CVE-2024-40766: The Patch Fixed the Bug. Nobody Fixed the Configuration., (Tue, Jun 23rd)

This post was originally published on this site

The vulnerability

In August 2024 SonicWall published advisory SNWLID-2024-0015 for CVE-2024-40766. It is an improper access control vulnerability in SonicOS. CVSS 9.3. It affects the management interface and the SSLVPN service on Gen 5, Gen 6 and Gen 7 firewalls. Each generation has its own affected firmware range: Gen 5 running SonicOS 5.9.2.14-12o and older, Gen 6 running 6.5.4.14-109n and older, and Gen 7 running SonicOS 7.0.1-5035 and older. Successful exploitation lets an attacker gain unauthorized access to the firewall. Under certain conditions it crashes the device entirely.

The scope of the affected install base is large. SonicWall reports serving approximately 500,000 businesses across 215 countries and territories. Many of them run SSLVPN as their sole remote access method. Many of them do not have a dedicated security team. That combination is exactly what ransomware operators look for.

Who is exploiting and how

Akira and Fog ransomware groups have been exploiting CVE-2024-40766 since at least September 2024 when Arctic Wolf first reported Akira affiliates compromising SSLVPN accounts on vulnerable devices. By December 2024 Macnica research confirmed that roughly half of organizations listed on Akira and Fog leak sites were running SonicWall and at least 48,933 devices were still publicly exposed and unpatched.

The exploitation has not slowed down. It has escalated in waves. In July-August 2025 Arctic Wolf, Huntress and Bitdefender reported a surge of Akira intrusions targeting Gen 7 firewalls. Rapid7 and Darktrace published corroborating analyses in September and October 2025 respectively. Fog ransomware operators were also observed exploiting the same vulnerability during this period accounting for roughly 25 percent of intrusions while Akira accounted for 75 percent. Researchers initially suspected a zero-day. SonicWall investigated and confirmed with high confidence that the activity still correlated with CVE-2024-40766. Many compromised organizations had migrated from Gen 6 to Gen 7 without resetting local user passwords. Dwell times across both groups were alarmingly short. Arctic Wolf documented encryption occurring in under four hours from initial access with some cases as fast as 55 minutes.
In September 2025 SonicWall confirmed a separate breach of its MySonicWall cloud platform where attackers accessed firewall configuration backup files containing encrypted credentials. SonicWall initially stated that fewer than 5 percent of its customer base was affected. The company later concluded that all backup files had been compromised. Any organization with a MySonicWall account should assume its configuration backup was accessed and its encrypted credentials exposed. In October 2025 Huntress reported over 100 SSLVPN accounts compromised across 16 customer environments in a single wave. The attackers were not brute-forcing. They authenticated rapidly using valid credentials obtained from another source.

The problem deepened in 2026. In February and March 2026 ReliaQuest documented what they assessed as the first in-the-wild exploitation of CVE-2024-12802, a separate authentication bypass vulnerability that allows attackers to bypass MFA on SonicWall SSLVPN appliances. On Gen 6 devices the firmware patch alone does not remediate the flaw. Six additional manual LDAP reconfiguration steps are required. In the environments ReliaQuest investigated the devices appeared patched based on firmware version. They were still fully exploitable. Attackers brute-forced credentials with automated tooling, bypassed MFA without triggering any failed-login alerts, and in one case reached a file server and deployed pre-ransomware staging tools within 30 minutes of VPN access. The session type sess="CLI" in the SonicWall authentication logs was the most consistent early indicator that automated tooling was driving the authentication.

On April 16, 2026 SonicWall Gen 6 devices reached end-of-life. No further firmware updates or security patches will be issued for that hardware generation. Gen 6 devices remain common in production environments especially at small businesses and in networks assembled through mergers and acquisitions. Any Gen 6 device still running SSLVPN is now operating without vendor support on a platform with two actively exploited vulnerabilities and no future remediation path.

The common thread across two years of exploitation is the same. The attackers are not breaking in through novel exploits. They are logging in with valid credentials to accounts that should not exist on devices that were patched but never cleaned up.

What we found on patched firewalls

Figure 1 summarizes the residual risk indicators we found across the audited devices. All identifiers are anonymized.

Figure 1???????
The most common finding was stale local accounts. 12 of 14 firewalls had SSLVPN accounts that did not exist in Active Directory. Some were legacy service accounts from the Gen 6 era. Some were accounts created during onboarding for employees who had left years ago. A few had usernames containing non-printable characters. That last one is a strong indicator of automated account creation by exploitation tooling. A human administrator does not create accounts with null bytes in the username.

The second finding was the most dangerous in practical terms. 11 of 14 firewalls had not rotated local account passwords after the firmware upgrade. The same credentials that may have been exposed through the vulnerability or through the MySonicWall backup incident are still valid. An attacker who collected them six months ago can use them today.

The third finding was the lack of any source-IP restriction on SSLVPN authentication. 10 of 14 firewalls accepted VPN connections from any IP on the planet. No geo-IP filtering. No ASN blocking. Legitimate remote workers connect from residential ISPs in the country where the company operates. Attackers connect from hosting providers and VPS infrastructure. A simple ASN filter would block most automated exploitation without affecting any real user.

The LDAP default user group problem

This finding deserves its own section because it is the most misunderstood and the most impactful. SonicWall firewalls that authenticate users via LDAP have a setting called the Default LDAP User Group. Every user who successfully authenticates through LDAP is automatically placed into this group in addition to whatever groups they belong to in Active Directory. The default group membership is additive. The risk is that it grants permissions the user should not have based on their actual role.

If this default group is mapped to a group that has SSLVPN access then every single Active Directory account with valid credentials can connect to the VPN. The receptionist. The warehouse operator. The service account with a weak password that nobody monitors. The contractor who left two years ago but whose AD account was never purged. If the credentials are valid and the account is active in AD the tunnel opens.

We found this misconfiguration in 9 of 14 firewalls. In one case the Default LDAP User Group was mapped to a group that had both SSLVPN Services and administrative access to the firewall management interface. That means any compromised AD credential from any source gives the attacker a VPN tunnel into the network and admin access to the perimeter device itself.

SonicWall published a knowledge base article on this specific risk in August 2025 after the Akira campaign drew attention to it. But it is guidance, not a patch. If an administrator does not read the article and manually reconfigure the setting then the overpermissive default stays exactly where it was. The fix is to create a dedicated local group with no service access and set that as the Default LDAP User Group. Then assign SSLVPN permissions explicitly and only to the groups that need them.

The virtual office portal bypass

Half of the firewalls we audited had the SonicWall Virtual Office Portal accessible from the internet without any authentication gate. The Virtual Office Portal is the web interface where SSLVPN users configure their MFA and TOTP tokens. If an attacker has valid credentials and the portal is reachable from the internet then they do not need to break MFA. They can enroll their own TOTP device for that account. They register their authenticator app. They complete the MFA challenge. They are in.

This is how multiple Akira intrusions bypassed MFA on organizations that believed they had it enforced. The attacker never broke the second factor. They set it up themselves on a device they controlled. The fix is a single access rule: restrict the Virtual Office Portal to internal network addresses or VPN-connected sources only. It takes minutes. But it requires knowing the portal exists in the first place and most firewall administrators we spoke with did not know it was publicly exposed.

What the sessions show you

On every audited firewall we exported the SSLVPN session data for the 30-day window after the patch was applied. Figure 2 shows the results from one representative device.

Legitimate user sessions are the green dots clustered at the bottom. Business hours. Duration under eight hours. Source IPs in residential or corporate ISP ranges. Normal.

The orange diamonds are sessions from VPS and hosting provider ASNs. They start during off-hours. Several ran for 40 to 60 hours. No legitimate employee connects to a corporate VPN from a cloud hosting provider for two and a half days straight.

The red squares are the worst. Those are sessions on stale local accounts. Accounts that had been disabled in Active Directory more than a year before the patch was applied. The accounts still existed as local firewall users. They authenticated successfully. They stayed connected for four to seven days. Those sessions were active on a patched firewall. The patch did not terminate them. The patch did not disable the accounts. The patch fixed the access control flaw and left everything else exactly as it was.

What actual remediation looks like

We walked each organization through a post-patch hardening audit. Figure 3 shows the before-and-after for one representative firewall.

That firewall had 23 local SSLVPN accounts. Six were legitimate and stayed. The other 17 were stale, orphaned or unexplained. The LDAP Default User Group was granting implicit SSLVPN access to 147 AD accounts. After reconfiguring the group that number dropped to 28 who actually needed VPN access. All 19 accounts without MFA enforced were either removed or had MFA turned on. Seven active sessions longer than 24 hours were terminated. Three exposed admin interfaces were restricted to management-VLAN-only access.

The patch took five minutes. This cleanup took an afternoon. That afternoon is the gap between a firewall that shows as patched in a vulnerability scanner and a firewall that is actually hardened against the threat actors who know exactly what post-patch misconfigurations to look for.

Post-patch hunting checklist

This is the checklist we used across every firewall we reviewed. It is designed to be run in one sitting by a single administrator with management access to the device.

What to Check

How to Check It

What Bad Looks Like

Local SSLVPN account inventory

Device > Users > Local Users & Groups (SonicOS 7.x). Compare against AD export. Flag any account not in AD.

Accounts with no AD counterpart. Accounts with non-printable or unusual characters in username.

LDAP Default User Group membership

Device > Users > Settings > Authentication. Select LDAP + Local Users then Configure LDAP. Check the Default LDAP User Group assignment.

Default group has SSLVPN Services or admin access. Any LDAP-authenticated user inherits VPN access regardless of AD group.

Password rotation post-upgrade

Confirm all local account passwords and LDAP-synchronized AD account passwords were changed after firmware update.

Passwords carried over from Gen 6 migration. Same credentials active pre- and post-patch. AD service accounts used for LDAP bind not rotated.

Virtual Office Portal exposure

Attempt external access to the Virtual Office Portal URL on the firewall. Check if MFA/TOTP enrollment is publicly accessible.

Portal reachable without auth from external IPs. Attacker can enroll TOTP for any account with known credentials.

Active session review

Network > SSL VPN > Status (SonicOS 7.x). Export and sort by duration and source ASN.

Sessions lasting >24 hours. Source IPs in hosting/VPS ASNs. Sessions from accounts not in current employee roster.

Packet capture and debug artifacts

Device > Diagnostics (SonicOS 7.x). Check for saved packet captures, debug logs, config exports created during compromise window.

Captures or exports created outside maintenance windows. Captures targeting LDAP bind traffic or internal subnets.

Account lockout and botnet filtering

Device > Users > Settings > Account Lockout. Policy > Security Services > Botnet Filter.

Lockout disabled or set to unreasonable thresholds (>20 attempts). Botnet filtering not enabled on SSLVPN zone.

Firmware version target

Device > Settings > Firmware & Backups. Verify running SonicOS 7.3.0 or later. Gen 6 devices: verify all six LDAP steps from SNWLID-2025-0001 are completed.

Running any version below 7.3.0. Gen 6 devices showing as patched by version alone without completed LDAP remediation steps.

Specific indicators of prior exploitation

Beyond the configuration items there are forensic artifacts that indicate the device was accessed by an attacker before the patch was applied:

  • Usernames with non-printable characters. Export the local user list and inspect it in a hex editor or run it through a script that flags any byte outside the ASCII printable range (0x20-0x7E). Automated exploitation tooling sometimes creates accounts with null bytes or control characters that look normal in the web interface but show up in raw exports.
  • Packet captures created during the compromise window. If an attacker gained admin access to the firewall they may have used the built-in packet capture to sniff LDAP bind credentials or internal traffic. Check Device > Diagnostics for saved captures and cross-reference creation timestamps against the incident timeline.
  • TOTP enrollments outside normal onboarding. Review the audit log for MFA/TOTP configuration changes. An attacker who enrolled their own TOTP device will appear as a configuration change against an existing account at an unusual time.
  • Configuration backups exported to unknown destinations. SonicWall config backups contain encrypted credentials. If a backup was exported during the compromise window the attacker can work on cracking the AES-256-encrypted passwords offline. Check system logs for configuration export events.
  • LDAP bind credential exposure. If a local admin account was compromised the attacker could have viewed the LDAP configuration page and obtained the bind password. Rotate the LDAP bind credential as part of the post-patch cleanup. The cost of rotation is low. The cost of a wrong assumption is not.

Detection rules worth implementing now

Here are some alerting rules derived from the patterns observed across the audited firewalls:

  • SSLVPN auth from hosting and VPS ASNs. Build a list of the top 20 hosting provider ASNs and alert on any SSLVPN authentication from them. Legitimate remote workers connect from residential ISPs. Attackers connect from hosting infrastructure. This single rule would have flagged the suspicious sessions in Figure 2 on day one.
  • Session duration exceeding 24 hours. No legitimate VPN session runs for days. Alert on any session over 24 hours. Terminate it. Investigate it.
  • Auth to accounts not in the current HR roster. Join the SSLVPN authentication log against an HR system or AD extract daily. Any successful authentication to a disabled, deleted or unknown account is an incident. Full stop.
  • Rapid multi-account authentication from a single source. Huntress documented attackers authenticating across dozens of accounts in minutes using valid credentials. That is not brute force. That is credential replay. Alert on more than five successful SSLVPN auths from a single source IP within 10 minutes.
  • External access to the Virtual Office Portal. If the portal is restricted to internal addresses then this rule catches bypass attempts. If the portal is still public then this rule shows you how often it is being probed.
  • sess="CLI" session type in VPN authentication logs. ReliaQuest documented in May 2026 that every brute-force attempt they observed against CVE-2024-12802 used this session type in the SonicWall logs. It indicates scripted or automated VPN authentication rather than an interactive user session. After successful access the session type changed to sess="GMS". That transition from CLI to GMS is a strong signal that automated credential testing just turned into hands-on-keyboard activity. Determine first whether any legitimate CLI-based VPN authentication exists in your environment. If none is authorized then any appearance of sess="CLI" is a high-confidence alert. Correlate with Event ID 238 for failed VPN logins and Event ID 1080 for successful SSL VPN zone logins.

Conclusion

CVE-2024-40766 has been public since August 2024. A patch has been available from day one. CVE-2024-12802 adds a second layer of risk for Gen 6 devices where the firmware update alone does not remediate the MFA bypass. And Gen 6 reached end-of-life on April 16, 2026. No more patches are coming for that hardware.

The problem is not that organizations are not patching. Most of them are. The problem is that patching is the only thing they do. They apply the firmware update. They mark the CVE as remediated. They move on. And everything around the vulnerability stays the same. The accounts stay. The LDAP group stays. The passwords stay. The Virtual Office Portal stays open. The sessions stay connected. On Gen 6 devices the LDAP configuration that enables MFA bypass stays in place even after the firmware update because the patch does not remove it.

Akira and Fog operators know all of this. They are not scanning for unpatched firewalls anymore. They are scanning for patched firewalls where nobody completed the post-patch steps. A patched SonicWall with 17 stale local accounts and an overpermissive LDAP default group is not a remediated device. It is a device with newer firmware and the same attack surface.
If you run SonicWall with SSLVPN enabled and you applied the patch for CVE-2024-40766 then you did Step 1. Do Step 2. Run the checklist. Reconcile the accounts. Fix the LDAP group. Rotate the passwords for both local firewall accounts and LDAP-synchronized Active Directory accounts including the LDAP bind credential. Restrict the portal. Terminate the stale sessions. Upgrade to SonicOS 7.3.0 or later which includes enhanced brute-force detection and improved MFA controls that older patched versions do not have.

If you are on Gen 6 verify that all six LDAP remediation steps from SNWLID-2025-0001 have been completed. Firmware version alone does not confirm remediation on Gen 6. The six steps are: delete the existing LDAP configuration that uses userPrincipalName, remove locally cached LDAP users, remove the configured SSL VPN User Domain, reboot the firewall, recreate the LDAP configuration without userPrincipalName, and create a fresh configuration backup to prevent the vulnerable state from being restored. SonicWall has published an automation script that executes these steps via SonicOS API or SSH. Gen 6 reached end-of-life in April 2026. Start planning your migration to supported hardware. The patch closed the bug. Now close the configuration gaps that the bug left behind.

Manuel Humberto Santander Peláez
SANS Internet Storm Center – Handler
Twitter: @manuelsantander
Mastodon:manuelsantander@infosec.exchange
email:msantand@isc.sans.org

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

Run isolated sandboxes with full lifecycle control: AWS Lambda introduces MicroVMs

This post was originally published on this site

Today, we are announcing AWS Lambda MicroVMs, a new serverless compute primitive within AWS Lambda that lets you run code generated by users or AI in isolated, stateful execution environments. You get virtual machine level isolation, near-instant launch and resume, and direct control over environment lifecycle and state, all without managing infrastructure or building expertise in complex virtualization technologies. Lambda MicroVMs are powered by Firecracker, the same lightweight virtualization technology that has powered over 15 trillions of monthly Lambda function invocations.

Why customers need this
Over the past few years a new class of multi-tenant applications has emerged that all share the need to hand each end user their own dedicated execution environment in which to safely run code that the application developer did not write. AI coding assistants, interactive code environments, data analytics platforms, vulnerability scanners, and game servers that run user-supplied scripts all fit this pattern. Building that capability today means making a difficult choice. Virtual machines deliver strong isolation but take minutes to start. Containers launch in seconds, yet their shared-kernel architecture requires significant custom hardening to safely contain untrusted code. Functions as a service are optimized for event-driven, request-response workloads, but are not designed for long-running interactive sessions that need to retain environment state across user interactions. That leaves developers either accepting tradeoffs between performance and isolation, or investing significant engineering resources to build and operate custom virtualization infrastructure to achieve isolated execution while delivering low-latency experiences to end-users. This presents an effort that demands deep expertise and pulls engineering time away from the product they are actually trying to build.

Lambda MicroVMs is purpose-built for exactly this gap. Each MicroVM gives a single end user or session its own isolated environment that launches rapidly, retains memory and disk state for the length of the session, and pauses to a low idle cost when the user steps away. Because the same Firecracker technology already underpins AWS Lambda Functions, you inherit the operational maturity of a service that has been running this stack at scale.

Let’s try it out
To get started, I navigated to the AWS Lambda console, where Lambda MicroVMs now appears in the left-hand navigation menu. I first need to create a MicroVM Image.

I packaged a Flask web app and its Dockerfile into a zip file, uploaded it to an Amazon Simple Storage Service (Amazon S3) bucket.

My Flask API – app.py

import logging

from flask import Flask, jsonify

app = Flask(__name__)
logging.basicConfig(level=logging.INFO)


@app.route("/")
def hello():
    app.logger.info("Received request to hello world endpoint")
    return jsonify(message="Hello, World!")


if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000)

My Dockerfile


FROM public.ecr.aws/lambda/microvms:al2023-minimal
RUN dnf install -y python3 python3-pip && dnf clean all

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY app.py .

EXPOSE 5000

CMD ["gunicorn", "--bind", "0.0.0.0:5000", "app:app"]

I used the following command to create my MicroVM Image.

aws lambda-microvms create-microvm-image 
--code-artifact uri=<path/to/s3/artifact.zip> --name <VM_image_name> 
--base-image-arn arn:aws:lambda:us-east-1:aws:microvm-image:al2023-1 
--build-role-arn <IAM role ARN>

You can also create the MicroVM Image in the AWS Console as in the image above. Once I ran the command, Lambda retrieved the zip, ran the Dockerfile, initialized the application, and took a Firecracker snapshot of the running disk and memory state. Build logs streamed in real time to Amazon CloudWatch under /aws/lambda/microvms/<image-name>, and when the image was ready it appeared in the console with its Amazon Resource Name (ARN) and version number.

aws lambda-microvms run-microvm 
--image-identifier arn:aws:lambda:<region>:<acct>:microvm-image:my-image 
--execution-role-arn arn:aws:iam::<acct>:role/MicroVMExecutionRole 
--idle-policy '{"maxIdleDurationSeconds":900,"suspendedDurationSeconds":300,"autoResumeEnabled":true}'

Launching can also be done via the AWS Console or the CLI. I passed the image ARN and an idle policy configured to auto-suspend after 15 minutes of inactivity and auto-resume on the next incoming request. No networking setup was required. Lambda assigned the MicroVM a unique ID, returned a dedicated endpoint URL, and started a new MicroVM with my Flask app already running, since it was resumed from a snapshot. My Flask app was already running the moment the launch completed. One API call to get a fully initialized, bootstrapped compute environment.

To send traffic, I generated a short-lived auth token with the CLI and attached it to a plain HTTPS request using the X-aws-proxy-auth header. The request landed on my Flask app immediately. I then let the MicroVM sit idle past the suspend threshold, at which point the MicroVM was suspended, with its memory and disk state snapshotted and stored. I then sent another request, and it resumed with the application state fully intact. From the client side, the pause never happened.

How it works
Under the covers, Lambda MicroVMs delivers three capabilities that, until today, no single AWS compute service offered together. The first is virtual machine level isolation, which comes from Firecracker. Each session runs in its own dedicated MicroVM with no shared kernel and no shared resources between users, so untrusted code supplied by one user is contained to their execution environment, without access to other environments or the underlying system. The second is rapid launch and resume. The model is image-then-launch: you create a MicroVM Image by supplying a Dockerfile and code packaged as a zip artifact in Amazon S3, and Lambda runs your Dockerfile, initializes your application, and takes a Firecracker snapshot of the running environment’s memory and disk state. Every subsequent MicroVM launched from that image resumes from the pre-initialized snapshot rather than booting cold, which means launches and idle resumes both achieve near-instant startup latency. Even a multi-gigabyte interactive session comes back online quickly enough to feel responsive to the end user. The third is stateful execution. A running MicroVM retains memory, disk, and running processes across the user’s session. During idle periods, a MicroVM can be suspended – with memory and disk state intact – and resumed when traffic arrives. Installed packages, loaded models, and working filesets are readily available when the user resumes their session. MicroVMs support up to 8 hours of total runtime and can be suspended automatically after a configurable idle window, which makes it straightforward to build products as varied as software vulnerability scans that complete in minutes, data analytics applications that run for hours, and interactive coding sessions with extended idle periods. As Lambda MicroVMs are started from pre-initialized snapshots, applications generating unique content, establishing network connections, or loading ephemeral data during initialization may need to integrate with service-provided hooks for compatibility.

Lambda MicroVMs is a new resource within AWS Lambda, with a distinct API surface. Lambda Functions remain the right choice for event-driven, request-response workloads, and Lambda MicroVMs is purpose-built for multi-tenant applications that need to hand each end user or session their own isolated environment to execute user- or AI-generated code. The two complement each other. An application using Lambda Functions for its event-driven backbone can call into Lambda MicroVMs for the steps that need to run untrusted code in isolation. You bring the application, and the service delivers the execution environment.

Now available
AWS Lambda MicroVMs is available today in the US East (N. Virginia, Ohio), US West (Oregon), Europe (Ireland) and Asia Pacific (Tokyo) Regions, on the ARM64 architecture, with up to 16 vCPUs, 32 GB of memory, and 32 GB of disk per MicroVM. Idle MicroVMs can be suspended explicitly through an API call or automatically through a lifecycle policy, which reduces the running cost while preserving full state for fast resume. Pricing details can be found on the AWS Lambda pricing page.

To get started, visit the AWS Lambda console, or learn more on the Lambda MicroVMs product page. For documentation, see the Lambda MicroVMs Developer Guide.

AWS Weekly Roundup: NY Summit recap, Local Zone in Hanoi, Grok 4.3 in Bedrock, price reductions, and more (June 22, 2026)

This post was originally published on this site

Last week AWS Summit New York City brought together thousands of customers, partners, and builders for a free, one-day event showcasing the latest in cloud and AI innovation. Dr. Swami Sivasubramanian, VP of Agentic AI at AWS unveiled a stack of AI launches in his keynote, all built around one thesis: agents that compound value over time.

  • Agents for working – You can launch autonomous agents and access a smarter activity feed with new Amazon Quick features, which now let you create and run multi-step agents directly in the desktop app and consolidates email, Slack, calendar, and tasks into a single prioritized view with personalized rules.
  • Agents for securing – You can shift from reactive to proactive security with AWS Continuum, a new AI-native security service that reasons, validates, and acts at machine speed across the full code vulnerability lifecycle. AWS Security Agent (now part of AWS Continuum) adds new features: threat modeling; pull request code scanning with remediation across major Git platforms; and IDE integrations via Kiro power, Claude Code plugin, and MCP.
  • Agents for building – You can write, ship, and modernize code in one continuous loop with Kiro, AWS DevOps Agent, and AWS Transform. Kiro introduces a native iOS app; AWS DevOps Agent adds release management capabilities to assess code changes before production; and AWS Transform continuous modernization reduces tech debt autonomously.
  • Agents customers create – You can go from agent idea to production in minutes with Amazon Bedrock AgentCore, which now includes a GA harness for infrastructure and orchestration, Web Search, Managed Knowledge Base, policy integrations with Guardrails, and the new AWS Context service for mapping organizational data relationships.

To learn more, visit the Summit recap from our top announcements blog post and Amazon News post.

Last week’s launches
Here are last week’s launches that caught my attention:

  • AWS Local Zone in Hanoi, Vietnam  —This new Local Zone is one of the first AWS Local Zones in the Asia Pacific with support for Amazon S3 and Amazon EBS Local Snapshots, enabling customers to meet data residency requirements by storing and backing up data locally. To get started, enable the Hanoi Local Zone (ap-southeast-1-han-1a) from the Regions and Zones tab in the AWS Global View or by using the ModifyAvailabilityZoneGroup API.
  • AWS Blocks, an open-source TypeScript framework for application developers (preview) — AWS Blocks runs a fully functional local environment with Postgres, authentication, and real-time messaging, no AWS account required. When you’re ready to deploy, the same application code runs on production AWS services with zero changes, and you can drop into AWS CDK at any point for direct resource configuration.
  • Grok 4.3 from xAI in Amazon Bedrock —You can use the Grok 4.3 model on Amazon Bedrock, giving you even more choice as you build generative AI applications across reasoning, agentic, and enterprise workflows. Grok 4.3 runs on a new inference engine in Bedrock designed for price performance, with support for tool calling, structured output, and response streaming.
  • Amazon S3 annotations: attach rich, queryable context directly to your objects — Amazon S3 now lets you attach up to 1 GB of rich, mutable, and queryable context directly to your objects using annotations, purpose-built for AI agents and autonomous workflows that need to discover, understand, and act on data at scale without maintaining separate metadata systems.
  • Amazon ECS announces faster service auto scaling — Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster).
  • Amazon EC2 G7 instances accelerated by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs — AWS is the first major cloud provider to support NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. G7 instances are accelerated by these GPUs with custom sixth-generation Intel Xeon Scalable processors, delivering up to 4.6x AI inference performance and up to 2.1x graphics performance compared to G6 instances.
  • Strands Agents introduces new capabilities — Strands is an open source toolkit for building production agents. You can now use better context management in Harness SDK, a new isolated execution environment with Strands Shell, and chaos testing and red teaming in Strands Evals.
  • AWS Management Console Private Access – You can access the AWS Console from VPCs without internet connectivity, allowing enterprises to manage their AWS infrastructure through the console while maintaining strict network security controls in air-gapped environments.
  • AWS Marketplace Storefront is now generally available – AWS Partners can create and deploy their own branded catalog of solutions and services on their website or application in hours. Channel Partners and Independent Software Vendors can now simplify how they manage their cloud marketplace business and make it easier for customers to discover and purchase their solutions from AWS Marketplace.
  • Palo Alto Networks (PANW) Advanced DNS Security on Amazon Route 53 Resolver DNS Firewall (preview) – You can now enforce DNS threat protections from Palo Alto Networks directly on Route 53 DNS Firewall rules, without deploying separate firewalls or modifying VPC configurations — by subscribing to PANW from the DNS Firewall console through the embedded AWS Marketplace widget.

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

Price reductions 
AWS continues to look for ways to increase performance and lower prices for our customers. I noticed a few such efforts last week, so I’d like to share them:

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!

Channy

eBanking Phishing Delivered Through IPv4-Mapped IPv6 Address, (Fri, Jun 19th)

This post was originally published on this site

I detected an interesting phishing email this morning. It targets a major Belgian bank:

The phishing in itself is a classic one, not relevant but the malicious link is interesting:

hxxp://[::ffff:5511:74be]/kWC5PHA1

The technique used by the attacker is to bypass simple security controls trying to extract domain names and IP addresses via simple regular expressions. The notation “[…]” tells the URL parser that what's inside is a literal IPv6 address. But it’s not a real IPv6 address. What’s the magic?

The started “::” in the address means that it can be expanded to this address:

0000:0000:0000:0000:0000:ffff:5511:74be

The trick is the fifth group (::ffff:) means that we are facing a IPv5-mapped IPv6 address. This is defined in RFC 4291[1]:

In the URL above, the two trailing 16-bit hex groups “5511” and “74be” are just the four IPv4 octets written in hex.

Hex Dec
0x55 85
0x11 17
0x74 116
0xBE 190

The real URL is therefore:

hxxp://85[.]17[.]116[.]190/kWC5PHA1

Another good news from the attacker’s point of view, there is no DNS record!

When visited, this URL redirects to another link where the real phishing kit is hosted:

hxxps://3439-aanmelden[.]verificatie[.]qzz[.]io/mon-belfius

[1] https://www.rfc-editor.org/info/rfc4291/

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.

Amazon ECS introduces new high-resolution metrics for faster service auto scaling

This post was originally published on this site

Amazon Elastic Container Service (Amazon ECS) service auto scaling automatically adjusts task counts to meet workload demand with comprehensive scaling policies, including predictive scaling for recurring traffic patterns, scheduled scaling for planned events, and target tracking to scale dynamically on real-time metrics.

You can choose proactive scaling by using predictive scaling (automatic) and scheduled scaling (customer-defined), or reactive scaling by using target tracking with just a target to scale on. Amazon ECS service auto scaling adjusts the number of tasks in an ECS service based on Amazon CloudWatch metrics, such as average CPU/Memory usage, request count per target, a custom metric such as queue depth, or demand surges by using advanced machine learning (ML) algorithms.

With today’s launch, Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster, 4.2x), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster, 3.5x)

This launch delivers three key benefits for your applications:

  • Improved performance and reliability: Faster scaling means, your application responds faster to demand surges, reducing latencies or failures for end users during demand surges.
  • Right-size without compromise: Depending on the workload, you can reduce baseline task counts because scale-out now happens fast enough to handle traffic spikes without preemptive capacity padding. This directly reduces compute costs while maintaining application performance and availability.
  • Simpler scaling configuration: Target tracking with high-resolution metrics delivers the aggressive scaling behavior that previously required custom scaling configurations, such as usage of step-scaling policies. One configuration change replaces custom engineering work.

How it works
To use ECS faster service auto scaling, first enable high-resolution metrics for your ECS service, and then configure a target tracking scaling policy which uses high-resolution metrics. ECS faster service autoscaling works across all compute options on ECS: AWS Fargate, ECS Managed Instances, and Amazon Elastic Compute Cloud (Amazon EC2). You can enable these metrics when you create or update your ECS service in the Amazon ECS console, or using AWS SDKs and tools, and AWS CloudFormation.

When you create a service in the console, add 20-seconds resolution metrics in the Monitoring configuration section. These metrics incur additional CloudWatch costs while the standard resolution (60-seconds) is free.

In the Service auto scaling section, check Use service auto scaling and choose Target Tracking for the scaling policy type to use real-time data to scale the number of tasks that your service runs based on demand.

Then, choose a Scaling policy type for the target tracking. You can select ECSServiceAverageCPUUtilizationHighResolution or ECSServiceAverageMemoryUtilizationHighResolution as new metrics.

That’s it – your ECS service will use high resolution metrics for auto scaling.

To update an existing ECS service to use faster auto scaling, you first need to configure high resolution metrics via Update Service. Once deployment completes, your service will generate high-resolution metrics. You can then go to the Service and auto scaling tab from your service details to update scaling policy to use higher resolution metrics.

That’s all you need. Your ECS service now evaluates scaling decisions at 20-second intervals.

You can also use the AWS Command Line Interface (AWS CLI) to enable new metrics in your ECS service through Application Auto Scaling. To learn more, visit the faster auto scaling documentation.

Now available
Faster service autoscaling with high-resolution metrics for Amazon ECS is available today. The feature itself has no additional cost, but high-resolution CloudWatch metrics introduce a new pricing dimension. For details, see the CloudWatch pricing page.

Give it a try today and send feedback to AWS re:Post for ECS or through your usual AWS Support contacts.

Channy

The Behavior of Coordinated SSH Brute Force Attacks over the last three months [Guest Diary], (Wed, Jun 17th)

This post was originally published on this site

[This is a Guest Diary by Adam Nason, an ISC intern as part of the SANS.edu BACS program]

Brute force SSH attacks are an ever-present threat on the internet today. We examine probing behavior over the last three months to identify coordinated and opportunistic attacks by threat actors. A DShield Honeypot has quietly collected and logged the behavior of these threat actors to develop a clearer picture of their malicious intentions. During the log collection period, several significant cyber and geopolitical events occurred. We will take a closer look at these behaviors by analyzing their timing and cross-referencing them with external factors that align with their attack patterns. Can an increase or change in SSH brute force botnet activity be observed during these volatile times?

Infrastructure Setup and Data Collection Framework – Home Lab

Infrastructure
• Raspberry Pi 4 Model B
• Network Equipment – Isolated from personal home network
  o UniFi Security Gateway Pro
  o UniFi 24 Port Switch

Data Ingestion
• RaspberryOS running on Raspberry Pi
• DShield Honeypot Software
  o Logs Collected: 17 Feb 2026 through 26 May 2026

Software Tools
• Elasticsearch, Logstash, Kabana (ELK)
• Microsoft VS Code (JSON and Python)
• Microsoft Excel

Data Analysis of Honeypot Logs

Scanning Volume and Timeline Overview

The cowrie honeypot logs recorded over 20 million SSH brute-force attempts over the past 100 days. Investigation into the scanning trends appears to be closely correlated with Chinese botnets, major law enforcement actions, geopolitical events, and critical cybersecurity advisories released in the first half of 2026. As shown in Figure 1: Daily Brute Force Probing Totals, the timeline shows extended periods of high-volume traffic, with abrupt spikes and drops that seem to align with external events.


Table 1: Overview of SSH data collected


Figure 1: Daily Brute Force Probing Totals

Notable events within the brute force scan timeline

February 17 – 24 (Initial Baseline)

The first week of running the honeypot produced what can be considered a quiet baseline of standard background scanning activity. During this period, between 200 and 400 attempts were captured each day.

February 25 – 28 (Sudden Surge)

Following a quiet start, a spike of over 2100% attempts was observed by the honeypot. It was during this period that CISA published Emergency Directive 26-03 (CISA, 2026), related to Cisco’s software-defined wide-area network, which led to opportunistic attacks against unpatched systems. Additional probing was observed during this period, which can also be attributed to the rising conflict between Iran, Israel, and the United States (Al Jazeera, 2026).

March 1 – 8 (Activity Peaks)

Scanning observations peaked this week, with over 300,000 events collected on March 8th (Radauskas, 2026). This is as tensions continue to rise between Iran, Israel, and the United States, and both advanced persistent threats and opportunistic botnets are becoming more active (Reuters, 2026).

March 9 – April 14 (Sustained Attacks)

The honeypot continues to collect and log a high volume of activity during this period, with daily probes remaining above 50,000, often exceeding 100,000 (Le Poidevin, 2026). The periodic spikes and dips tend to lean towards automated attack campaigns.

April 15 (Rapid Decline)

Attack observations plummet to just over 23,000 attempts logged by the honeypot.

April 16 – May 14 (Attack Rebounds)

With news of new vulnerabilities (CISA, 2026), and tensions growing with the Iran-United States ceasefire, logged scans start to rise again. A second spike is observed on May 2nd with 244,344 probes in a single day. This comes just after 24 hours following a major Linux vulnerability that was published by CISA (CISA, 2026)

May 15 – 23 (Extended Decline)

Daily log observations drop nearly 95%, as the ceasefire extends (Madhani et al., 2026), and opportunistic threat actors lose interest as the Iran, Israel, and United States war continues to drag on with minimal active military engagements.

Top 10 Identified Probing IPs (Patterns, Clusters, and Campaign Data)

From February 2026 through May 2026, the top ten observed IP addresses (Table 2) appeared to have strong geographic and Autonomous System Number (ASN) clustering. Both DigitalOcean (ASN – AS14061) and M247 (ASN – AS9009) show activity from multiple countries (Table 3). Furthermore, synchronized scanning bursts can be observed using identical SSH client fingerprints and version strings, occurring within minutes of each other across different countries.


Table 2: Top Ten Probing IPs, with Country and ASN


Table 3: Country Clustering of IPs and ASN

A closer review of the data shows an example of what appears to be synchronized scanning. Over the course of 53 seconds, two attacks are observed from both the United States and Ukraine. Of note, both attacks exhibit the same HASSH fingerprint. HASSH is a fingerprinting standard developed by the Detection Cloud team at Salesforce and is used to detect attacks with higher granularity than a simple IP address can (Reardon, 2022). Seeing the same HASSH, SSH Version, from two different ASNs and countries does point to a high likelihood of a coordinated attack.


Table 4: Clustered attack within 53-seconds

Further review of the collected honeypot logs shows that 702,706 events use the exact same HASSH fingerprint (03a80b21afa810682a776a7d42e5e6fb) and SSH version, indicating the use of a single managed attack toolkit that has been deployed globally (SSHwatch, 2025).

Finally, a detailed review of the logs shows evidence of a botnet quota assignment (Table 5). These are throttled scan rates, which are tell-tale indicators of a botnet-driven SSH campaign. Reviewing the logs over such extended periods shows a low variation and high uniform attack rates, which point to a controller assigning quotas or workloads to the botnet zombies under its control. Automating a scan in this type of organization has been shown to be a characteristic of these types of programmed botnet operations (Sing et al., 2024 p. 1731-1750).


Table 5: Attack Rate Analysis of IPs showing Automated Campaigns

Reduce your Attack Surface

As we have seen above, networks are under constant attack, which seems to follow the ebb and flow of external events on digital cyberspace. However, there are several steps you can take to reduce your attack surface, most of which require little effort. Strategies like IP blocks, geo-blocking, and changing default values can go a long way in preventing a potential compromise.

Prevention
• While not specifically noted in this paper, a majority of SSH brute force login attempts observed targeted the user ‘root’. Disabling ‘root’ user login would effectively make all those attack attempts obsolete.
• Enforce Multi-Factor Authentication (MFA).
• Use private keys for SSH, provided they are properly protected, rotated, and audited.
• Properly hardened jump boxes to reduce exposure and enable session monitoring with Security Information and Event Management (SIEM) systems.
(Hartman, 2026)

Detection
• Centralized SIEM Log Collection
• Deploy rules to alert to brute force patterns in platforms like Snort or Suricata. This can include ‘SYN Flood Attacks’ against port 22 (or your SSH port if you change the default value).
(Hartman, 2026)

Conclusion

Over 20 million SSH brute force attempts were collected by my DShield honeypot over nearly 100 days. This data was compared with several major cybersecurity advisories, changing geopolitical tensions, and law enforcement activities, which uncovered a close alignment. The top probing IPs showed clear signs of SSH botnet attack campaigns when evaluating the log data for attack timing, rates, HASSH fingerprints, and SSH client identification. These findings help to illustrate the adaptability and persistence of threat actors as they respond to capitalize on external events, highlighting the underlying need of all connected devices to be aware not only of cybersecurity-related advisories but also of the activities taking place across the globe. Some of the simplest security measures would have stopped many of these attacks in their tracks, raising the persistent need to continue to remind users to change default settings (usernames and ports) and increase the use of MFA.

Have you observed similar activities in your honeypot or within your organization? Feel free to share your findings and experiences in the comments below or consider joining the SANS Internet Storm Center and contributing data to their global network of sensors and honeypots.

[1] Al Jazeera. (2026, February 28). US, Israel bomb Iran: A timeline of talks and threats leading up to attacks. https://www.aljazeera.com/news/2026/2/28/us-israel-bomb-iran-a-timeline-of-talks-and-threats-leading-up-to-attacks#:~:text=US%2C%20Israel%20bomb%20Iran%3A,since%20the%20June%202025
[2] CISA. (2026, February 25). ED 26-03: Mitigate vulnerabilities in Cisco SD-WAN systems. Cybersecurity and Infrastructure Security Agency CISA. https://www.cisa.gov/news-events/directives/ed-26-03-mitigate-vulnerabilities-cisco-sd-wan-systems
[3] CISA. (2026, May 1). CISA adds one known exploited vulnerability to catalog. Cybersecurity and Infrastructure Security Agency CISA. https://www.cisa.gov/news-events/alerts/2026/05/01/cisa-adds-one-known-exploited-vulnerability-catalog
[4] CISA. (2026, April 20). CISA adds eight known exploited vulnerabilities to catalog. Cybersecurity and Infrastructure Security Agency CISA. https://cisa.gov/news-events/alerts/2026/04/20/cisa-adds-eight-known-exploited-vulnerabilities-catalog
[5] Hartman, K. G. (2026, February 13). Securing SSH keys in cloud environments: Practical guidance for security, forensics, and legal accountability. SANS Institute. https://www.sans.org/blog/securing-ssh-keys-cloud-environments-practical-guidance-security-forensics-legal-accountability
[6] Le Poidevin, O. (2026, March 9). UAE envoy to UN urges de-escalation of US-Israeli war with Iran. reuters.com. https://www.reuters.com/world/middle-east/uae-envoy-un-urges-de-escalation-us-israeli-war-with-iran-2026-03-09/#:~:text=UAE%20envoy%20to%20UN,and%20a%20return%20to
[7] Madhani, A., Gambrell, J., Price, M., & Metz, S. (2026, May 29). US and Iranian negotiators reach tentative deal to extend ceasefire and start new nuclear talks. AP News. https://apnews.com/article/iran-us-war-oil-may-28-2026-8f5ed2813ba63df7ae9ccbe991688d29
[8] Radauskas, G. (2026, March 3). Wild pack without a leader: pro-Iranian hackers already active in wake of US-Israeli strikes. Cybernews. https://cybernews.com/security/iran-war-cyberattacks-hacking/
[9] Reardon, B. (2022, April 14). Open sourcing HASSH. Salesforce Engineering Blog. https://engineering.salesforce.com/open-sourcing-hassh-abed3ae5044c/
[10] Reuters. (2026, February 28). Global reaction to US, Israeli attacks on Iran. reuters.com. https://www.reuters.com/business/aerospace-defense/global-reaction-israeli-us-attacks-iran-2026-02-28/#:~:text=Global%20reaction%20to%20US%2C,into%20a%20renewed%20military
[11] Singh, S. K., Gautam, S., Cartier, C., Patil, S., & Ricci, R. (2024). Where The Wild Things Are: Brute-Force SSH Attacks In The Wild And How To Stop Them. USENIX Association, 1731-1750. https://www.usenix.org/conference/nsdi24/presentation/singh-sachin
[12] SSHwatch. (2025, April 28). SSH honeypots: Detecting and understanding attack patterns. https://www.sshwatch.com/ssh-honeypots-detecting-and-understanding-attack-patterns/#:~:text=Distributed%20credential%20partitioning%2C%20wordlist,logs%20with%20tools%20like
[13] https://www.sans.edu/cyber-security-programs/bachelors-degree/

Note: To ensure full transparency and academic integrity, generative A.I. software (Grammarly) was used for grammar and spelling checks. No further use of generative A.I. was used in the creation of this writing.

———–
Guy Bruneau IPSS Inc.
My GitHub Page
Twitter: GuyBruneau
gbruneau at isc dot sans dot edu

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

Top announcements of the AWS Summit in New York, 2026

This post was originally published on this site

Today at the AWS Summit in New York City, Swami Sivasubramanian, AWS VP of Agentic AI, provided the day’s keynote.

Here’s our roundup of the biggest announcements from the event:

New in Amazon Bedrock AgentCore
We’re introducing new capabilities on Amazon Bedrock AgentCore: connecting AI agents to organizational, web, and paid knowledge, helping teams find and fix what’s going wrong in production, and enforcing controls that scale as agents grow more capable.

Together, these capabilities help you build more capable agents faster, govern those agents with controls that scale, and improve them continuously. To learn more, read our blog post covering all the new features.

New in AI-based security tools

New in building AI-based applications 

  • Introducing Kiro for iOS — Kiro introduces a native iOS app, available in a gated preview, built for real engineering work that gives developers a new surface to kick off, monitor, steer, and interact with their Kiro sessions directly from their phone. That means you can now start sessions, check back when they’re done, review diffs, and approve changes all while staying connected to your work with no laptop running.
  • AWS DevOps Agent adds release management capabilities to assess code changes before production — You can use a new release readiness review of code changes and autonomous release testing. These new features verify every change against the natural language standards you give to the DevOps Agent and run change-specific tests in production-like environments.
  • Proactively reduce tech debt autonomously with AWS Transform – continuous modernization — You can use continuous analysis (preview) to automatically scan your code repositories against configurable baselines and generates findings in hours, not weeks. Once you’ve identified and prioritized findings, you can configure autonomous remediations that generate pull requests for affected repositories automatically.

In addition to the keynote announcements, we have other important launches this week:

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

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.

Introducing Amazon Bedrock Managed Knowledge Base for faster, more accurate enterprise AI applications

This post was originally published on this site

Today, we’re announcing Amazon Bedrock Managed Knowledge Base, a new set of capabilities that enables developers to build enterprise-grade generative AI applications with their proprietary data in minutes. Organizations building agentic AI applications need secure, reliable, and up-to-date access to enterprise-wide data to deliver accurate, fast, and trusted outcomes. Managed Knowledge Base abstracts away the complexity of building and managing retrieval-augmented generation (RAG) pipelines, allowing developers to focus on business outcomes rather than infrastructure management.

Developers building knowledge bases for their agents face three key challenges today:

  • Connecting to enterprise data – Enterprise knowledge lives across disparate systems with different content types, access control lists, and document formats. Building and maintaining custom connectors for each source adds complexity that slows down development.
  • Optimizing RAG accuracy – Best practices for retrieval-augmented generation keep evolving. Developers need to experiment with different parsing strategies, chunking approaches, embedding models, and agentic retrieval behaviors to get accurate answers from their data.
  • Managing infrastructure at scale – Organizations need to serve large knowledge bases with millions of documents, or manage thousands of smaller knowledge bases across teams. Both patterns require reliable infrastructure, security enforcement, and cost control.

These challenges require developers to repeatedly perform undifferentiated work instead of focusing on their applications.

Amazon Bedrock Managed Knowledge Base addresses these challenges by abstracting away the multiple infrastructure components developers traditionally have to assemble and maintain themselves (storage, retrieval, embeddings, re-ranking, and foundation model selection) into a single managed primitive. By default, the service automatically selects and manages a default embeddings model, re-ranker model, and foundational model on your behalf, so you can get up to speed quickly without needing to pick or maintain one yourself. On top of this managed foundation, three core innovations further improve ease of use and accuracy:

  • Native data connectors – Six pre-built ingestion connectors that natively pull enterprise data and permissions from SaaS applications, eliminating the overhead developers face in managing application-specific requirements. At launch, we support Amazon S3, SharePoint, Confluence, Web Crawler, Google Drive, and OneDrive.
  • Smart Parsing – Different content types and sources require different approaches to achieve accurate retrieval. Smart Parsing handles this complexity automatically, selecting the right parsing strategy for each data type and connector to provide the highest accuracy for your agents.
  • Agentic Retriever – Optimized for complex queries that require multiturn, multihop retrieval within a single knowledge base or across multiple knowledge bases. Agentic Retriever automatically infers end-user intent and draws relevant context from institutional knowledge spread across data sources and modalities.

With just a few lines of code, Amazon Bedrock Managed Knowledge Base automatically manages and scales the end-to-end RAG pipeline that powers your enterprise knowledge agents. For agent builders, it’s available as a pre-built target type in Amazon Bedrock AgentCore Gateway, reducing integration to a few lines of code, auto-generating role-based permissions, and providing observability and evaluation metrics in the AgentCore Observability dashboard.

Getting started with Amazon Bedrock Managed Knowledge Base
Creating a Managed Knowledge Base is straightforward. Navigate to the Amazon Bedrock AgentCore console or the Amazon Bedrock console, open the Knowledge Bases page, and choose Create Managed KB. The experience is the same in both consoles. You will see that Unstructured Vector Store KB is now available as the recommended option, alongside the other knowledge base types you may already be familiar with:

Picture 1 – Knowledge Bases list page in the Amazon Bedrock AgentCore console showing the Type column with different KB types and the Create Managed KB button

When creating a new Knowledge Bases, you can connect to your enterprise data sources by choosing from the list of supported connectors directly from a dropdown. AWS Identity and Access Management (IAM) roles are automatically created, and you can choose to edit these permissions if needed:

Picture 2 – Create Knowledge Base page showing the Data source dropdown expanded with all supported connectors: Amazon S3, Confluence, Custom, Google Drive, One Drive, SharePoint, and Web Crawler

An optimized set of defaults will be presented, allowing you to create your knowledge base in just a few clicks. Once the data is synced, you can integrate the knowledge base with your agent or provide it as a tool for your foundation model and start querying.

Smart Parsing for accurate data ingestion
One of the key challenges in building knowledge bases is preparing diverse data types for accurate retrieval. Once you point Managed Knowledge Base at your data sources, Smart Parsing automatically determines the optimal parsing strategy for each data type and connector, no extra configuration is required.

Smart Parsing combines multiple techniques:

  • Connector-specific data models – Optimized handling for each data source. For example, the Web Crawler connector preserves HTML structure including embedded images and tables, ensuring rich content is not dropped during ingestion. SharePoint connectors maintain document hierarchy and relationships between files.
  • Multimodal processing – Automatic detection and processing of different content types within documents. The system identifies bounding boxes in documents, then sends them to foundation models for data extraction, captioning, and scene description in video files.
  • Optimized chunking – Smart Parsing leverages foundation models to understand document structure and extract meaningful content, ensuring that complex documents with mixed formats are properly indexed. Intelligent defaults balance retrieval accuracy with performance based on document type and content structure, while advanced users can customize chunking strategies when needed.

This automated approach eliminates weeks of experimentation typically required to achieve production-quality retrieval accuracy, while still preserving the flexibility to customize when needed.

Using Agentic Retriever for complex queries
After your data is ingested, you can start querying your knowledge base. Generative AI applications often struggle with complex user queries that require reasoning, recursive multi-step retrieval, and intermediate evaluations of results. Consider a user asking two related questions: “What is the cloud infrastructure budget for the ML platform team?” and “Does our expense policy allow prepaying annual commitments?” A single retrieval step might surface documents about the ML platform team but fail to connect the budget information with the expense policy needed to fully answer the question.

Picture 3 – Agentic Retriever decomposes complex user queries into a step-by-step plan, performing multi-hop retrieval across multiple knowledge bases and combining results to deliver accurate, grounded responses

Agentic Retriever solves this by creating a step-by-step query plan: 1. Which team owns the ML platform, and what is their cloud infrastructure budget? 2. What does the expense policy say about prepaying annual commitments? 3. Does the policy allow the ML platform team to prepay against this budget?

The system performs multi-hop retrieval and reasoning at each step, and once it has gathered sufficient relevant passages, it stops the search process and returns the top results. By abstracting away the complexity of building a separate multi-hop reasoning pipeline, this approach dramatically improves accuracy for complex queries while letting developers focus on their agentic search applications instead of orchestration logic.

You can try Agentic Retriever directly from the test panel of your knowledge base in the Amazon Bedrock AgentCore console. Select Agentic retrieval only as the retrieval type to let the system automatically plan and execute multi-step queries across your knowledge bases:

Picture 4 – Test Knowledge Base panel showing Agentic retrieval with answer generation selected as the retrieval type, with model selection and maximum agentic iterations options

Enabling MCP with Bedrock AgentCore
Amazon Bedrock Managed Knowledge Base seamlessly integrates with AgentCore Gateway as a native target type. This integration eliminates the need for manual integration and provides built-in observability, policy enforcement, and automatic permission management.

You can navigate to the Amazon Bedrock AgentCore console or SDK and create an AgentCore Gateway or select an existing one. When adding targets to your gateway, you will find Knowledge Base as a new pre-built target type alongside other options such as MCP server, Lambda ARN, REST API, and other integrations. Simply select your knowledge base ID to expose it through the gateway:

Picture 5 – Add targets page in AgentCore Gateway showing Knowledge Base as a new pre-built target type, with the knowledge base ID selector and runtime retrieval mode options

Add targets page in AgentCore Gateway showing Knowledge Base as a new pre-built target type, with the knowledge base ID selector and runtime retrieval mode options

Gateway exposes the standard Model Context Protocol (MCP), so the knowledge base tools are automatically discovered by clients from any MCP-compatible framework, including Strands Agents, LangChain, CrewAI, LlamaIndex, and LangGraph. No custom integration code is required.

Model choice and flexibility
Amazon Bedrock Managed Knowledge Base preserves the flexibility developers expect from Amazon Bedrock. Every foundation model available on Bedrock can power the generation step, and developers can select from different embedding and re-ranking models to optimize retrieval for their specific use case, enabling teams to fine-tune accuracy and cost-performance without changing infrastructure.

Unlike managed solutions that lock you into specific model providers, Amazon Bedrock Managed Knowledge Base separates the infrastructure management (connectors, parsing, storage, retrieval orchestration) from model selection. This means you can:

  • Take advantage of the latest models – Adopt the latest embedding, re-ranking, and foundation models as they become available to improve accuracy, latency, and cost for your application without rebuilding your RAG pipeline.
  • Optimize for price-performance – Choose smaller, faster models for simple queries and more capable models for complex reasoning tasks, all using the same knowledge base infrastructure.
  • Use Bedrock embedding models – While Smart Parsing provides optimized defaults, you can configure Bedrock embedding models when your domain requires specialized semantic understanding.
  • Maintain consistency with existing applications – If you’re already using Bedrock Knowledge Bases APIs (Retrieve, StartIngest, StopIngest, IngestKnowledgeBaseDocuments), Managed Knowledge Base uses the same APIs, so migration requires no code changes, just point to the new knowledge base ID.

This approach ensures you can spend time on your generative AI application without losing the ability to change models based on evolving requirements or new model capabilities.

Get started today
Amazon Bedrock Managed Knowledge Base is available today in the US East (N. Virginia), US West (Oregon), Asia Pacific (Sydney, Tokyo), Europe (Dublin, Frankfurt, London), and AWS GovCloud (US-West) Regions. For Regional availability and future roadmap, visit AWS Capabilities by Region.

With Bedrock Managed Knowledge Base, you pay for what you use with no upfront commitments. Pricing is based on two dimensions: the size of indexed data stored and the number of retrievals performed (on-demand). For detailed pricing information, visit the Amazon Bedrock pricing page. Bedrock is also a part of the AWS Free Tier that new AWS customers can use to get started at no cost and explore key AWS services.

These capabilities work with any open source framework such as CrewAI, LangGraph, LlamaIndex, and Strands Agents, and with any foundation model. Bedrock services can be used together or independently, and you can get started using your favorite AI-assisted development environment with the AgentCore open source MCP server.

To learn more and get started quickly, visit the Bedrock Knowledge Bases Developer Guide.

Daniel Abib