Tag Archives: security teams

Israeli Security Attacks AMD by Publishing Zero-Day Exploits

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/03/israeli_securit.html

Last week, the Israeli security company CTS Labs published a series of exploits against AMD chips. The publication came with the flashy website, detailed whitepaper, cool vulnerability names — RYZENFALL, MASTERKEY, FALLOUT, and CHIMERA — and logos we’ve come to expect from these sorts of things. What’s new is that the company only gave AMD a day’s notice, which breaks with every norm about responsible disclosure. CTS Labs didn’t release details of the exploits, only high-level descriptions of the vulnerabilities, but it is probably still enough for others to reproduce their results. This is incredibly irresponsible of the company.

Moreover, the vulnerabilities are kind of meh. Nicholas Weaver explains:

In order to use any of the four vulnerabilities, an attacker must already have almost complete control over the machine. For most purposes, if the attacker already has this access, we would generally say they’ve already won. But these days, modern computers at least attempt to protect against a rogue operating system by having separate secure subprocessors. CTS Labs discovered the vulnerabilities when they looked at AMD’s implementation of the secure subprocessor to see if an attacker, having already taken control of the host operating system, could bypass these last lines of defense.

In a “Clarification,” CTS Labs kind of agrees:

The vulnerabilities described in amdflaws.com could give an attacker that has already gained initial foothold into one or more computers in the enterprise a significant advantage against IT and security teams.

The only thing the attacker would need after the initial local compromise is local admin privileges and an affected machine. To clarify misunderstandings — there is no need for physical access, no digital signatures, no additional vulnerability to reflash an unsigned BIOS. Buy a computer from the store, run the exploits as admin — and they will work (on the affected models as described on the site).

The weirdest thing about this story is that CTS Labs describes one of the vulnerabilities, Chimera, as a backdoor. Although it doesn’t t come out and say that this was deliberately planted by someone, it does make the point that the chips were designed in Taiwan. This is an incredible accusation, and honestly needs more evidence before we can evaluate it.

The upshot of all of this is that CTS Labs played this for maximum publicity: over-hyping its results and minimizing AMD’s ability to respond. And it may have an ulterior motive:

But CTS’s website touting AMD’s flaws also contained a disclaimer that threw some shadows on the company’s motives: “Although we have a good faith belief in our analysis and believe it to be objective and unbiased, you are advised that we may have, either directly or indirectly, an economic interest in the performance of the securities of the companies whose products are the subject of our reports,” reads one line. WIRED asked in a follow-up email to CTS whether the company holds any financial positions designed to profit from the release of its AMD research specifically. CTS didn’t respond.

We all need to demand better behavior from security researchers. I know that any publicity is good publicity, but I am pleased to see the stories critical of CTS Labs outnumbering the stories praising it.

EDITED TO ADD (3/21): AMD responds:

AMD’s response today agrees that all four bug families are real and are found in the various components identified by CTS. The company says that it is developing firmware updates for the three PSP flaws. These fixes, to be made available in “coming weeks,” will be installed through system firmware updates. The firmware updates will also mitigate, in some unspecified way, the Chimera issue, with AMD saying that it’s working with ASMedia, the third-party hardware company that developed Promontory for AMD, to develop suitable protections. In its report, CTS wrote that, while one CTS attack vector was a firmware bug (and hence in principle correctable), the other was a hardware flaw. If true, there may be no effective way of solving it.

Response here.

Central Logging in Multi-Account Environments

Post Syndicated from matouk original https://aws.amazon.com/blogs/architecture/central-logging-in-multi-account-environments/

Centralized logging is often required in large enterprise environments for a number of reasons, ranging from compliance and security to analytics and application-specific needs.

I’ve seen that in a multi-account environment, whether the accounts belong to the same line of business or multiple business units, collecting logs in a central, dedicated logging account is an established best practice. It helps security teams detect malicious activities both in real-time and during incident response. It provides protection to log data in case it is accidentally or intentionally deleted. It also helps application teams correlate and analyze log data across multiple application tiers.

This blog post provides a solution and building blocks to stream Amazon CloudWatch log data across accounts. In a multi-account environment this repeatable solution could be deployed multiple times to stream all relevant Amazon CloudWatch log data from all accounts to a centralized logging account.

Solution Summary 

The solution uses Amazon Kinesis Data Streams and a log destination to set up an endpoint in the logging account to receive streamed logs and uses Amazon Kinesis Data Firehose to deliver log data to the Amazon Simple Storage Solution (S3) bucket. Application accounts will subscribe to stream all (or part) of their Amazon CloudWatch logs to a defined destination in the logging account via subscription filters.

Below is a diagram illustrating how the various services work together.


In logging an account, a Kinesis Data Stream is created to receive streamed log data and a log destination is created to facilitate remote streaming, configured to use the Kinesis Data Stream as its target.

The Amazon Kinesis Data Firehose stream is created to deliver log data from the data stream to S3. The delivery stream uses a generic AWS Lambda function for data validation and transformation.

In each application account, a subscription filter is created between each Amazon CloudWatch log group and the destination created for this log group in the logging account.

The following steps are involved in setting up the central-logging solution:

  1. Create an Amazon S3 bucket for your central logging in the logging account
  2. Create an AWS Lambda function for log data transformation and decoding in logging account
  3. Create a central logging stack as a logging-account destination ready to receive streamed logs and deliver them to S3
  4. Create a subscription in application accounts to deliver logs from a specific CloudWatch log group to the logging account destination
  5. Create Amazon Athena tables to query and analyze log data in your logging account

Creating a log destination in your logging account

In this section, we will setup the logging account side of the solution, providing detail on the list above. The example I use is for the us-east-1 region, however any region where required services are available could be used.

It’s important to note that your logging-account destination and application-account subscription must be in the same region. You can deploy the solution multiple times to create destinations in all required regions if application accounts use multiple regions.

Step 1: Create an S3 bucket

Use the CloudFormation template below to create S3 bucket in logging account. This template also configures the bucket to archive log data to Glacier after 60 days.


{
  "AWSTemplateFormatVersion":"2010-09-09",
  "Description": "CF Template to create S3 bucket for central logging",
  "Parameters":{

    "BucketName":{
      "Type":"String",
      "Default":"",
      "Description":"Central logging bucket name"
    }
  },
  "Resources":{
                        
   "CentralLoggingBucket" : {
      "Type" : "AWS::S3::Bucket",
      "Properties" : {
        "BucketName" : {"Ref": "BucketName"},
        "LifecycleConfiguration": {
            "Rules": [
                {
                  "Id": "ArchiveToGlacier",
                  "Prefix": "",
                  "Status": "Enabled",
                  "Transitions":[{
                      "TransitionInDays": "60",
                      "StorageClass": "GLACIER"
                  }]
                }
            ]
        }
      }
    }

  },
  "Outputs":{
    "CentralLogBucket":{
    	"Description" : "Central log bucket",
    	"Value" : {"Ref": "BucketName"} ,
    	"Export" : { "Name" : "CentralLogBucketName"}
    }
  }
} 

To create your central-logging bucket do the following:

  1. Save the template file to your local developer machine as “central-log-bucket.json”
  2. From the CloudFormation console, select “create new stack” and import the file “central-log-bucket.json”
  3. Fill in the parameters and complete stack creation steps (as indicated in the screenshot below)
  4. Verify the bucket has been created successfully and take a note of the bucket name

Step 2: Create data processing Lambda function

Use the template below to create a Lambda function in your logging account that will be used by Amazon Firehose for data transformation during the delivery process to S3. This function is based on the AWS Lambda kinesis-firehose-cloudwatch-logs-processor blueprint.

The function could be created manually from the blueprint or using the cloudformation template below. To find the blueprint navigate to Lambda -> Create -> Function -> Blueprints

This function will unzip the event message, parse it and verify that it is a valid CloudWatch log event. Additional processing can be added if needed. As this function is generic, it could be reused by all log-delivery streams.

{
  "AWSTemplateFormatVersion":"2010-09-09",
  "Description": "Create cloudwatch data processing lambda function",
  "Resources":{
      
    "LambdaRole": {
        "Type": "AWS::IAM::Role",
        "Properties": {
            "AssumeRolePolicyDocument": {
                "Version": "2012-10-17",
                "Statement": [
                    {
                        "Effect": "Allow",
                        "Principal": {
                            "Service": "lambda.amazonaws.com"
                        },
                        "Action": "sts:AssumeRole"
                    }
                ]
            },
            "Path": "/",
            "Policies": [
                {
                    "PolicyName": "firehoseCloudWatchDataProcessing",
                    "PolicyDocument": {
                        "Version": "2012-10-17",
                        "Statement": [
                            {
                                "Effect": "Allow",
                                "Action": [
                                    "logs:CreateLogGroup",
                                    "logs:CreateLogStream",
                                    "logs:PutLogEvents"
                                ],
                                "Resource": "arn:aws:logs:*:*:*"
                            }
                        ]
                    }
                }
            ]
        }
    },
      
    "FirehoseDataProcessingFunction": {
        "Type": "AWS::Lambda::Function",
        "Properties": {
            "Handler": "index.handler",
            "Role": {"Fn::GetAtt": ["LambdaRole","Arn"]},
            "Description": "Firehose cloudwatch data processing",
            "Code": {
                "ZipFile" : { "Fn::Join" : ["\n", [
                  "'use strict';",
                  "const zlib = require('zlib');",
                  "function transformLogEvent(logEvent) {",
                  "       return Promise.resolve(`${logEvent.message}\n`);",
                  "}",
                  "exports.handler = (event, context, callback) => {",
                  "    Promise.all(event.records.map(r => {",
                  "        const buffer = new Buffer(r.data, 'base64');",
                  "        const decompressed = zlib.gunzipSync(buffer);",
                  "        const data = JSON.parse(decompressed);",
                  "        if (data.messageType !== 'DATA_MESSAGE') {",
                  "            return Promise.resolve({",
                  "                recordId: r.recordId,",
                  "                result: 'ProcessingFailed',",
                  "            });",
                  "         } else {",
                  "            const promises = data.logEvents.map(transformLogEvent);",
                  "            return Promise.all(promises).then(transformed => {",
                  "                const payload = transformed.reduce((a, v) => a + v, '');",
                  "                const encoded = new Buffer(payload).toString('base64');",
                  "                console.log('---------------payloadv2:'+JSON.stringify(payload, null, 2));",
                  "                return {",
                  "                    recordId: r.recordId,",
                  "                    result: 'Ok',",
                  "                    data: encoded,",
                  "                };",
                  "           });",
                  "        }",
                  "    })).then(recs => callback(null, { records: recs }));",
                    "};"

                ]]}
            },
            "Runtime": "nodejs6.10",
            "Timeout": "60"
        }
    }

  },
  "Outputs":{
   "Function" : {
      "Description": "Function ARN",
      "Value": {"Fn::GetAtt": ["FirehoseDataProcessingFunction","Arn"]},
      "Export" : { "Name" : {"Fn::Sub": "${AWS::StackName}-Function" }}
    }
  }
}

To create the function follow the steps below:

  1. Save the template file as “central-logging-lambda.json”
  2. Login to logging account and, from the CloudFormation console, select “create new stack”
  3. Import the file “central-logging-lambda.json” and click next
  4. Follow the steps to create the stack and verify successful creation
  5. Take a note of Lambda function arn from the output section

Step 3: Create log destination in logging account

Log destination is used as the target of a subscription from application accounts, log destination can be shared between multiple subscriptions however according to the architecture suggested in this solution all logs streamed to the same destination will be stored in the same S3 location, if you would like to store log data in different hierarchy or in a completely different bucket you need to create separate destinations.

As noted previously, your destination and subscription have to be in the same region

Use the template below to create destination stack in logging account.

{
  "AWSTemplateFormatVersion":"2010-09-09",
  "Description": "Create log destination and required resources",
  "Parameters":{

    "LogBucketName":{
      "Type":"String",
      "Default":"central-log-do-not-delete",
      "Description":"Destination logging bucket"
    },
    "LogS3Location":{
      "Type":"String",
      "Default":"<BU>/<ENV>/<SOURCE_ACCOUNT>/<LOG_TYPE>/",
      "Description":"S3 location for the logs streamed to this destination; example marketing/prod/999999999999/flow-logs/"
    },
    "ProcessingLambdaARN":{
      "Type":"String",
      "Default":"",
      "Description":"CloudWatch logs data processing function"
    },
    "SourceAccount":{
      "Type":"String",
      "Default":"",
      "Description":"Source application account number"
    }
  },
    
  "Resources":{
    "MyStream": {
      "Type": "AWS::Kinesis::Stream",
      "Properties": {
        "Name": {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-Stream"] ]},
        "RetentionPeriodHours" : 48,
        "ShardCount": 1,
        "Tags": [
          {
            "Key": "Solution",
            "Value": "CentralLogging"
          }
       ]
      }
    },
    "LogRole" : {
      "Type"  : "AWS::IAM::Role",
      "Properties" : {
          "AssumeRolePolicyDocument" : {
              "Statement" : [ {
                  "Effect" : "Allow",
                  "Principal" : {
                      "Service" : [ {"Fn::Join": [ "", [ "logs.", { "Ref": "AWS::Region" }, ".amazonaws.com" ] ]} ]
                  },
                  "Action" : [ "sts:AssumeRole" ]
              } ]
          },         
          "Path" : "/service-role/"
      }
    },
      
    "LogRolePolicy" : {
        "Type" : "AWS::IAM::Policy",
        "Properties" : {
            "PolicyName" : {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-LogPolicy"] ]},
            "PolicyDocument" : {
              "Version": "2012-10-17",
              "Statement": [
                {
                  "Effect": "Allow",
                  "Action": ["kinesis:PutRecord"],
                  "Resource": [{ "Fn::GetAtt" : ["MyStream", "Arn"] }]
                },
                {
                  "Effect": "Allow",
                  "Action": ["iam:PassRole"],
                  "Resource": [{ "Fn::GetAtt" : ["LogRole", "Arn"] }]
                }
              ]
            },
            "Roles" : [ { "Ref" : "LogRole" } ]
        }
    },
      
    "LogDestination" : {
      "Type" : "AWS::Logs::Destination",
      "DependsOn" : ["MyStream","LogRole","LogRolePolicy"],
      "Properties" : {
        "DestinationName": {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-Destination"] ]},
        "RoleArn": { "Fn::GetAtt" : ["LogRole", "Arn"] },
        "TargetArn": { "Fn::GetAtt" : ["MyStream", "Arn"] },
        "DestinationPolicy": { "Fn::Join" : ["",[
		
				"{\"Version\" : \"2012-10-17\",\"Statement\" : [{\"Effect\" : \"Allow\",",
                " \"Principal\" : {\"AWS\" : \"", {"Ref":"SourceAccount"} ,"\"},",
                "\"Action\" : \"logs:PutSubscriptionFilter\",",
                " \"Resource\" : \"", 
                {"Fn::Join": [ "", [ "arn:aws:logs:", { "Ref": "AWS::Region" }, ":" ,{ "Ref": "AWS::AccountId" }, ":destination:",{ "Ref" : "AWS::StackName" },"-Destination" ] ]}  ,"\"}]}"

			]]}
          
          
      }
    },
      
    "S3deliveryStream": {
      "DependsOn": ["S3deliveryRole", "S3deliveryPolicy"],
      "Type": "AWS::KinesisFirehose::DeliveryStream",
      "Properties": {
        "DeliveryStreamName": {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-DeliveryStream"] ]},
        "DeliveryStreamType": "KinesisStreamAsSource",
        "KinesisStreamSourceConfiguration": {
            "KinesisStreamARN": { "Fn::GetAtt" : ["MyStream", "Arn"] },
            "RoleARN": {"Fn::GetAtt" : ["S3deliveryRole", "Arn"] }
        },
        "ExtendedS3DestinationConfiguration": {
          "BucketARN": {"Fn::Join" : [ "", ["arn:aws:s3:::",{"Ref":"LogBucketName"}] ]},
          "BufferingHints": {
            "IntervalInSeconds": "60",
            "SizeInMBs": "50"
          },
          "CompressionFormat": "UNCOMPRESSED",
          "Prefix": {"Ref": "LogS3Location"},
          "RoleARN": {"Fn::GetAtt" : ["S3deliveryRole", "Arn"] },
          "ProcessingConfiguration" : {
              "Enabled": "true",
              "Processors": [
              {
                "Parameters": [ 
                { 
                    "ParameterName": "LambdaArn",
                    "ParameterValue": {"Ref":"ProcessingLambdaARN"}
                }],
                "Type": "Lambda"
              }]
          }
        }

      }
    },
      
    "S3deliveryRole": {
      "Type": "AWS::IAM::Role",
      "Properties": {
        "AssumeRolePolicyDocument": {
          "Version": "2012-10-17",
          "Statement": [
            {
              "Sid": "",
              "Effect": "Allow",
              "Principal": {
                "Service": "firehose.amazonaws.com"
              },
              "Action": "sts:AssumeRole",
              "Condition": {
                "StringEquals": {
                  "sts:ExternalId": {"Ref":"AWS::AccountId"}
                }
              }
            }
          ]
        }
      }
    },
      
    "S3deliveryPolicy": {
      "Type": "AWS::IAM::Policy",
      "Properties": {
        "PolicyName": {"Fn::Join" : [ "", [{ "Ref" : "AWS::StackName" },"-FirehosePolicy"] ]},
        "PolicyDocument": {
          "Version": "2012-10-17",
          "Statement": [
            {
              "Effect": "Allow",
              "Action": [
                "s3:AbortMultipartUpload",
                "s3:GetBucketLocation",
                "s3:GetObject",
                "s3:ListBucket",
                "s3:ListBucketMultipartUploads",
                "s3:PutObject"
              ],
              "Resource": [
                {"Fn::Join": ["", [ {"Fn::Join" : [ "", ["arn:aws:s3:::",{"Ref":"LogBucketName"}] ]}]]},
                {"Fn::Join": ["", [ {"Fn::Join" : [ "", ["arn:aws:s3:::",{"Ref":"LogBucketName"}] ]}, "*"]]}
              ]
            },
            {
              "Effect": "Allow",
              "Action": [
                "lambda:InvokeFunction",
                "lambda:GetFunctionConfiguration",
                "logs:PutLogEvents",
                "kinesis:DescribeStream",
                "kinesis:GetShardIterator",
                "kinesis:GetRecords",
                "kms:Decrypt"
              ],
              "Resource": "*"
            }
          ]
        },
        "Roles": [{"Ref": "S3deliveryRole"}]
      }
    }

  },
  "Outputs":{
      
   "Destination" : {
      "Description": "Destination",
      "Value": {"Fn::Join": [ "", [ "arn:aws:logs:", { "Ref": "AWS::Region" }, ":" ,{ "Ref": "AWS::AccountId" }, ":destination:",{ "Ref" : "AWS::StackName" },"-Destination" ] ]},
      "Export" : { "Name" : {"Fn::Sub": "${AWS::StackName}-Destination" }}
    }

  }
} 

To create log your destination and all required resources, follow these steps:

  1. Save your template as “central-logging-destination.json”
  2. Login to your logging account and, from the CloudFormation console, select “create new stack”
  3. Import the file “central-logging-destination.json” and click next
  4. Fill in the parameters to configure the log destination and click Next
  5. Follow the default steps to create the stack and verify successful creation
    1. Bucket name is the same as in the “create central logging bucket” step
    2. LogS3Location is the directory hierarchy for saving log data that will be delivered to this destination
    3. ProcessingLambdaARN is as created in “create data processing Lambda function” step
    4. SourceAccount is the application account number where the subscription will be created
  6. Take a note of destination ARN as it appears in outputs section as you did above.

Step 4: Create the log subscription in your application account

In this section, we will create the subscription filter in one of the application accounts to stream logs from the CloudWatch log group to the log destination that was created in your logging account.

Create log subscription filter

The subscription filter is created between the CloudWatch log group and a destination endpoint. Asubscription could be filtered to send part (or all) of the logs in the log group. For example,you can create a subscription filter to stream only flow logs with status REJECT.

Use the CloudFormation template below to create subscription filter. Subscription filter and log destination must be in the same region.

{
  "AWSTemplateFormatVersion":"2010-09-09",
  "Description": "Create log subscription filter for a specific Log Group",
  "Parameters":{

    "DestinationARN":{
      "Type":"String",
      "Default":"",
      "Description":"ARN of logs destination"
    },
    "LogGroupName":{
      "Type":"String",
      "Default":"",
      "Description":"Name of LogGroup to forward logs from"
    },
    "FilterPattern":{
      "Type":"String",
      "Default":"",
      "Description":"Filter pattern to filter events to be sent to log destination; Leave empty to send all logs"
    }
  },
    
  "Resources":{
    "SubscriptionFilter" : {
      "Type" : "AWS::Logs::SubscriptionFilter",
      "Properties" : {
        "LogGroupName" : { "Ref" : "LogGroupName" },
        "FilterPattern" : { "Ref" : "FilterPattern" },
        "DestinationArn" : { "Ref" : "DestinationARN" }
      }
    }
  }
}

To create a subscription filter for one of CloudWatch log groups in your application account, follow the steps below:

  1. Save the template as “central-logging-subscription.json”
  2. Login to your application account and, from the CloudFormation console, select “create new stack”
  3. Select the file “central-logging-subscription.json” and click next
  4. Fill in the parameters as appropriate to your environment as you did above
    a.  DestinationARN is the value of obtained in “create log destination in logging account” step
    b.  FilterPatterns is the filter value for log data to be streamed to your logging account (leave empty to stream all logs in the selected log group)
    c.  LogGroupName is the log group as it appears under CloudWatch Logs
  5. Verify successful creation of the subscription

This completes the deployment process in both the logging- and application-account side. After a few minutes, log data will be streamed to the central-logging destination defined in your logging account.

Step 5: Analyzing log data

Once log data is centralized, it opens the door to run analytics on the consolidated data for business or security reasons. One of the powerful services that AWS offers is Amazon Athena.

Amazon Athena allows you to query data in S3 using standard SQL.

Follow the steps below to create a simple table and run queries on the flow logs data that has been collected from your application accounts

  1. Login to your logging account and from the Amazon Athena console, use the DDL below in your query  editor to create a new table

CREATE EXTERNAL TABLE IF NOT EXISTS prod_vpc_flow_logs (

Version INT,

Account STRING,

InterfaceId STRING,

SourceAddress STRING,

DestinationAddress STRING,

SourcePort INT,

DestinationPort INT,

Protocol INT,

Packets INT,

Bytes INT,

StartTime INT,

EndTime INT,

Action STRING,

LogStatus STRING

)

ROW FORMAT SERDE ‘org.apache.hadoop.hive.serde2.RegexSerDe’

WITH SERDEPROPERTIES (

“input.regex” = “^([^ ]+)\\s+([0-9]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([^ ]+)\\s+([0-9]+)\\s+([0-9]+)\\s+([^ ]+)\\s+([^ ]+)$”)

LOCATION ‘s3://central-logging-company-do-not-delete/’;

2. Click ”run query” and verify a successful run/ This creates the table “prod_vpc_flow_logs”

3. You can then run queries against the table data as below:

Conclusion

By following the steps I’ve outlined, you will build a central logging solution to stream CloudWatch logs from one application account to a central logging account. This solution is repeatable and could be deployed multiple times for multiple accounts and logging requirements.

 

About the Author

Mahmoud Matouk is a Senior Cloud Infrastructure Architect. He works with our customers to help accelerate migration and cloud adoption at the enterprise level.

 

Spectre and Meltdown Attacks Against Microprocessors

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2018/01/spectre_and_mel_1.html

The security of pretty much every computer on the planet has just gotten a lot worse, and the only real solution — which of course is not a solution — is to throw them all away and buy new ones.

On Wednesday, researchers just announced a series of major security vulnerabilities in the microprocessors at the heart of the world’s computers for the past 15-20 years. They’ve been named Spectre and Meltdown, and they have to do with manipulating different ways processors optimize performance by rearranging the order of instructions or performing different instructions in parallel. An attacker who controls one process on a system can use the vulnerabilities to steal secrets elsewhere on the computer. (The research papers are here and here.)

This means that a malicious app on your phone could steal data from your other apps. Or a malicious program on your computer — maybe one running in a browser window from that sketchy site you’re visiting, or as a result of a phishing attack — can steal data elsewhere on your machine. Cloud services, which often share machines amongst several customers, are especially vulnerable. This affects corporate applications running on cloud infrastructure, and end-user cloud applications like Google Drive. Someone can run a process in the cloud and steal data from every other users on the same hardware.

Information about these flaws has been secretly circulating amongst the major IT companies for months as they researched the ramifications and coordinated updates. The details were supposed to be released next week, but the story broke early and everyone is scrambling. By now all the major cloud vendors have patched their systems against the vulnerabilities that can be patched against.

“Throw it away and buy a new one” is ridiculous security advice, but it’s what US-CERT recommends. It is also unworkable. The problem is that there isn’t anything to buy that isn’t vulnerable. Pretty much every major processor made in the past 20 years is vulnerable to some flavor of these vulnerabilities. Patching against Meltdown can degrade performance by almost a third. And there’s no patch for Spectre; the microprocessors have to be redesigned to prevent the attack, and that will take years. (Here’s a running list of who’s patched what.)

This is bad, but expect it more and more. Several trends are converging in a way that makes our current system of patching security vulnerabilities harder to implement.

The first is that these vulnerabilities affect embedded computers in consumer devices. Unlike our computer and phones, these systems are designed and produced at a lower profit margin with less engineering expertise. There aren’t security teams on call to write patches, and there often aren’t mechanisms to push patches onto the devices. We’re already seeing this with home routers, digital video recorders, and webcams. The vulnerability that allowed them to be taken over by the Mirai botnet last August simply can’t be fixed.

The second is that some of the patches require updating the computer’s firmware. This is much harder to walk consumers through, and is more likely to permanently brick the device if something goes wrong. It also requires more coordination. In November, Intel released a firmware update to fix a vulnerability in its Management Engine (ME): another flaw in its microprocessors. But it couldn’t get that update directly to users; it had to work with the individual hardware companies, and some of them just weren’t capable of getting the update to their customers.

We’re already seeing this. Some patches require users to disable the computer’s password, which means organizations can’t automate the patch. Some antivirus software blocks the patch, or — worse — crashes the computer. This results in a three-step process: patch your antivirus software, patch your operating system, and then patch the computer’s firmware.

The final reason is the nature of these vulnerabilities themselves. These aren’t normal software vulnerabilities, where a patch fixes the problem and everyone can move on. These vulnerabilities are in the fundamentals of how the microprocessor operates.

It shouldn’t be surprising that microprocessor designers have been building insecure hardware for 20 years. What’s surprising is that it took 20 years to discover it. In their rush to make computers faster, they weren’t thinking about security. They didn’t have the expertise to find these vulnerabilities. And those who did were too busy finding normal software vulnerabilities to examine microprocessors. Security researchers are starting to look more closely at these systems, so expect to hear about more vulnerabilities along these lines.

Spectre and Meltdown are pretty catastrophic vulnerabilities, but they only affect the confidentiality of data. Now that they — and the research into the Intel ME vulnerability — have shown researchers where to look, more is coming — and what they’ll find will be worse than either Spectre or Meltdown. There will be vulnerabilities that will allow attackers to manipulate or delete data across processes, potentially fatal in the computers controlling our cars or implanted medical devices. These will be similarly impossible to fix, and the only strategy will be to throw our devices away and buy new ones.

This isn’t to say you should immediately turn your computers and phones off and not use them for a few years. For the average user, this is just another attack method amongst many. All the major vendors are working on patches and workarounds for the attacks they can mitigate. All the normal security advice still applies: watch for phishing attacks, don’t click on strange e-mail attachments, don’t visit sketchy websites that might run malware on your browser, patch your systems regularly, and generally be careful on the Internet.

You probably won’t notice that performance hit once Meltdown is patched, except maybe in backup programs and networking applications. Embedded systems that do only one task, like your programmable thermostat or the computer in your refrigerator, are unaffected. Small microprocessors that don’t do all of the vulnerable fancy performance tricks are unaffected. Browsers will figure out how to mitigate this in software. Overall, the security of the average Internet-of-Things device is so bad that this attack is in the noise compared to the previously known risks.

It’s a much bigger problem for cloud vendors; the performance hit will be expensive, but I expect that they’ll figure out some clever way of detecting and blocking the attacks. All in all, as bad as Spectre and Meltdown are, I think we got lucky.

But more are coming, and they’ll be worse. 2018 will be the year of microprocessor vulnerabilities, and it’s going to be a wild ride.

Note: A shorter version of this essay previously appeared on CNN.com. My previous blog post on this topic contains additional links.

How to Manage Amazon GuardDuty Security Findings Across Multiple Accounts

Post Syndicated from Tom Stickle original https://aws.amazon.com/blogs/security/how-to-manage-amazon-guardduty-security-findings-across-multiple-accounts/

Introduced at AWS re:Invent 2017, Amazon GuardDuty is a managed threat detection service that continuously monitors for malicious or unauthorized behavior to help you protect your AWS accounts and workloads. In an AWS Blog post, Jeff Barr shows you how to enable GuardDuty to monitor your AWS resources continuously. That blog post shows how to get started with a single GuardDuty account and provides an overview of the features of the service. Your security team, though, will probably want to use GuardDuty to monitor a group of AWS accounts continuously.

In this post, I demonstrate how to use GuardDuty to monitor a group of AWS accounts and have their findings routed to another AWS account—the master account—that is owned by a security team. The method I demonstrate in this post is especially useful if your security team is responsible for monitoring a group of AWS accounts over which it does not have direct access—known as member accounts. In this solution, I simplify the work needed to enable GuardDuty in member accounts and configure findings by simplifying the process, which I do by enabling GuardDuty in the master account and inviting member accounts.

Enable GuardDuty in a master account and invite member accounts

To get started, you must enable GuardDuty in the master account, which will receive GuardDuty findings. The master account should be managed by your security team, and it will display the findings from all member accounts. The master account can be reverted later by removing any member accounts you add to it. Adding member accounts is a two-way handshake mechanism to ensure that administrators from both the master and member accounts formally agree to establish the relationship.

To enable GuardDuty in the master account and add member accounts:

  1. Navigate to the GuardDuty console.
  2. In the navigation pane, choose Accounts.
    Screenshot of the Accounts choice in the navigation pane
  1. To designate this account as the GuardDuty master account, start adding member accounts:
    • You can add individual accounts by choosing Add Account, or you can add a list of accounts by choosing Upload List (.csv).
  1. Now, add the account ID and email address of the member account, and choose Add. (If you are uploading a list of accounts, choose Browse, choose the .csv file with the member accounts [one email address and account ID per line], and choose Add accounts.)
    Screenshot of adding an account

For security reasons, AWS checks to make sure each account ID is valid and that you’ve entered each member account’s email address that was used to create the account. If a member account’s account ID and email address do not match, GuardDuty does not send an invitation.
Screenshot showing the Status of Invite

  1. After you add all the member accounts you want to add, you will see them listed in the Member accounts table with a Status of Invite. You don’t have to individually invite each account—you can choose a group of accounts and when you choose to invite one account in the group, all accounts are invited.
  2. When you choose Invite for each member account:
    1. AWS checks to make sure the account ID is valid and the email address provided is the email address of the member account.
    2. AWS sends an email to the member account email address with a link to the GuardDuty console, where the member account owner can accept the invitation. You can add a customized message from your security team. Account owners who receive the invitation must sign in to their AWS account to accept the invitation. The service also sends an invitation through the AWS Personal Health Dashboard in case the member email address is not monitored. This invitation appears in the member account under the AWS Personal Health Dashboard alert bell on the AWS Management Console.
    3. A pending-invitation indicator is shown on the GuardDuty console of the member account, as shown in the following screenshot.
      Screenshot showing the pending-invitation indicator

When the invitation is sent by email, it is sent to the account owner of the GuardDuty member account.
Screenshot of the invitation sent by email

The account owner can click the link in the email invitation or the AWS Personal Health Dashboard message, or the account owner can sign in to their account and navigate to the GuardDuty console. In all cases, the member account displays the pending invitation in the member account’s GuardDuty console with instructions for accepting the invitation. The GuardDuty console walks the account owner through accepting the invitation, including enabling GuardDuty if it is not already enabled.

If you prefer to work in the AWS CLI, you can enable GuardDuty and accept the invitation. To do this, call CreateDetector to enable GuardDuty, and then call AcceptInvitation, which serves the same purpose as accepting the invitation in the GuardDuty console.

  1. After the member account owner accepts the invitation, the Status in the master account is changed to Monitored. The status helps you track the status of each AWS account that you invite.
    Screenshot showing the Status change to Monitored

You have enabled GuardDuty on the member account, and all findings will be forwarded to the master account. You can now monitor the findings about GuardDuty member accounts from the GuardDuty console in the master account.

The member account owner can see GuardDuty findings by default and can control all aspects of the experience in the member account with AWS Identity and Access Management (IAM) permissions. Users with the appropriate permissions can end the multi-account relationship at any time by toggling the Accept button on the Accounts page. Note that ending the relationship changes the Status of the account to Resigned and also triggers a security finding on the side of the master account so that the security team knows the member account is no longer linked to the master account.

Working with GuardDuty findings

Most security teams have ticketing systems, chat operations, security information event management (SIEM) systems, or other security automation systems to which they would like to push GuardDuty findings. For this purpose, GuardDuty sends all findings as JSON-based messages through Amazon CloudWatch Events, a scalable service to which you can subscribe and to which AWS services can stream system events. To access these events, navigate to the CloudWatch Events console and create a rule that subscribes to the GuardDuty-related findings. You then can assign a target such as Amazon Kinesis Data Firehose that can place the findings in a number of services such as Amazon S3. The following screenshot is of the CloudWatch Events console, where I have a rule that pulls all events from GuardDuty and pushes them to a preconfigured AWS Lambda function.

Screenshot of a CloudWatch Events rule

The following example is a subset of GuardDuty findings that includes relevant context and information about the nature of a threat that was detected. In this example, the instanceId, i-00bb62b69b7004a4c, is performing Secure Shell (SSH) brute-force attacks against IP address 172.16.0.28. From a Lambda function, you can access any of the following fields such as the title of the finding and its description, and send those directly to your ticketing system.

Example GuardDuty findings

You can use other AWS services to build custom analytics and visualizations of your security findings. For example, you can connect Kinesis Data Firehose to CloudWatch Events and write events to an S3 bucket in a standard format, which can be encrypted with AWS Key Management Service and then compressed. You also can use Amazon QuickSight to build ad hoc dashboards by using AWS Glue and Amazon Athena. Similarly, you can place the data from Kinesis Data Firehose in Amazon Elasticsearch Service, with which you can use tools such as Kibana to build your own visualizations and dashboards.

Like most other AWS services, GuardDuty is a regional service. This means that when you enable GuardDuty in an AWS Region, all findings are generated and delivered in that region. If you are regulated by a compliance regime, this is often an important requirement to ensure that security findings remain in a specific jurisdiction. Because customers have let us know they would prefer to be able to enable GuardDuty globally and have all findings aggregated in one place, we intend to give the choice of regional or global isolation as we evolve this new service.

Summary

In this blog post, I have demonstrated how to use GuardDuty to monitor a group of GuardDuty member accounts and aggregate security findings in a central master GuardDuty account. You can use this solution whether or not you have direct control over the member accounts.

If you have comments about this blog post, submit them in the “Comments” section below. If you have questions about using GuardDuty, start a thread in the GuardDuty forum or contact AWS Support.

-Tom

Rapid7 Nexpose Community Edition – Free Vulnerability Scanner

Post Syndicated from Darknet original https://www.darknet.org.uk/2017/09/rapid7-nexpose-community-edition-free-vulnerability-scanner/?utm_source=rss&utm_medium=social&utm_campaign=darknetfeed

Rapid7 Nexpose Community Edition – Free Vulnerability Scanner

Rapid7 Nexpose Community Edition is a free vulnerability scanner & security risk intelligence solution designed for organizations with large networks, prioritize and manage risk effectively.

It proactively supports the entire vulnerability management lifecycle, including discovery, detection, verification, risk classification, impact analysis, reporting and mitigation.

Nexpose Community Edition Features

Data breaches are growing at an alarming rate. Your attack surface is constantly changing, the adversary is becoming more nimble than your security teams, and your board wants to know what you are doing about it.

Read the rest of Rapid7 Nexpose Community Edition – Free Vulnerability Scanner now! Only available at Darknet.

Top Ten Ways to Protect Yourself Against Phishing Attacks

Post Syndicated from Roderick Bauer original https://www.backblaze.com/blog/top-ten-ways-protect-phishing-attacks/

It’s hard to miss the increasing frequency of phishing attacks in the news. Earlier this year, a major phishing attack targeted Google Docs users, and attempted to compromise at least one million Google Docs accounts. Experts say the “phish” was convincing and sophisticated, and even people who thought they would never be fooled by a phishing attack were caught in its net.

What is phishing?

Phishing attacks use seemingly trustworthy but malicious emails and websites to obtain your personal account or banking information. The attacks are cunning and highly effective because they often appear to come from an organization or business you actually use. The scam comes into play by tricking you into visiting a website you believe belongs to the trustworthy organization, but in fact is under the control of the phisher attempting to extract your private information.

Phishing attacks are once again in the news due to a handful of high profile ransomware incidents. Ransomware invades a user’s computer, encrypts their data files, and demands payment to decrypt the files. Ransomware most often makes its way onto a user’s computer through a phishing exploit, which gives the ransomware access to the user’s computer.

The best strategy against phishing is to scrutinize every email and message you receive and never to get caught. Easier said than done—even smart people sometimes fall victim to a phishing attack. To minimize the damage in an event of a phishing attack, backing up your data is the best ultimate defense and should be part of your anti-phishing and overall anti-malware strategy.

How do you recognize a phishing attack?

A phishing attacker may send an email seemingly from a reputable credit card company or financial institution that requests account information, often suggesting that there is a problem with your account. When users respond with the requested information, attackers can use it to gain access to the accounts.

The image below is a mockup of how a phishing attempt might appear. In this example, courtesy of Wikipedia, the bank is fictional, but in a real attempt the sender would use an actual bank, perhaps even the bank where the targeted victim does business. The sender is attempting to trick the recipient into revealing confidential information by getting the victim to visit the phisher’s website. Note the misspelling of the words “received” and “discrepancy” as recieved and discrepency. Misspellings sometimes are indications of a phishing attack. Also note that although the URL of the bank’s webpage appears to be legitimate, the hyperlink would actually take you to the phisher’s webpage, which would be altogether different from the URL displayed in the message.

By Andrew Levine – en:Image:PhishingTrustedBank.png, Public Domain, https://commons.wikimedia.org/w/index.php?curid=549747

Top ten ways to protect yourself against phishing attacks

  1. Always think twice when presented with a link in any kind of email or message before you click on it. Ask yourself whether the sender would ask you to do what it is requesting. Most banks and reputable service providers won’t ask you to reveal your account information or password via email. If in doubt, don’t use the link in the message and instead open a new webpage and go directly to the known website of the organization. Sign in to the site in the normal manner to verify that the request is legitimate.
  2. A good precaution is to always hover over a link before clicking on it and observe the status line in your browser to verify that the link in the text and the destination link are in fact the same.
  3. Phishers are clever, and they’re getting better all the time, and you might be fooled by a simple ruse to make you think the link is one you recognize. Links can have hard-to-detect misspellings that would result in visiting a site very different than what you expected.
  4. Be wary even of emails and message from people you know. It’s very easy to spoof an email so it appears to come from someone you know, or to create a URL that appears to be legitimate, but isn’t.

For example, let’s say that you work for roughmedia.com and you get an email from Chuck in accounting ([email protected]) that has an attachment for you, perhaps a company form you need to fill out. You likely wouldn’t notice in the sender address that the phisher has replaced the “m” in media with an “r” and an “n” that look very much like an “m.” You think it’s good old Chuck in finance and it’s actually someone “phishing” for you to open the attachment and infect your computer. This type of attack is known as “spear phishing” because it’s targeted at a specific individual and is using social engineering—specifically familiarity with the sender—as part of the scheme to fool you into trusting the attachment. This technique is by far the most successful on the internet today. (This example is based on Gimlet Media’s Reply All Podcast Episode, “What Kind of Idiot Gets Phished?“)

  1. Use anti-malware software, but don’t rely on it to catch all attacks. Phishers change their approach often to keep ahead of the software attack detectors.
  2. If you are asked to enter any valuable information, only do so if you’re on a secure connection. Look for the “https” prefix before the site URL, indicating the site is employing SSL (Secure Socket Layer). If there is no “s” after “http,” it’s best not to enter any confidential information.
By Fabio Lanari – Internet1.jpg by Rock1997 modified., GFDL, https://commons.wikimedia.org/w/index.php?curid=20995390
  1. Avoid logging in to online banks and similar services via public Wi-Fi networks. Criminals can compromise open networks with man-in-the-middle attacks that capture your information or spoof website addresses over the connection and redirect you to a fake page they control.
  2. Email, instant messaging, and gaming social channels are all possible vehicles to deliver phishing attacks, so be vigilant!
  3. Lay the foundation for a good defense by choosing reputable tech vendors and service providers that respect your privacy and take steps to protect your data. At Backblaze, we have full-time security teams constantly looking for ways to improve our security.
  4. When it is available, always take advantage of multi-factor verification to protect your accounts. The standard categories used for authentication are 1) something you know (e.g. your username and password), 2) something you are (e.g. your fingerprint or retina pattern), and 3) something you have (e.g. an authenticator app on your smartphone). An account that allows only a single factor for authentication is more susceptible to hacking than one that supports multiple factors. Backblaze supports multi-factor authentication to protect customer accounts.

Be a good internet citizen, and help reduce phishing and other malware attacks by notifying the organization being impersonated in the phishing attempt, or by forwarding suspicious messages to the Federal Trade Commission at [email protected]. Some email clients and services, such as Microsoft Outlook and Google Gmail, give you the ability to easily report suspicious emails. Phishing emails misrepresenting Apple can be reported to [email protected].

Backing up your data is an important part of a strong defense against phishing and other malware

The best way to avoid becoming a victim is to be vigilant against suspicious messages and emails, but also to assume that no matter what you do, it is very possible that your system will be compromised. Even the most sophisticated and tech-savvy of us can be ensnared if we are tired, in a rush, or just unfamiliar with the latest methods hackers are using. Remember that hackers are working full-time on ways to fool us, so it’s very difficult to keep ahead of them.

The best defense is to make sure that any data that could compromised by hackers—basically all of the data that is reachable via your computer—is not your only copy. You do that by maintaining an active and reliable backup strategy.

Files that are backed up to cloud storage, such as with Backblaze, are not vulnerable to attacks on your local computer in the way that local files, attached drives, network drives, or sync services like Dropbox that have local directories on your computer are.

In the event that your computer is compromised and your files are lost or encrypted, you can recover your files if you have a cloud backup that is beyond the reach of attacks on your computer.

The post Top Ten Ways to Protect Yourself Against Phishing Attacks appeared first on Backblaze Blog | Cloud Storage & Cloud Backup.

Perform More Productive Audits of Your AWS Resources by Using the New AWS Auditor Learning Path

Post Syndicated from Jodi Scrofani original https://aws.amazon.com/blogs/security/perform-more-productive-audits-of-your-aws-resources-by-using-the-new-aws-auditor-learning-path/

Auditing image

AWS customers in highly regulated industries such as financial services and healthcare tend to undergo frequent security audits. To help make these audits more productive, AWS has released the AWS Auditor Learning Path. This set of online and in-person classes provides foundational and advanced education about implementing security in the AWS Cloud and using AWS tools to gather the information necessary to audit an AWS environment. The Learning Path also includes a set of self-paced labs to help you gain hands-on experience for auditing your use of AWS services.

After completing the AWS Auditor Learning Path, you should have an understanding of how your IT department consumes AWS services and be able to more effectively engage with your compliance and security teams. The Learning Path is specifically designed for:

  • Auditing executives
  • Field auditors
  • Specialized internal auditors

To get started today, see the AWS Auditor Learning Path.

– Jodi

Implementing DevSecOps Using AWS CodePipeline

Post Syndicated from Ramesh Adabala original https://aws.amazon.com/blogs/devops/implementing-devsecops-using-aws-codepipeline/

DevOps is a combination of cultural philosophies, practices, and tools that emphasizes collaboration and communication between software developers and IT infrastructure teams while automating an organization’s ability to deliver applications and services rapidly, frequently, and more reliably.

CI/CD stands for continuous integration and continuous deployment. These concepts represent everything related to automation of application development and the deployment pipeline — from the moment a developer adds a change to a central repository until that code winds up in production.

DevSecOps covers security of and in the CI/CD pipeline, including automating security operations and auditing. The goals of DevSecOps are to:

  • Embed security knowledge into DevOps teams so that they can secure the pipelines they design and automate.
  • Embed application development knowledge and automated tools and processes into security teams so that they can provide security at scale in the cloud.

The Security Cloud Adoption Framework (CAF) whitepaper provides prescriptive controls to improve the security posture of your AWS accounts. These controls are in line with a DevOps blog post published last year about the control-monitor-fix governance model.

Security CAF controls are grouped into four categories:

  • Directive: controls establish the governance, risk, and compliance models on AWS.
  • Preventive: controls protect your workloads and mitigate threats and vulnerabilities.
  • Detective: controls provide full visibility and transparency over the operation of your deployments in AWS.
  • Responsive: controls drive remediation of potential deviations from your security baselines.

To embed the DevSecOps discipline in the enterprise, AWS customers are automating CAF controls using a combination of AWS and third-party solutions.

In this blog post, I will show you how to use a CI/CD pipeline to automate preventive and detective security controls. I’ll use an example that show how you can take the creation of a simple security group through the CI/CD pipeline stages and enforce security CAF controls at various stages of the deployment. I’ll use AWS CodePipeline to orchestrate the steps in a continuous delivery pipeline.

These resources are being used in this example:

  • An AWS CloudFormation template to create the demo pipeline.
  • A Lambda function to perform the static code analysis of the CloudFormation template.
  • A Lambda function to perform dynamic stack validation for the security groups in scope.
  • An S3 bucket as the sample code repository.
  • An AWS CloudFormation source template file to create the security groups.
  • Two VPCs to deploy the test and production security groups.

These are the high-level security checks enforced by the pipeline:

  • During the Source stage, static code analysis for any open security groups. The pipeline will fail if there are any violations.
  • During the Test stage, dynamic analysis to make sure port 22 (SSH) is open only to the approved IP CIDR range. The pipeline will fail if there are any violations.

demo_pipeline1

 

These are the pipeline stages:

1. Source stage: In this example, the pipeline gets the CloudFormation code that creates the security group from S3, the code repository service.

This stage passes the CloudFormation template and pipeline name to a Lambda function, CFNValidateLambda. This function performs the static code analysis. It uses the regular expression language to find patterns and identify security group policy violations. If it finds violations, then Lambda fails the pipeline and includes the violation details.

Here is the regular expression that Lambda function using for static code analysis of the open SSH port:

"^.*Ingress.*(([fF]rom[pP]ort|[tT]o[pP]ort).\s*:\s*u?.(22).*[cC]idr[iI]p.\s*:\s*u?.((0\.){3}0\/0)|[cC]idr[iI]p.\s*:\s*u?.((0\.){3}0\/0).*([fF]rom[pP]ort|[tT]o[pP]ort).\s*:\s*u?.(22))"

2. Test stage: After the static code analysis is completed successfully, the pipeline executes the following steps:

a. Create stack: This step creates the stack in the test VPC, as described in the test configuration.

b. Stack validation: This step triggers the StackValidationLambda Lambda function. It passes the stack name and pipeline name in the event parameters. Lambda validates the security group for the following security controls. If it finds violations, then Lambda deletes the stack, stops the pipeline, and returns an error message.

The following is the sample Python code used by AWS Lambda to check if the SSH port is open to the approved IP CIDR range (in this example, 72.21.196.67/32):

for n in regions:
    client = boto3.client('ec2', region_name=n)
    response = client.describe_security_groups(
        Filters=[{'Name': 'tag:aws:cloudformation:stack-name', 'Values': [stackName]}])
    for m in response['SecurityGroups']:
        if "72.21.196.67/32" not in str(m['IpPermissions']):
            for o in m['IpPermissions']:
                try:
                    if int(o['FromPort']) <= 22 <= int(o['ToPort']):
                        result = False
                        failReason = "Found Security Group with port 22 open to the wrong source IP range"
                        offenders.append(str(m['GroupId']))
                except:
                    if str(o['IpProtocol']) == "-1":
                        result = False
                        failReason = "Found Security Group with port 22 open to the wrong source IP range"
                        offenders.append(str(n) + " : " + str(m['GroupId']))

c. Approve test stack: This step creates a manual approval task for stack review. This step could be eliminated for automated deployments.

d. Delete test stack: After all the stack validations are successfully completed, this step deletes the stack in the test environment to avoid unnecessary costs.

3. Production stage: After the static and dynamic security checks are completed successfully, this stage creates the stack in the production VPC using the production configuration supplied in the template.

a. Create change set: This step creates the change set for the resources in the scope.

b. Execute change set: This step executes the change set and creates/updates the security group in the production VPC.

 

Source code and CloudFormation template

You’ll find the source code at https://github.com/awslabs/automating-governance-sample/tree/master/DevSecOps-Blog-Code

basic-sg-3-cfn.json creates the pipeline in AWS CodePipeline with all the stages previously described. It also creates the static code analysis and stack validation Lambda functions.

The CloudFormation template points to a shared S3 bucket. The codepipeline-lambda.zip file contains the Lambda functions. Before you run the template, upload the zip file to your S3 bucket and then update the CloudFormation template to point to your S3 bucket location.

The CloudFormation template uses the codepipe-single-sg.zip file, which contains the sample security group and test and production configurations. Update these configurations with your VPC details, and then upload the modified zip file to your S3 bucket.

Update these parts of the code to point to your S3 bucket:

 "S3Bucket": {
      "Default": "codepipeline-devsecops-demo",
      "Description": "The name of the S3 bucket that contains the source artifact, which must be in the same region as this stack",
      "Type": "String"
    },
    "SourceS3Key": {
      "Default": "codepipe-single-sg.zip",
      "Description": "The file name of the source artifact, such as myfolder/myartifact.zip",
      "Type": "String"
    },
    "LambdaS3Key": {
      "Default": "codepipeline-lambda.zip",
      "Description": "The file name of the source artifact of the Lambda code, such as myfolder/myartifact.zip",
      "Type": "String"
    },
	"OutputS3Bucket": {
      "Default": "codepipeline-devsecops-demo",
      "Description": "The name of the output S3 bucket that contains the processed artifact, which must be in the same region as this stack",
      "Type": "String"
    },

After the stack is created, AWS CodePipeline executes the pipeline and starts deploying the sample CloudFormation template. In the default template, security groups have wide-open ports (0.0.0.0/0), so the pipeline execution will fail. Update the CloudFormation template in codepipe-single-sg.zip with more restrictive ports and then upload the modified zip file to S3 bucket. Open the AWS CodePipeline console, and choose the Release Change button. This time the pipeline will successfully create the security groups.

demo_pipeline2

You could expand the security checks in the pipeline to include other AWS resources, not just security groups. The following table shows the sample controls you could enforce in the pipeline using the static and dynamic analysis Lambda functions.

demo_pipeline3
If you have feedback about this post, please add it to the Comments section below. If you have questions about implementing the example used in this post, please open a thread on the Developer Tools forum.

Why Are Hackers Winning The Security Game?

Post Syndicated from Darknet original http://feedproxy.google.com/~r/darknethackers/~3/hIv8MSR20dY/

A lot of people and companies get complacent and don’t believe the hackers are winning, but trust me they are. So we have to ask, why are hackers winning the security game? What’s putting them ahead of the security teams and CISOs inside organizations. It’s an old story anyway, the Hackers always win in some […]

The post Why Are Hackers…

Read the full post at darknet.org.uk