers. To help both builders and Guardians improve over time, our security review tool captures feedback from security engineers on their inputs. This helps ensure that our security ownership mechanism reinforces and improves itself over time.

AWS and other organizations have benefited from this mechanism because it generates specialized security resources and distributes security knowledge that scales without needing to hire additional staff.

A program such as this could help your business build and ship faster, as it has for AWS, while maintaining an appropriately high security bar that rises over time. By training builders to be security practitioners and advocates within your development cycle, you can increase the chances of identifying risks and security findings earlier. These findings, earlier in the development lifecycle, can reduce the likelihood of having to patch security bugs or even start over after the product has already been built. We also believe that a consistent security experience for your product teams is an important aspect of successfully distributing your security ownership. An experience with less confusion and friction will help build trust between the product and security teams.

To learn more about building positive security culture for your business, watch this spotlight interview with Stephen Schmidt, Chief Security Officer, Amazon.

If you’re an AWS customer and want to learn more about how AWS built the Security Guardians program, reach out to your local AWS solutions architect or account manager for more information.

 
If you have feedback about this post, submit comments in the Comments section below. If you have questions about this post, contact AWS Support.

Want more AWS Security news? Follow us on Twitter.

Ana Malhotra

Ana Malhotra

Ana is a Security Specialist Solutions Architect and the Healthcare and Life Sciences (HCLS) Security Lead for AWS Industry, based in Seattle, Washington. As a former AWS Application Security Engineer, Ana loves talking all things AppSec, including people, process, and technology. In her free time, she enjoys tapping into her creative side with music and dance.

Mitch Beaumont

Mitch Beaumont

Mitch is a Principal Solutions Architect for Amazon Web Services, based in Sydney, Australia. Mitch works with some of Australia’s largest financial services customers, helping them to continually raise the security bar for the products and features that they build and ship. Outside of work, Mitch enjoys spending time with his family, photography, and surfing.

mTLS: When certificate authentication is done wrong

Post Syndicated from Michael Stepankin original https://github.blog/2023-08-17-mtls-when-certificate-authentication-is-done-wrong/

Although X.509 certificates have been here for a while, they have become more popular for client authentication in zero-trust networks in recent years. Mutual TLS, or authentication based on X.509 certificates in general, brings advantages compared to passwords or tokens, but you get increased complexity in return.

In this post, I’ll deep dive into some interesting attacks on mTLS authentication. We won’t bother you with heavy crypto stuff, but instead we’ll have a look at implementation vulnerabilities and how developers can make their mTLS systems vulnerable to user impersonation, privilege escalation, and information leakages.

We will present some CVEs we found in popular open-source identity servers and ways to exploit them. Finally, we’ll explain how these vulnerabilities can be spotted in source code and how to fix them.

This blog post is based on work that I recently presented at Black Hat USA and DEF CON.

Introduction: What is mutual TLS?

Website certificates are a very widely recognized technology, even to people who don’t work in the tech industry, thanks to the padlock icon used by web browsers. Whenever we connect to Gmail or GitHub, our browser checks the certificate provided by the server to make sure it’s truly the service we want to talk to. Fewer people know that the same technology can be used to authenticate clients: the TLS protocol is also designed to be able to verify the client using public and private key cryptography.

It happens on the handshake level, even before any application data is transmitted:

Excerpt from RFC 5246: "Figure 1. Message flow for a full handshake"

If configured to do so, a server can ask a client to provide a security certificate in the X.509 format. This certificate is just a blob of binary data that contain information about the client, such as its name, public key, issuer, and other fields:

$ openssl x509 -text -in client.crt
Certificate:
    Data:
        Version: 1 (0x0)
        Serial Number:
            d6:2a:25:e3:89:22:4d:1b
    Signature Algorithm: sha256WithRSAEncryption
        Issuer: CN=localhost            //used to locate issuers certificate
        Validity
            Not Before: Jun 13 14:34:28 2023 GMT
            Not After : Jul 13 14:34:28 2023 GMT
        Subject: CN=client          //aka "user name"
        Subject Public Key Info:
            Public Key Algorithm: rsaEncryption
                RSA Public-Key: (2048 bit)
                Modulus:
                    00:9c:7c:b4:e5:e9:3d:c1:70:9c:9d:18:2f:e8:a0:

The server checks that this certificate is signed by one of the trusted authorities. This is a bit similar to checking the signature of a JWT token. Next, the client sends a “Certificate verify” message encrypted with the private key, so that the server can verify that the client actually has the private key.

How certificates are validated

“Certificate validation” commonly refers to the PKIX certificate validation process defined in RFC 5280.

In short, in order to validate the certificate, the server constructs a certification path (also known as a certificate chain) from the target certificate to a trust anchor. The trust anchor is a self-signed root certificate that is inherently trusted by the validator. The end entity certificate is often signed by an intermediate CA, which is also signed by another intermediate certificate or directly by a trust anchor.

Diagram of certificate chain with three links: Client certificate, Intermediate CA, Root Certificate Authority

Then, for each certificate in the chain, the validator checks the signature, validity period, allowed algorithms and key lengths, key usage, and other properties. There are also a number of optional certificate extensions: if they are included in the certificate, they can be checked as well. This process is quite complicated, so every language or library implements it differently.

Note: in my research I mostly looked at how mTLS is implemented in applications written in Java, but it is likely that the ideas and attacks below apply to other languages as well.

mTLS in a Java web application, an example

Let’s see how to use mTLS in a Java web application. The bare minimum configuration is to enable it in the application settings and specify the location of all trusted root certificates, like this:

$ cat application.properties

…
server.ssl.client-auth=need
server.ssl.trust-store=/etc/spring/server-truststore.p12
server.ssl.trust-store-password=changeit

From the client, such as curl, you need to specify which certificate is sent to the server. The rest of the application code, such as request mappings, is exactly the same as for a normal web application.

$ curl -k -v –cert client.pem http://localhost/hello

This setup works for very simple mTLS configurations, when there is only a single root certificate, and all client certificates are signed by it. You can find this example in various articles on the web and it’s quite secure due to its simplicity. Let’s quickly break down its pros and cons.

Pros:

  • Speed: Authorization happens only during TLS handshake, all subsequent “keep-alive” HTTP requests are considered authenticated, saving CPU time.
  • Storage: Similar to JWT, the server does not store all client certificates, only the root certificate.

Cons:

  • No granular control: if mTLS is enabled, all requests have to be authenticated, even to /static/style.css.
  • Any certificate signed by a trusted CA can be used to access this HTTP service. Even if the certificate is issued for another purpose, it still can potentially be used for TLS authentication.
  • No host verification by default: client certificates can be accepted from any IP.
  • Certificate issuance process needs to be implemented separately.
  • Certificates expire, so need to be rotated frequently.

As you can see, this approach brings some advantages and disadvantages compared to traditional authentication methods, such as password or tokens.

Previous attacks

Before we dive into the attack section, I’ll briefly mention some previous well-known attacks on certificate parsing and validation:

  • Obviously, the security of the authentication system depends on the strength of the signature. If we can somehow forge the content of the certificate, but keep the same signature, we can completely break the authentication process.
  • Since the X.509 format is quite complex, just parsing these data structures can lead to buffer and heap overflows.
  • Lack of basic constraints checking. The end-entity certificates should not be used to sign additional certificates.

My approach

In Java, most of these attacks are already mitigated in APIs provided by the JDK. Weak algorithms are intentionally not allowed. Fuzzing of certificate parsing in Java also did not look productive to me, as the vast majority of PKIX code is implemented in memory-safe Java, instead of using native libraries. I had to take a different approach, so I decided to have a deep look at how mTLS is used from the source code perspective. Since the certificate validation process is quite complex, I suspected that someone might implement it in a weird way. After several weeks, it yielded me some interesting vulnerabilities in popular open source projects.

So, let’s move on to the attack’s section.

Chapter 1: Improper certificate extraction

In real-life applications, developers often need to access the certificate presented during the TLS handshake. For example, they might need it for authorization purposes, such as checking the current username. In Java, there are two common ways how to access it:

X509Certificate[] certificates = sslSession.getPeerCertificates();  

// another way
X509Certificate[] certificates = request.getAttribute("javax.servlet.request.X509Certificate");

Interestingly, this API returns an array of certificates presented by the client, not a single one. Why? Perhaps because TLS specification defines that clients may send a full chain of certificates, from end-entity to the root CA.

So, I decided to take a look at how different applications use this API. The most common approach I’ve seen is to take only the first certificate from the array and consider it as the client certificate. This is correct, as mTLS RFC explicitly says that the sender’s certificate MUST come first in the list.

//way 1 is good
String user = certificates[0].getSubjectX500Principal().getName();

At the same time, I discovered some rare cases when applications disregard this rule and iterate over the array trying to find a certificate that matches some criteria.

//way 2 is dangerous
for (X509Certificate cert : certificates) {
   if (isClientCertificate(cert)) {
      user = cert.getSubjectX500Principal().getName();
   }
}

This is dangerous, as the underlying TLS library in Java only verifies the first certificate in the list. Moreover, it does not require the chain to be sent in a strict order.

Example: CVE-2023-2422 improper certificate validation in KeyCloak

One of these examples was a vulnerability I discovered in Keycloak. Keycloak is a popular authorization server that supports OAuth, SAML, and other authorization methods, as well as mutual TLS.

Keycloak iterates over all certificates in the array, searching for the one that matches the client_id form parameter. As soon as it finds a matching certificate, it implicitly trusts it, assuming that its signature has already been checked during the TLS handshake:

X509Certificate[] certs = null;
ClientModel client = null;
try { 
    certs = provider.getCertificateChain(context.getHttpRequest());
    String client_id = null;
    ...
    if (formData != null) {
        client_id = formData.getFirst(OAuth2Constants.CLIENT_ID);
    }
    …
    matchedCertificate = Arrays.stream(certs)
        .map(certificate -> certificate.getSubjectDN().getName())
        .filter(subjectdn -> subjectDNPattern.matcher(subjectdn).matches())
        .findFirst();

In reality, a client can send as many certificates as they want, and the server only verifies the first one.

A potential attacker can exploit this behavior to authenticate under a different username. It is possible to send a list of certificates, where the first one contains one username and is properly chained to a root CA. But the last certificate in the array might be self signed and belong to a different user. The client does not even need to provide a valid private key for it.

Diagram of a certificate list in which the first client certificate is signed by a CA, but the second is self-signed.

Speaking about the exploitation, there are a number of endpoints in Keycloak that support mTLS authentication, but we need one that does not require any additional factors, such as tokens or secrets. “client-management/register-node” is a good example, as it mutates the user’s data. We can normally use this api with mTLS in the following way:

$ cat client1.crt client1.key > chain1.pem
$ curl --tlsv1.2 --tls-max 1.2 --cert chain1.pem -v -i -s -k "https://127.0.0.1:8443/realms/master/clients-managements/register-node?client_id=client1" -d "client_cluster_host=http://127.0.0.1:1213/"

To demonstrate the vulnerability, we generate a new self signed certificate using openssl and add it to the end of the array.

$ openssl req -newkey rsa:2048 -nodes -x509 -subj /CN=client2 -out client2-fake.crt
$ cat client1.crt client1.key client2-fake.crt client1.key > chain2.pem
$ curl --tlsv1.2 --tls-max 1.2 --cert chain2.pem -v -i -s -k "https://127.0.0.1:8443/realms/master/clients-managements/register-node?client_id=client2" -d "client_cluster_host=http://127.0.0.1:1213/"

When we send the second curl request, Keycloak performs this action on behalf of the user specified in client2-fake.crt, instead of client1.crt. Therefore, we can mutate data on the server for any client that supports mTLS.

How to fix that? Easy: just use the first certificate from the array. That’s exactly how Keycloak patched this vulnerability. This CVE is a good example of how developers provide methods and interfaces that can be misunderstood or used incorrectly.

Passing certificate as a header

Another common scenario for mTLS deployments is when the TLS connection is terminated on a reverse proxy. In this case, the reverse proxy often checks the certificate and forwards it to a backend server as an additional header. Here is a typical nginx configuration to enable mTLS:

$ cat nginx.conf

http {
    server {
        server_name example.com;
        listen 443 ssl;
        …
        ssl_client_certificate /etc/nginx/ca.pem;
        ssl_verify_client on;

        location / {
            proxy_pass http://host.internal:80;
            proxy_set_header ssl-client-cert $ssl_client_cert;
        }
    }

I’ve seen a number of systems like that, and in most cases the backend servers behind nginx do not perform additional validation, just trusting the reverse proxy. This behavior is not directly exploitable, but it’s not ideal either. Why? Well, first of all, it means that any server in the local network can make a request with this header, so this network segment needs to be carefully isolated from any traffic coming from outside. Additionally, if the backend or reverse proxy is affected by request smuggling or header injection, its exploitation becomes trivial. Over the past few years, we’ve seen a lot of request and header smuggling vulnerabilities, including the latest CVEs in Netty and Nodejs. Be careful when implementing these scenarios and check the certificate’s signature on all servers if possible.

Chapter 2: “Follow the chain, where does it lead you?”

Excerpt from RFC 4158: "Figure 1 - Sample Hierarchical PKI"

In large systems, servers may not store all root and intermediate certificates locally, but use external storage instead. RFC 4387 explains the concept of a certificate store: an interface you can use to lazily access certificates during chain validation. These stores are implemented over different protocols, such as HTTP, LDAP, FTP, or SQL queries.

RFC 3280 defines some X.509 certificate extensions that can contain information about where to find the issuer and CA certificates. For instance, the Authority Information Access (AIA) extension contains a URL pointing to the Issuer’s certificate. If this extension is used for validation, there is a high chance that you can exploit it to perform an SSRF attack. Also, Subject, Issuer, Serial, and their alternative names can be used to construct SQL or LDAP queries, creating opportunities for injection attacks.

Client certificate with an AIA extension, containing a link to http://example.com

When certificate stores are in use, you should think of these values as “untrusted user input” or “Insertion points,” similar to those we have in Burp Suite’s Intruder. And what attackers will really love is that all of these values can be used in queries before the signature is checked.

Example: CVE-2023-33201 LDAP injection in Bouncy Castle

To demonstrate an example of this vulnerability, we’ll use LDAPCertStore from the Bouncy Castle library. Bouncy Castle is one of the most popular libraries for certificate validation in Java. Here is an example of how you can use this store to build and validate a certificate chain.

PKIXBuilderParameters pkixParams = new PKIXBuilderParameters(keystore, selector);

//setup additional LDAP store
X509LDAPCertStoreParameters CertStoreParameters = new X509LDAPCertStoreParameters.Builder("ldap://127.0.0.1:1389", "CN=certificates").build();
CertStore certStore = CertStore.getInstance("LDAP", CertStoreParameters, "BC");
pkixParams.addCertStore(certStore);

// Build and verify the certification chain
try {
   CertPathBuilder builder = CertPathBuilder.getInstance("PKIX", "BC");
   PKIXCertPathBuilderResult result =
           (PKIXCertPathBuilderResult) builder.build(pkixParams);

Under the hood, Bouncy Castle uses the Subject field from the certificate to build an LDAP query. The Subject field is inserted in the filter, without—you guessed it—any escaping.

Client certificate containing the text "Subject: CN=Client*)(userPassword=123"

So, if the Subject contains any special characters, it can change the syntax of the query. In most cases, this can be exploited as a blind ldap query injection. Therefore, it might be possible to use this vulnerability to extract other fields from the LDAP directory. The exploitability depends on many factors, including whether the application exposes any errors or not, and it also depends on the structure of the LDAP directory.

In general, whenever you incorporate user-supplied data into an LDAP query, special characters should be properly filtered. That’s exactly how this CVE has been patched in the Bouncy Castle code.

Chapter 3: Certificate revocation and its unintended uses

Similar to Json web tokens, the beauty of certificate chains is that they can be trusted just based on their signature. But what happens if we need to revoke a certificate, so it can no longer be used?

The PKIX specification (RFC 4387) addresses this problem by proposing a special store for revoked certificates, accessible via HTTP or LDAP protocols. Many developers believe that revocation checking is absolutely necessary, whereas others urge to avoid it for performance reasons or only use offline revocation lists.

Generally speaking, the store location can be hardcoded into the application or taken from the certificate itself. There are two certificate extensions used for that: Authority Information Access OSCP URL and CRL Distribution points.

Client certificate containing URLs in its AIA OSCL and CRL Distribution points.

Looking at it from the hackers point of view, I think it’s incredible that the location of the revocation server can be taken from the certificate. So, if the application takes URLs relying on AIA or CRLDP extension to make a revocation check, it can be abused for SSRF attacks.

Sadly for attackers, this normally happens after the signature checks, but in some cases it’s still exploitable.

Moreover, LDAP is also supported, at least in Java. You probably heard that, in Java, unmarshaling an LDAP lookup response can lead to a remote code execution. A few years back, Moritz Bechler reported this problem and remote code execution via revocation has since been patched in the JDK. You can check out his blog post for more details.

In my research, I decided to check if the Bouncy Castle library is also affected. It turns out that Bouncy Castle can be configured to use the CRLDP extension and make calls to an LDAP server. At the same time, Bouncy Castle only fetches a specific attribute from the LDAP response and does not support references. So, remote code execution is not possible there. HTTP SSRF is still viable though.

private static Collection getCrlsFromLDAP(CertificateFactory certFact, URI distributionPoint) throws IOException, CRLException
{
    Map<String, String> env = new Hashtable<String, String>();

    env.put(Context.INITIAL_CONTEXT_FACTORY, "com.sun.jndi.ldap.LdapCtxFactory");
    env.put(Context.PROVIDER_URL, distributionPoint.toString());

    byte[] val = null;
    try
    {
        DirContext ctx = new InitialDirContext((Hashtable)env);
        Attributes avals = ctx.getAttributes("");
        Attribute aval = avals.get("certificateRevocationList;binary");
        val = (byte[])aval.get();
    }

Example: CVE-2023-28857 credentials leak in Apereo CAS

I also had a quick look at open source projects that support mTLS and perform revocation checking. One of these projects was Apereo CAS. It’s another popular authentication server that is highly configurable. Administrators of Apereo CAS can enable the revocation check using an external LDAP server by specifying its address and password in the settings:

cas.authn.x509.crl-fetcher=ldap
cas.authn.x509.ldap.ldap-url=ldap://example.com:1389/
cas.authn.x509.ldap.bind-dn=admin
cas.authn.x509.ldap.bind-credential=s3cr3taaaaa

If these settings are applied, Apereo CAS performs the revocation check for the certificate, fetching the address from the certificate’s CRLDP extension.

/**
* Validate the X509Certificate received.
*
* @param cert the cert
* @throws GeneralSecurityException the general security exception
*/
private void validate(final X509Certificate cert) throws GeneralSecurityException {
   cert.checkValidity();
   this.revocationChecker.check(cert);

   val pathLength = cert.getBasicConstraints();
   if (pathLength < 0) {
       if (!isCertificateAllowed(cert)) {
           val msg = "Certificate subject does not match pattern " + this.regExSubjectDnPattern.pattern();
           LOGGER.error(msg);

I was afraid that this could lead to remote code execution, but it turns out that Apereo CAS uses a custom library for LDAP connection, which does not support external codebases or object factories needed for RCE.

When I tested this in Apereo CAS, I noticed one interesting behavior. The server prefers the LDAP URL located inside the certificate, instead of the one that is configured in settings. At the same time, Apereo CAS still sends the password from the settings. I quickly set up a testing environment and sent a self-signed certificate in the header. My self-signed certificate had a CRLDP extension with the LDAP URL pointing to a netcat listener. After sending this request to Apereo CAS, I received a request to my netcat listener with the username and password leaked.

Pair of screenshots: the first contains a POST request to Apereo CAS and the second is a terminal running netcat.

After reporting this vulnerability, the application developers issued a fix within just one day. They patched it by clearing the login and password used for LDAP connection if the URL is taken from the CRLDP. Therefore, the password leak is no longer possible. Nevertheless, I would say that using URLs from the CRLDP extension is still dangerous, as it broadens the attack surface.

Summary

If you’re developing an mTLS system or performing a security assessment, I suggest:

  1. Pay attention when extracting usernames from the mTLS chain, as the servers only verify the first certificate in the chain.
  2. Use Certificate Stores with caution, as it can lead to LDAP and SQL injections.
  3. Certificate revocation can lead to SSRF or even to RCE in the worst case. So, do the revocation check only after all other checks and do not rely on URLs taken from the certificate extensions.

The post mTLS: When certificate authentication is done wrong appeared first on The GitHub Blog.

Let’s Architect! Security in software architectures

Post Syndicated from Luca Mezzalira original https://aws.amazon.com/blogs/architecture/lets-architect-security-in-software-architectures/

Security is fundamental for each product and service you are building with. Whether you are working on the back-end or the data and machine learning components of a system, the solution should be securely built.

In 2022, we discussed security in our post Let’s Architect! Architecting for Security. Today, we take a closer look at general security practices for your cloud workloads to secure both networks and applications, with a mix of resources to show you how to architect for security using the services offered by Amazon Web Services (AWS).

In this edition of Let’s Architect!, we share some practices for protecting your workloads from the most common attacks, introduce the Zero Trust principle (you can learn how AWS itself is implementing it!), plus how to move to containers and/or alternative approaches for managing your secrets.

A deep dive on the current security threat landscape with AWS

This session from AWS re:Invent, security engineers guide you through the most common threat vectors and vulnerabilities that AWS customers faced in 2022. For each possible threat, you can learn how it’s implemented by attackers, the weaknesses attackers tend to leverage, and the solutions offered by AWS to avert these security issues. We describe this as fundamental architecting for security: this implies adopting suitable services to protect your workloads, as well as follow architectural practices for security.

Take me to this re:Invent 2022 session!

Statistics about common attacks and how they can be launched

Statistics about common attacks and how they can be launched

Zero Trust: Enough talk, let’s build better security

What is Zero Trust? It is a security model that produces higher security outcomes compared with the traditional network perimeter model.

How does Zero Trust work in practice, and how can you start adopting it? This AWS re:Invent 2022 session defines the Zero Trust models and explains how to implement one. You can learn how it is used within AWS, as well as how any architecture can be built with these pillars in mind. Furthermore, there is a practical use case to show you how Delphix put Zero Trust into production.

Take me to this re:Invent 2022 session!

AWS implements the Zero Trust principle for managing interactions across different services

AWS implements the Zero Trust principle for managing interactions across different services

A deep dive into container security on AWS

Nowadays, it’s vital to have a thorough understanding of a container’s underlying security layers. AWS services, like Amazon Elastic Kubernetes Service and Amazon Elastic Container Service, have harnessed these Linux security-layer protections, keeping a sharp focus on the principle of least privilege. This approach significantly minimizes the potential attack surface by limiting the permissions and privileges of processes, thus upholding the integrity of the system.

This re:Inforce 2023 session discusses best practices for securing containers for your distributed systems.

Take me to this re:Inforce 2023 session!

Fundamentals and best practices to secure containers

Fundamentals and best practices to secure containers

Migrating your secrets to AWS Secrets Manager

Secrets play a critical role in providing access to confidential systems and resources. Ensuring the secure and consistent management of these secrets, however, presents a challenge for many organizations.

Anti-patterns observed in numerous organizational secrets management systems include sharing plaintext secrets via unsecured means, such as emails or messaging apps, which can allow application developers to view these secrets in plaintext or even neglect to rotate secrets regularly. This detailed guidance walks you through the steps of discovering and classifying secrets, plus explains the implementation and migration processes involved in transferring secrets to AWS Secrets Manager.

Take me to this AWS Security Blog post!

An organization's perspectives and responsibilities when building a secrets management solution

An organization’s perspectives and responsibilities when building a secrets management solution

Conclusion

We’re glad you joined our conversation on building secure architectures! Join us in a couple of weeks when we’ll talk about cost optimization on AWS.

To find all the blogs from this series, visit the Let’s Architect! list of content on the AWS Architecture Blog.

Introducing per hostname TLS settings — security fit to your needs

Post Syndicated from Dina Kozlov original http://blog.cloudflare.com/introducing-per-hostname-tls-settings/

Introducing per hostname TLS settings — security fit to your needs

Introducing per hostname TLS settings — security fit to your needs

One of the goals of Cloudflare is to give our customers the necessary knobs to enable security in a way that fits their needs. In the realm of SSL/TLS, we offer two key controls: setting the minimum TLS version, and restricting the list of supported cipher suites. Previously, these settings applied to the entire domain, resulting in an “all or nothing” effect. While having uniform settings across the entire domain is ideal for some users, it sometimes lacks the necessary granularity for those with diverse requirements across their subdomains.

It is for that reason that we’re excited to announce that as of today, customers will be able to set their TLS settings on a per-hostname basis.

The trade-off with using modern protocols

In an ideal world, every domain could be updated to use the most secure and modern protocols without any setbacks. Unfortunately, that's not the case. New standards and protocols require adoption in order to be effective. TLS 1.3 was standardized by the IETF in April 2018. It removed the vulnerable cryptographic algorithms that TLS 1.2 supported and provided a performance boost by requiring only one roundtrip, as opposed to two. For a user to benefit from TLS 1.3, they need their browser or device to support the new TLS version. For modern browsers and devices, this isn’t a problem – these operating systems are built to dynamically update to support new protocols. But legacy clients and devices were, obviously, not built with the same mindset. Before 2015, new protocols and standards were developed over decades, not months or years, so the clients were shipped out with support for one standard — the one that was used at the time.

If we look at Cloudflare Radar, we can see that about 62.9% of traffic uses TLS 1.3. That’s quite significant for a protocol that was only standardized 5 years ago. But that also means that a significant portion of the Internet continues to use TLS 1.2 or lower.

The same trade-off applies for encryption algorithms. ECDSA was standardized in 2005, about 20 years after RSA. It offers a higher level of security than RSA and uses shorter key lengths, which adds a performance boost for every request. To use ECDSA, a domain owner needs to obtain and serve an ECDSA certificate and the connecting client needs to support cipher suites that use elliptical curve cryptography (ECC). While most publicly trusted certificate authorities now support ECDSA-based certificates, the slow rate of adoption has led many legacy systems to only support RSA, which means that restricting applications to only support ECC-based algorithms could prevent access from those that use older clients and devices.

Balancing the trade-offs

When it comes to security and accessibility, it’s important to find the right middle ground for your business.

To maintain brand, most companies deploy all of their assets under one domain. It’s common for the root domain (e.g. example.com) to be used as a marketing website to provide information about the company, its mission, and the products and services it offers. Then, under the same domain, you might have your company blog (e.g. blog.example.com), your management portal (e.g. dash.example.com), and your API gateway (e.g. api.example.com).

The marketing website and the blog are similar in that they’re static sites that don’t collect information from the accessing users. On the other hand, the management portal and API gateway collect and present sensitive data that needs to be protected.

When you’re thinking about which settings to deploy, you want to consider the data that’s exchanged and the user base. The marketing website and blog should be accessible to all users. You can set them up to support modern protocols for the clients that support them, but you don’t necessarily want to restrict access for users that are accessing these pages from old devices.

The management portal and API gateway should be set up in a manner that provides the best protection for the data exchanged. That means dropping support for less secure standards with known vulnerabilities and requiring new, secure protocols to be used.

To be able to achieve this setup, you need to be able to configure settings for every subdomain within your domain individually.

Per hostname TLS settings – now available!

Customers that use Cloudflare’s Advanced Certificate Manager can configure TLS settings on individual hostnames within a domain. Customers can use this to enable HTTP/2, or to configure the minimum TLS version and the supported ciphers suites on a particular hostname. Any settings that are applied on a specific hostname will supersede the zone level setting. The new capability also allows you to have different settings on a hostname and its wildcard record; which means you can configure example.com to use one setting, and *.example.com to use another.

Let’s say that you want the default min TLS version for your domain to be TLS 1.2, but for your dashboard and API subdomains, you want to set the minimum TLS version to be TLS 1.3. In the Cloudflare dashboard, you can set the zone level minimum TLS version to 1.2 as shown below. Then, to make the minimum TLS version for the dashboard and API subdomains TLS 1.3, make a call to the per-hostname TLS settings API endpoint with the specific hostname and setting.

Introducing per hostname TLS settings — security fit to your needs

This is all available, starting today, through the API endpoint! And if you’d like to learn more about how to use our per-hostname TLS settings, please jump on over to our developer documentation.

Unmasking the top exploited vulnerabilities of 2022

Post Syndicated from Himanshu Anand original http://blog.cloudflare.com/unmasking-the-top-exploited-vulnerabilities-of-2022/

Unmasking the top exploited vulnerabilities of 2022

Unmasking the top exploited vulnerabilities of 2022

The Cybersecurity and Infrastructure Security Agency (CISA) just released a report highlighting the most commonly exploited vulnerabilities of 2022. With our role as a reverse proxy to a large portion of the Internet, Cloudflare is in a unique position to observe how the Common Vulnerabilities and Exposures (CVEs) mentioned by CISA are being exploited on the Internet.

We wanted to share a bit of what we’ve learned.

Based on our analysis, two CVEs mentioned in the CISA report are responsible for the vast majority of attack traffic seen in the wild: Log4J and Atlassian Confluence Code Injection. Although CISA/CSA discuss a larger number of vulnerabilities in the same report, our data clearly suggests a major difference in exploit volume between the top two and the rest of the list.

The top CVEs for 2022

Looking at the volume of requests detected by WAF Managed Rules that were created for the specific CVEs listed in the CISA report, we rank the vulnerabilities in order of prevalence:

Popularity rank

Description

CVEs

1. Improper Input Validation caused Remote Code execution in Apache Log4j logging library

Log4J

CVE-2021-44228

2. Atlassian Confluence Server and Data Center Remote Code Execution Vulnerability

Atlassian Confluence Code Injection

CVE-2022-26134

3. 3 issues were combined together to achieve Remote Code execution also known as ProxyShell

Microsoft Exchange servers

CVE-2021-34473, CVE-2021-31207, CVE-2021-34523

4. undisclosed requests may bypass iControl REST authentication and run arbitrary code

BIG-IP F5

CVE-2022-1388

5. 2 issues were combined to together to achieve remote Root

VMware

CVE-2022-22954

CVE-2022-22960

6. Remote Code execution Issue in Confluence Server and Data Center

Atlassian Confluence 0-day

CVE-2021-26084

Topping the list is Log4J (CVE-2021-44228). This isn’t surprising, as this is likely one of the most high impact exploits we have seen in decades — leading to full remote compromise. The second most exploited vulnerability is the Atlassian Confluence Code Injection (CVE-2022-26134).

In third place we find the combination of three CVEs targeting Microsoft Exchange servers (CVE-2021-34473, CVE-2021-31207, and CVE-2021-34523). In fourth is a BIG-IP F5 exploit (CVE-2022-1388) followed by the combination of two VMware vulnerabilities (CVE-2022-22954 and CVE-2022-22960). Our list ends with another Atlassian Confluence 0-day (CVE-2021-26084).

When comparing the attack volume for these five groups, we immediately notice that one vulnerability stands out. Log4J is more than an order of magnitude more exploited than the runner up (Atlassian Confluence Code Injection); and all the remaining CVEs are even lower. Although the CISA/CSA report groups all these vulnerabilities together, we think that there are really two groups: one dominant CVE (Log4J), and a secondary group of comparable 0-days. Each of the two groups have similar attack volume.

The chart below, in logarithmic scale, clearly shows the difference in popularity.

Unmasking the top exploited vulnerabilities of 2022

CVE-2021-44228: Log4J

The first on our list is the notorious CVE-2021-44228 — better known as the Log4j vulnerability. This flaw caused significant disturbance in the cyber world in 2021, and continues to be exploited extensively.

Cloudflare released new managed rules within hours after the vulnerability was made public. We also released updated detections in the following days (blog). Overall, we released rules in three stages:

The rules we deployed detect the exploit in four categories:

  • Log4j Headers: Attack pattern in HTTP header
  • Log4j Body: Attack pattern in HTTP Body
  • Log4j URI: Attack Pattern in URI
  • Log4j Body Obfuscation: Obfuscated Attack pattern

We have found that Log4J attacks in HTTP Headers are more common than in HTTP bodies. The graph below shows the persistence of exploit attempts for this vulnerability over time, with clear peaks and growth into July 2023 (time of writing).

Unmasking the top exploited vulnerabilities of 2022

Due to the high impact of this vulnerability, to step up and lead the charge for a safer, better Internet, on March 15, 2022 Cloudflare announced that all plans (including Free) would get WAF Managed Rules for high-impact vulnerabilities. These free rules tackle high-impact vulnerabilities such as the Log4J exploit, the Shellshock vulnerability, and various widespread WordPress exploits. Every business or personal website, regardless of size or budget, can protect their digital assets using Cloudflare’s WAF.

The full security advisory for Log4J published by Apache Software Foundation can be found here.

CVE-2022-26134: Atlassian Confluence Code Injection

A code injection vulnerability that afflicted Atlassian Confluence was the second most exploited CVE in 2022. This exploit posed a threat to entire systems, leaving many businesses at the mercy of attackers. This is an indication of how critical knowledge-based systems have become in managing information within organizations. Attackers are targeting these systems as they recognize how  important they are.. In response, the Cloudflare WAF team rolled out two emergency releases to protect its customers:

As part of these releases, two rules were made available to all WAF users:

  • Atlassian Confluence – Code Injection – CVE:CVE-2022-26134
  • Atlassian Confluence – Code Injection – Extended – CVE:CVE-2022-26134

The graph below displays the number of hits received each day, showing a clear peak followed by a gradual decline as systems were patched and secured.

Both Log4J and Confluence Code Injection show some seasonality, where a higher volume of attacks is carried out between September / November 2022 until March 2023. This likely reflects campaigns that are managed by attackers that are still attempting to exploit this vulnerability (an ongoing campaign is visible towards the end of July 2023).

Unmasking the top exploited vulnerabilities of 2022

Security advisory for reference.

CVE-2021-34473, CVE-2021-31207, and CVE-2021-34523: Microsoft Exchange SSRF and RCE Vulnerabilities

Three previously unknown bugs were chained together to achieve a Remote Code Execution (RCE) 0-day attack. Given how widely adopted Microsoft Exchange servers are, these exploits posed serious threats to data security and business operations across all industries, geographies and sectors.

Cloudflare WAF published a rule for this vulnerability with the Emergency Release: March 3, 2022 that contained the rule Microsoft Exchange SSRF and RCE vulnerability – CVE:CVE-2022-41040, CVE:CVE-2022-41082.

The trend of these attacks over the past year can be seen in the graph below.

Unmasking the top exploited vulnerabilities of 2022

Security advisories for reference: CVE-2021-34473, CVE-2021-31207 and CVE-2021-34523.

CVE-2022-1388: RCE in BIG-IP F5

This particular security vulnerability can be exploited where an unauthenticated adversary has network connectivity to the BIG-IP system (the F5 product name of a group of application security and performance solutions). Either via the management interface or self-assigned IP addresses the attacker can execute unrestricted system commands.

Cloudflare did an emergency release to detect this issue (Emergency Release: May 5, 2022) with the rule Command Injection – RCE in BIG-IP – CVE:CVE-2022-1388.

There is a relatively persistent pattern of exploitation without signs of specific campaigns, with the exception of a spike occurring in late June 2023.

Unmasking the top exploited vulnerabilities of 2022
a

F5 security advisory for reference.

CVE-2022-22954: VMware Workspace ONE Access and Identity Manager Server-side Template Injection Remote Code Execution Vulnerability

With this vulnerability, an attacker can remotely trigger a server-side template injection that may result in remote code execution. Successful exploitation allows an unauthenticated attacker with network access to the web interface to execute an arbitrary shell command as the VMware user. Later, this issue was combined with CVE-2022-22960 (which was a Local Privilege Escalation Vulnerability (LPE) issue). In combination, these two vulnerabilities allowed remote attackers to execute commands with root privileges.

Cloudflare WAF published a rule for this vulnerability: Release: May 5, 2022. Exploit attempt graph over time shown below.

Unmasking the top exploited vulnerabilities of 2022

VMware Security advisory.

CVE-2021-26084: Confluence Server Webwork OGNL injection

An OGNL injection vulnerability was found that allows an unauthenticated attacker to execute arbitrary code on a Confluence Server or Data Center instance. Cloudflare WAF performed an emergency release for this vulnerability on September 9, 2022. When compared to the other CVEs discussed in this post, we have not observed a lot of exploits over the past year.

Unmasking the top exploited vulnerabilities of 2022

Official security advisory.

Recommendations for enhanced protection

We recommend all server admins to keep their software updated when fixes become available. Cloudflare customers — including those on our free tier — can leverage new rules addressing CVEs and 0-day threats, updated weekly in the Managed Ruleset. High-risk CVEs may even prompt emergency releases. In addition to this, Enterprise customers have access to the WAF Attack Score: an AI-powered detection feature that supplements traditional signature-based rules, identifying unknown threats and bypass attempts. With the combined strength of rule-based and AI detection, Cloudflare offers robust defense against known and emerging threats.

Conclusions

Cloudflare’s data is able to augment CISA’s vulnerability report — of note, we see attempts to exploit the top two vulnerabilities that are several orders of magnitude more compared to the remainder of the list. Organizations should focus their software patching efforts based on the list provided. It is, of course, important to note that all software should be patched, and good WAF implementations will ensure additional security and “buy time” for underlying systems to be secured for both existing and future vulnerabilities.

Unsupervised graph anomaly detection – Catching new fraudulent behaviours

Post Syndicated from Grab Tech original https://engineering.grab.com/graph-anomaly-model

Earlier in this series, we covered the importance of graph networks, graph concepts, graph visualisation, and graph-based fraud detection methods. In this article, we will discuss how to automatically detect new types of fraudulent behaviour and swiftly take action on them.

One of the challenges in fraud detection is that fraudsters are incentivised to always adversarially innovate their way of conducting frauds, i.e., their modus operandi (MO in short). Machine learning models trained using historical data may not be able to pick up new MOs, as they are new patterns that are not available in existing training data. To enhance Grab’s existing security defences and protect our users from these new MOs, we needed a machine learning model that is able to detect them quickly without the need for any label supervision, i.e., an unsupervised learning model rather than the regular supervised learning model.

To address this, we developed an in-house machine learning model for detecting anomalous patterns in graphs, which has led to the discovery of new fraud MOs. Our focus was initially on GrabFood and GrabMart verticals, where we monitored the interactions between consumers and merchants. We modelled these interactions as a bipartite graph (a type of graph for modelling interactions between two groups) and then performed anomaly detection on the graph. Our in-house anomaly detection model was also presented at the International Joint Conference on Neural Networks (IJCNN) 2023, a premier academic conference in the area of neural networks, machine learning, and artificial intelligence.

In this blog, we discuss the model and its application within Grab. For avid audiences that want to read the details of our model, you can access it here. Note that even though we implemented our model for anomaly detection in GrabFood and GrabMart, the model is designed for general purposes and is applicable to interaction graphs between any two groups.

Interaction-Focused Anomaly Detection on Bipartite Node-and-Edge-Attributed Graphs
By Rizal Fathony, Jenn Ng, Jia Chen
Presented at International Joint Conference on Neural Networks (IJCNN) 2023

Before we dive into how our model works, it is important to understand the process of graph construction in our application as the model assumes the availability of the graphs in a standardised format.

Graph construction 

We modelled the interactions between consumers and merchants in GrabFood and GrabMart platforms as bipartite graphs (G), where the first group of nodes (U) represents the consumers, the second group of nodes (V) represents the merchants, and the edges (E) connecting them means that the consumers have placed some food/mart orders to the merchants. The graph is also supplied with rich transactional information about the consumers and the merchants in the form of node features (Xu and Xv), as well as order information in the form of edge features (Xe).

Fig 1. Graph construction process

The goal of our anomaly model is to detect anomalous and suspicious behaviours from the consumers or merchants (node-level anomaly detection), as well as anomalous order interactions (edge-level anomaly detection). As mentioned, this detection needs to be done without any label supervision.

Model architecture

We designed our graph anomaly model as a type of autoencoder, with an encoder and two decoders – a feature decoder and a structure decoder. The key feature of our model is that it accepts a bipartite graph with both node and edge attributes as the input. This is important as both node and edge attributes encode essential information for determining if certain behaviours are suspicious. Many previous works on graph anomaly detection only support node attributes. In addition, our model can produce both node and edge level anomaly scores, unlike most of the previous works that produce node-level scores only. We named our model GraphBEAN, which is short for Bipartite Node-and-Edge-Attributed Networks.

From the input, the encoder then processes the attributed bipartite graph into a series of graph convolution layers to produce latent representations for both node groups. Our graph convolution layers produce new representations for each node in both node groups (U and V), as well as for each edge in the graph. Note that the last convolution layer in the encoder only produces the latent representations for nodes, without producing edge representations. The reason for this design is that we only put the latent representations for the active actors, the nodes representing consumers and merchants, but not their interactions.

Fig 2. GraphBEAN architecture

From the nodes’ latent representations, the feature decoder is tasked to reconstruct the original graph with both node and edge attributes via a series of graph convolution layers. As the graph structure is provided by the feature decoder, we task the structure decoder to learn the graph structure by predicting if there exists an edge connecting two nodes. This edge prediction, as well as the graph reconstructed by the feature decoder, are then compared to the original input graph via a reconstruction loss function.

The model is then trained using the bipartite graph constructed from GrabFood and GrabMart transactions. We use a reconstruction-based loss function as the training objective of the model. After the training is completed, we compute the anomaly score of each node and edge in the graph using the trained model.

Anomaly score computation

Our anomaly scores are reconstruction-based. The score design assumes that normal behaviours are common in the dataset and thus, can be easily reconstructed by the model. On the other hand, anomalous behaviours are rare. Therefore the model will have a hard time reconstructing them, hence producing high errors.

Fig 3. Edge-level and node-level anomaly scores computation

The model produces two types of anomaly scores. First, the edge-level anomaly scores, which are calculated from the edge reconstruction error. Second, the node-level anomaly scores, which are calculated from node reconstruction error plus an aggregate over the edge scores from the edges connected to the node. This aggregate could be a mean or max aggregate.

Actioning system

In our implementation of GraphBEAN within Grab, we designed a full pipeline of anomaly detection and actioning systems. It is a fully-automated system for constructing a bipartite graph from GrabFood and GrabMart transactions, training a GraphBEAN model using the graph, and computing anomaly scores. After computing anomaly scores for all consumers and merchants (node-level), as well as all of their interactions (edge-level), it automatically passes the scores to our actioning system. But before that, it also passes them through a system we call fraud type tagger. This is also a fully-automated heuristic-based system that tags some of the detected anomalies with some fraud tags. The purpose of this tagging is to provide some context in general, like the types of detected anomalies. Some examples of these tags are promo abuse or possible collusion.

Fig 4. Pipeline in our actioning system

Both the anomaly scores and the fraud type tags are then forwarded to our actioning system. The system consists of two subsystems:

  • Human expert actioning system: Our fraud experts analyse the detected anomalies and perform certain actioning on them, like suspending certain transaction features from suspicious merchants.
  • Automatic actioning system: Combines the anomaly scores and fraud type tags with other external signals to automatically do actioning on the detected anomalies, like preventing promos from being used by fraudsters or preventing fraudulent transactions from occurring. These actions vary depending on the type of fraud and the scores.

What’s next?

The GraphBEAN model enables the detection of suspicious behaviour on graph data without the need for label supervision. By implementing the model on GrabFood and GrabMart platforms, we learnt that having such a system enables us to quickly identify new types of fraudulent behaviours and then swiftly perform action on them. This also allows us to enhance Grab’s defence against fraudulent activity and actively protect our users.

We are currently working on extending the model into more generic heterogeneous (multi-entity) graphs. In addition, we are also working on implementing it to more use cases within Grab.

Join us

Grab is the leading superapp platform in Southeast Asia, providing everyday services that matter to consumers. More than just a ride-hailing and food delivery app, Grab offers a wide range of on-demand services in the region, including mobility, food, package and grocery delivery services, mobile payments, and financial services across 428 cities in eight countries.

Powered by technology and driven by heart, our mission is to drive Southeast Asia forward by creating economic empowerment for everyone. If this mission speaks to you, join our team today!

Perform continuous vulnerability scanning of AWS Lambda functions with Amazon Inspector

Post Syndicated from Manjunath Arakere original https://aws.amazon.com/blogs/security/perform-continuous-vulnerability-scanning-of-aws-lambda-functions-with-amazon-inspector/

This blog post demonstrates how you can activate Amazon Inspector within one or more AWS accounts and be notified when a vulnerability is detected in an AWS Lambda function.

Amazon Inspector is an automated vulnerability management service that continually scans workloads for software vulnerabilities and unintended network exposure. Amazon Inspector scans mixed workloads like Amazon Elastic Compute Cloud (Amazon EC2) instances and container images located in Amazon Elastic Container Registry (Amazon ECR). At re:Invent 2022, we announced Amazon Inspector support for Lambda functions and Lambda layers to provide a consolidated solution for compute types.

Only scanning your functions for vulnerabilities before deployment might not be enough since vulnerabilities can appear at any time, like the widespread Apache Log4j vulnerability. So it’s essential that workloads are continuously monitored and rescanned in near real time as new vulnerabilities are published or workloads are changed.

Amazon Inspector scans are intelligently initiated based on the updates to Lambda functions or when new Common Vulnerabilities and Exposures (CVEs) are published that are relevant to your function. No agents are needed for Amazon Inspector to work, which means you don’t need to install a library or agent in your Lambda functions or layers. When Amazon Inspector discovers a software vulnerability or network configuration issue, it creates a finding which describes the vulnerability, identifies the affected resource, rates the severity of the vulnerability, and provides remediation guidance.

In addition, Amazon Inspector integrates with several AWS services, such as Amazon EventBridge and AWS Security Hub. You can use EventBridge to build automation workflows like getting notified for a specific vulnerability finding or performing an automatic remediation with the help of Lambda or AWS Systems Manager.

In this blog post, you will learn how to do the following:

  1. Activate Amazon Inspector in a single AWS account and AWS Region.
  2. See how Amazon Inspector automated discovery and continuous vulnerability scanning works by deploying a new Lambda function with a vulnerable package dependency.
  3. Receive a near real-time notification when a vulnerability with a specific severity is detected in a Lambda function with the help of EventBridge and Amazon Simple Notification Service (Amazon SNS).
  4. Remediate the vulnerability by using the recommendation provided in the Amazon Inspector dashboard.
  5. Activate Amazon Inspector in multiple accounts or Regions through AWS Organizations.

Solution architecture

Figure 1 shows the AWS services used in the solution and how they are integrated.

Figure 1: Solution architecture overview

Figure 1: Solution architecture overview

The workflow for the solution is as follows:

  1. Deploy a new Lambda function by using the AWS Serverless Application Model (AWS SAM).
  2. Amazon Inspector scans when a new vulnerability is published or when an update to an existing Lambda function or a new Lambda function is deployed. Vulnerabilities are identified in the deployed Lambda function.
  3. Amazon EventBridge receives the events from Amazon Inspector and checks against the rules for specific events or filter conditions.
  4. In this case, an EventBridge rule exists for the Amazon Inspector findings, and the target is defined as an SNS topic to send an email to the system operations team.
  5. The EventBridge rule invokes the target SNS topic with the event data, and an email is sent to the confirmed subscribers in the SNS topic.
  6. The system operations team receives an email with detailed information on the vulnerability, the fixed package versions, the Amazon Inspector score to prioritize, and the impacted Lambda functions. By using the remediation information from Amazon Inspector, the team can now prioritize actions and remediate.

Prerequisites

To follow along with this demo, we recommend that you have the following in place:

  • An AWS account.
  • A command line interface: AWS CloudShell or AWS CLI. In this post, we recommend the use of CloudShell because it already has Python and AWS SAM. However, you can also use your CLI with AWS CLI, SAM, and Python.
  • An AWS Region where Amazon Inspector Lambda code scanning is available.
  • An IAM role in that account with administrator privileges.

The solution in this post includes the following AWS services: Amazon Inspector, AWS Lambda, Amazon EventBridge, AWS Identity and Access Management (IAM), Amazon SNS, AWS CloudShell and AWS Organizations for activating Amazon Inspector at scale (multi-accounts).

Step 1: Activate Amazon Inspector in a single account in the Region

The first step is to activate Amazon Inspector in your account in the Region you are using.

To activate Amazon Inspector

  1. Sign in to the AWS Management Console.
  2. Open AWS CloudShell. CloudShell inherits the credentials and permissions of the IAM principal who is signed in to the AWS Management Console. CloudShell comes with the CLIs and runtimes that are needed for this demo (AWS CLI, AWS SAM, and Python).
  3. Use the following command in CloudShell to get the status of the Amazon Inspector activation.
    aws inspector2 batch-get-account-status

  4. Use the following command to activate Inspector in the default Region for resource type LAMBDA. Other allowed values for resource types are EC2, ECR and LAMDA_CODE.
    aws inspector2 enable --resource-types '["LAMBDA"]'

  5. Use the following command to verify the status of the Amazon Inspector activation.
    aws inspector2 batch-get-account-status

You should see a response that shows that Amazon Inspector is enabled for Lambda resources, as shown in Figure 2.

Figure 2: Amazon Inspector status after you enable Lambda scanning

Figure 2: Amazon Inspector status after you enable Lambda scanning

Step 2: Create an SNS topic and subscription for notification

Next, create the SNS topic and the subscription so that you will be notified of each new Amazon Inspector finding.

To create the SNS topic and subscription

  1. Use the following command in CloudShell to create the SNS topic and its subscription and replace <REGION_NAME>, <AWS_ACCOUNTID> and <[email protected]> by the relevant values.
    aws sns create-topic --name amazon-inspector-findings-notifier; 
    
    aws sns subscribe \
    --topic-arn arn:aws:sns:<REGION_NAME>:<AWS_ACCOUNTID>:amazon-inspector-findings-notifier \
    --protocol email --notification-endpoint <[email protected]>

  2. Check the email inbox you entered for <[email protected]>, and in the email from Amazon SNS, choose Confirm subscription.
  3. In the CloudShell console, use the following command to list the subscriptions, to verify the topic and email subscription.
    aws sns list-subscriptions

    You should see a response that shows subscription details like the email address and ARN, as shown in Figure 3.

    Figure 3: Subscribed email address and SNS topic

    Figure 3: Subscribed email address and SNS topic

  4. Use the following command to send a test message to your subscribed email and verify that you receive the message by replacing <REGION_NAME> and <AWS_ACCOUNTID>.
    aws sns publish \
        --topic-arn "arn:aws:sns:<REGION_NAME>:<AWS_ACCOUNTID>:amazon-inspector-findings-notifier" \
        --message "Hello from Amazon Inspector2"

Step 3: Set up Amazon EventBridge with a custom rule and the SNS topic as target

Create an EventBridge rule that will invoke your previously created SNS topic whenever Amazon Inspector finds a new vulnerability with a critical severity.

To set up the EventBridge custom rule

  1. In the CloudShell console, use the following command to create an EventBridge rule named amazon-inspector-findings with filters InspectorScore greater than 8 and severity state set to CRITICAL.
    aws events put-rule \
        --name "amazon-inspector-findings" \
        --event-pattern "{\"source\": [\"aws.inspector2\"],\"detail-type\": [\"Inspector2 Finding\"],\"detail\": {\"inspectorScore\": [ { \"numeric\": [ \">\", 8] } ],\"severity\": [\"CRITICAL\"]}}"

    Refer to the topic Amazon EventBridge event schema for Amazon Inspector events to customize the event pattern for your application needs.

  2. To verify the rule creation, go to the EventBridge console and in the left navigation bar, choose Rules.
  3. Choose the rule with the name amazon-inspector-findings. You should see the event pattern as shown in Figure 4.
    Figure 4: Event pattern for the EventBridge rule to filter on CRITICAL vulnerabilities.

    Figure 4: Event pattern for the EventBridge rule to filter on CRITICAL vulnerabilities.

  4. Add the SNS topic you previously created as the target to the EventBridge rule. Replace <REGION_NAME>, <AWS_ACCOUNTID>, and <RANDOM-UNIQUE-IDENTIFIER-VALUE> with the relevant values. For RANDOM-UNIQUE-IDENTIFIER-VALUE, create a memorable and unique string.
    aws events put-targets \
        --rule amazon-inspector-findings \
        --targets "Id"="<RANDOM-UNIQUE-IDENTIFIER-VALUE>","Arn"="arn:aws:sns:<REGION_NAME>:<AWS_ACCOUNTID>:amazon-inspector-findings-notifier"

    Important: Save the target ID. You will need this in order to delete the target in the last step.

  5. Provide permission to enable Amazon EventBridge to publish to SNS topic amazon-inspector-findings-notifier
    aws sns set-topic-attributes --topic-arn "arn:aws:sns:<REGION_NAME>:<AWS_ACCOUNTID>:amazon-inspector-findings-notifier" \
    --attribute-name Policy \
    --attribute-value "{\"Version\":\"2012-10-17\",\"Id\":\"__default_policy_ID\",\"Statement\":[{\"Sid\":\"PublishEventsToMyTopic\",\"Effect\":\"Allow\",\"Principal\":{\"Service\":\"events.amazonaws.com\"},\"Action\":\"sns:Publish\",\"Resource\":\"arn:aws:sns:<REGION_NAME>:<AWS_ACCOUNTID>:amazon-inspector-findings-notifier\"}]}"

Step 4: Deploy the Lambda function to the AWS account by using AWS SAM

In this step, you will use Serverless Application Manager (SAM) quick state templates to build and deploy a Lambda function with a vulnerable library, in order to generate findings. Learn more about AWS SAM.

To deploy the Lambda function with a vulnerable library

  1. In the CloudShell console, use a prebuilt “hello-world” AWS SAM template to deploy the Lambda function.
    sam init --runtime python3.7 --dependency-manager pip --app-template hello-world --name sam-app

  2. Use the following command to add the vulnerable package python-jwt==3.3.3 to the Lambda function.
    cd sam-app;
    echo -e 'requests\npython-jwt==3.3.3' > hello_world/requirements.txt

  3. Use the following command to build the application.
    sam build

  4. Use the following command to deploy the application with the guided option.
    sam deploy --guided

    This command packages and deploys the application to your AWS account. It provides a series of prompts. You may respond to the prompts with the:

    1. Stack name you want
    2. Set the default options, except for the
      1. HelloWorldFunction may not have authorization defined, Is this okay? [y/N]: prompt. Here, input y and press Enter and
      2. Deploy this changeset? [y/N]: prompt. Here, input y and press Enter.

Step 5: View Amazon Inspector findings

Amazon Inspector will automatically generate findings when scanning the Lambda function previously deployed. To view those findings, follow the steps below.

To view Amazon Inspector findings for the vulnerability

  1. Navigate to the Amazon Inspector console.
  2. In the left navigation menu, choose All findings to see all of the Active findings, as shown in Figure 5.

    Due to the custom event pattern rule in Amazon EventBridge, even though there are multiple findings for the vulnerable package python-jwt==3.3.3, you will be notified only for the finding that has InspectorScore greater than 8 and severity CRITICAL.

  3. Choose the title of each finding to see detailed information about the vulnerability.
    Figure 5: Example of findings from the Amazon Inspector console

    Figure 5: Example of findings from the Amazon Inspector console

Step 6: Remediate the vulnerability by applying the fixed package version

Now you can remediate the vulnerability by updating the package version as suggested by Amazon Inspector.

To remediate the vulnerability

  1. In the Amazon Inspector console, in the left navigation menu, choose All Findings.
  2. Choose the title of the vulnerability to see the finding details and the remediation recommendations.
    Figure 6: Amazon Inspector finding for python-jwt, with the associated remediation

    Figure 6: Amazon Inspector finding for python-jwt, with the associated remediation

  3. To remediate, use the following command to update the package version to the fixed version as suggested by Amazon Inspector.
    cd /home/cloudshell-user/sam-app;
    echo -e "requests\npython-jwt==3.3.4" > hello_world/requirements.txt

  4. Use the following command to build the application.
    sam build

  5. Use the following command to deploy the application with the guided option.
    sam deploy --guided

    This command packages and deploys the application to your AWS account. It provides a series of prompts. You may respond to the prompts with the

    1. Stack name you want
    2. Set the default options, except for the
      1. HelloWorldFunction may not have authorization defined, Is this okay? [y/N]: prompt. Here, input y and press Enter and
      2. Deploy this changeset? [y/N]: prompt. Here, input y and press Enter.
  6. Amazon Inspector automatically rescans the function after its deployment and reevaluates the findings. At this point, you can navigate back to the Amazon Inspector console, and in the left navigation menu, choose All findings. In the Findings area, you can see that the vulnerabilities are moved from Active to Closed status.

    Due to the custom event pattern rule in Amazon EventBridge, you will be notified by email with finding status as CLOSED.

    Figure 7: Inspector rescan results, showing no open findings after remediation

    Figure 7: Inspector rescan results, showing no open findings after remediation

(Optional) Step 7: Activate Amazon Inspector in multiple accounts and Regions

To benefit from Amazon Inspector scanning capabilities across the accounts that you have in AWS Organizations and in your selected Regions, use the following steps:

To activate Amazon Inspector in multiple accounts and Regions

  1. In the CloudShell console, use the following command to clone the code from the aws-samples inspector2-enablement-with-cli GitHub repo.
    cd /home/cloudshell-user;
    git clone https://github.com/aws-samples/inspector2-enablement-with-cli.git;
    cd inspector2-enablement-with-cli

  2. Follow the instructions from the README.md file.
  3. Configure the file param_inspector2.json with the relevant values, as follows:
    • inspector2_da: The delegated administrator account ID for Amazon Inspector to manage member accounts.
    • scanning_type: The resource types (EC2, ECR, LAMBDA) to be enabled by Amazon Inspector.
    • auto_enable: The resource types to be enabled on every account that is newly attached to the delegated administrator.
    • regions: Because Amazon Inspector is a regional service, provide the list of AWS Regions to enable.
  4. Select the AWS account that would be used as the delegated administrator account (<DA_ACCOUNT_ID>).
  5. Delegate an account as the admin for Amazon Inspector by using the following command.
    ./inspector2_enablement_with_awscli.sh -a delegate_admin -da <DA_ACCOUNT_ID>

  6. Activate the delegated admin by using the following command:
    ./inspector2_enablement_with_awscli.sh -a activate -t <DA_ACCOUNT_ID> -s all

  7. Associate the member accounts by using the following command:
    ./inspector2_enablement_with_awscli.sh -a associate -t members

  8. Wait five minutes.
  9. Enable the resource types (EC2, ECR, LAMBDA) on your member accounts by using the following command:
    ./inspector2_enablement_with_awscli.sh -a activate -t members

  10. Enable Amazon Inspector on the new member accounts that are associated with the organization by using the following command:
    ./inspector2_enablement_with_awscli.sh -auto_enable

  11. Check the Amazon Inspector status in your accounts and in multiple selected Regions by using the following command:
    ./inspector2_enablement_with_awscli.sh -a get_status

There are other options you can use to enable Amazon Inspector in multiple accounts, like AWS Control Tower and Terraform. For the reference architecture for Control Tower, see the AWS Security Reference Architecture Examples on GitHub. For more information on the Terraform option, see the Terraform aws_inspector2_enabler resource page.

Step 8: Delete the resources created in the previous steps

AWS offers a 15-day free trial for Amazon Inspector so that you can evaluate the service and estimate its cost.

To avoid potential charges, delete the AWS resources that you created in the previous steps of this solution (Lambda function, EventBridge target, EventBridge rule, and SNS topic), and deactivate Amazon Inspector.

To delete resources

  1. In the CloudShell console, enter the sam-app folder.
    cd /home/cloudshell-user/sam-app

  2. Delete the Lambda function and confirm by typing “y” when prompted for confirmation.
    sam delete

  3. Remove the SNS target from the Amazon EventBridge rule.
    aws events remove-targets --rule "amazon-inspector-findings" --ids <RANDOM-UNIQUE-IDENTIFIER-VALUE>

    Note: If you don’t remember the target ID, navigate to the Amazon EventBridge console, and in the left navigation menu, choose Rules. Select the rule that you want to delete. Choose CloudFormation, and copy the ID.

  4. Delete the EventBridge rule.
    aws events delete-rule --name amazon-inspector-findings

  5. Delete the SNS topic.
    aws sns delete-topic --topic-arn arn:aws:sns:<REGION_NAME>:<AWS_ACCOUNTID>:amazon-inspector-findings-notifier

  6. Disable Amazon Inspector.
    aws inspector2 disable --resource-types '["LAMBDA"]'

    Follow the new few steps to roll back changes only if you have performed the activities listed in Step 7: Activate Amazon Inspector in multiple accounts and Regions.

  7. In the CloudShell console, enter the folder inspector2-enablement-with-cli.
    cd /home/cloudshell-user/inspector2-enablement-with-cli

  8. Deactivate the resource types (EC2, ECR, LAMBDA) on your member accounts.
    ./inspector2_enablement_with_awscli.sh -a deactivate -t members -s all

  9. Disassociate the member accounts.
    ./inspector2_enablement_with_awscli.sh -a disassociate -t members

  10. Deactivate the delegated admin account.
    ./inspector2_enablement_with_awscli.sh -a deactivate -t <DA_ACCOUNT_ID> -s all

  11. Remove the delegated account as the admin for Amazon Inspector.
    ./inspector2_enablement_with_awscli.sh -a remove_admin -da <DA_ACCOUNT_ID>

Conclusion

In this blog post, we discussed how you can use Amazon Inspector to continuously scan your Lambda functions, and how to configure an Amazon EventBridge rule and SNS to send out notification of Lambda function vulnerabilities in near real time. You can then perform remediation activities by using AWS Lambda or AWS Systems Manager. We also showed how to enable Amazon Inspector at scale, activating in both single and multiple accounts, in default and multiple Regions.

As of the writing this post, a new feature to perform code scans for Lambda functions is available. Amazon Inspector can now also scan the custom application code within a Lambda function for code security vulnerabilities such as injection flaws, data leaks, weak cryptography, or missing encryption, based on AWS security best practices. You can use this additional scanning functionality to further protect your workloads.

If you have feedback about this blog post, submit comments in the Comments section below. If you have question about this blog post, start a new thread on the Amazon Inspector forum or contact AWS Support.

 
Want more AWS Security news? Follow us on Twitter.

Manjunath Arakere

Manjunath Arakere

Manjunath is a Senior Solutions Architect in the Worldwide Public Sector team at AWS. He works with Public Sector partners to design and scale well-architected solutions, and he supports their cloud migrations and application modernization initiatives. Manjunath specializes in migration, modernization and serverless technology.

Stéphanie Mbappe

Stéphanie Mbappe

Stéphanie is a Security Consultant with Amazon Web Services. She delights in assisting her customers at every step of their security journey. Stéphanie enjoys learning, designing new solutions, and sharing her knowledge with others.

What’s Up, Home? – How to secure your (home) monitoring

Post Syndicated from Janne Pikkarainen original https://blog.zabbix.com/whats-up-home-how-to-secure-your-home-monitoring/26241/

As my home monitoring experiment has become such a celebrity and as it has so much going on, I’m trying to make sure I won’t ever lose its configuration and data, no matter what would happen. Here are some tips and reminders for you, too.

In the IT world, so much can go wrong. Hardware can die, files can become corrupt, malware can hit you, hackers can breach your systems, buggy software updates can cause havoc, you can fat-finger some commands or click on the wrong place and remove data… the list is endless. Here are some ways I’m attempting to protect my home monitoring environment.

Keep software updated

First things first. In today’s malicious world, it’s mandatory that you keep your software updated. With Linux and Zabbix, you don’t have an excuse to skip the updates. Updating your systems is fast and trouble-free. In small environments like home, even major Zabbix upgrades are fast as the database is not very big, so the database migrations that in a corporate environment can take time, will go through in minutes if not faster.

Remember to backup

Keep your backups in good shape. In my case, I do take backups with BackupPC and monitor my backups with Zabbix.

But I don’t trust one environment. What if my BackupPC says Kaboom? A nightly cron job also copies backup archives from my Raspberry Pi to my Mac, which in turn mirrors the backups to my iCloud. And, there’s one more cloud service I’m using for all my backups but not mentioning it here by name.

Test your backups

As long as you have not verified that your backups do actually work, you do not have a working backup. Have a virtual machine into which you can try to restore your backups. See if they work. Test them periodically, either manually or figure out an automated way to do that. 

In the case of Zabbix, you can make your primary Zabbix monitor your test environment, and make Zabbix alert if your restore environment Zabbix suddenly starts responding back something else than the regular login page, or if the restore environment database doesn’t come back with some query response you would expect.

Setup a HA cluster

As any hardware can die, it’s not a bad idea to set up a HA cluster. Last winter I was preparing for potential electricity blackouts here in Finland and did setup my laptop to be a secondary node for my Zabbix. This setup has been working very well.

Use strong passwords

Even if it would only be your sandbox environment where you do test new stuff, please remember to use strong passwords. An evil actor can attempt to breach more targets in your network through a single point of failure.

Use ssh keys

Instead of username + password combination, use ssh keys for ssh authentication. Keys are immune to brute-force attempts and with tuning, ssh keys can also be allowed to only connect from specific IPs and run only specific commands. You know how in your ~/.ssh/authorized_keys the lines do start with something like 

ssh-rsa aZfgT12b(....

but if you modify it to be 

command="/usr/bin/rsync" ssh-rsa aZfgT12b(....

well, then only rsync would be allowed.

Or, for IP address limitation

from="123.123.123.123" ssh-rsa aZfgT12b(....

Of course, these can be combined:

from="123.123.123.123" command="/usr/bin/rsync" ssh-rsa aZfgT12b(....

Obviously, this grants you much more security than traditional logins.

Use HashiCorp Vault

OK, I admit I’m not doing this at home as it would be overkill for my few logins. But, in a larger environment with absolutely critical safety requirements, use HashiCorp Vault for protecting your credentials. Zabbix has native support for it.

Monitor your logs

Setup a centralized log server — it can be your Zabbix server environment, too — and make sure you monitor the logs. My Zabbix gets all my logs, but wouldn’t be a bad idea to use more advanced log monitoring tools, too. Since I already do have ElastiFlow running at home, at some point I might start utilizing Elasticsearch for the logs. Not doing it much yet.

Use chkrootkit, AIDE, others

Tools like AIDE or chkrootkit can help you detect an intrusion. Set them to run in your cron and get alerted in case of any anomalies. Maybe I’ll one day integrate Zabbix with these tools.

Firewall your environment, use VPN

Don’t allow direct access from the Internet to your Zabbix, or your database, or anything really. In my case, my Asus router allows setting up OpenVPN connections, so that’s what I use. Whilst on the go, I just connect to OpenVPN on my phone and do whatever I need remotely through that.

Anything else?

Did I miss something? Let me know in the comments.

This post was originally published on the author’s page.

The post What’s Up, Home? – How to secure your (home) monitoring appeared first on Zabbix Blog.