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1/ Agentic LLMs can automate vuln detection. Very exciting, but doesn't address the hardest part (imo) of vuln research: prioritization. Can we reliably explore the search space and separate signal from noise? I wrote a paper (and OSS tool) to solve this. arxiv.org/pdf/2512.06155
feels like sandboxing could use a healthy dose of this
I am looking forward to the forthcoming golden age of security engineering. Like other forms of engineering, it's about designing for and building in resilience to some chosen degree of failures in complete systems.
What most call security engineering today is really just sparkling security operations, especially if it is centered around responding to external events to protect what others have designed and built. This is necessary, but not sufficient to have a hope of pulling ahead of the attacks that are coming.
Caleb Gross retweeted
I am evaluating secure sandbox surfaces for high-trust agent environments (benchmarking, SFT, expert on-policy supervision, outcome based RL, etc). Obviously there are some pre-baked solutions, one that is attractive is Docker's sbx (docker.com/products/docker-s…).
Sbx runs agents inside isolated Linux microVM's with their own kernel and private Docker Engine. They use filesystem passthroughs and host-side bridges to facilitate file access, networking, mcp tool usage, credentials, etc. These microVM's offer a very strong execution boundary but obviously the brokers are a major attack surface.
Looking through the changelog for sbx, the situation is a bit more grim than you would expect. Between June 1 and today (Oct 24) there are 9 CVE's including critical issues (like a macOS virtio-fs escape CVE-2026-77179). These look like typical logic issues you might expect. What's interesting though is that the changelogs also reveal areas that got security fixes but no CVE. For example, a full broker mediated guest-to-host escape, shown in the poc below (on v0.39.0 before Sept 7). Some light reverse engineering was required since you don't get core implementation source.
Pre-patch a sandbox could supply DBUS_SESSION_BUS_ADDRESS through the guest-controlled host_env sent to the host browser bridge. On the Linux host, the bridge passed that environment to xdg-open, whose desktop-detection path invoked dbus-send, which accepts a "unixexec:" address leading to code execution.
Caleb Gross retweeted
We're thrilled to have contributed a V8 vulnerability to one of this year's four $250K Chrome full chain exploits. The vulnerability was subtle enough that Google needed multiple commits to finally fix it.
However, we would like to take a step further. To convince Fortune to smile upon us deterministically. We managed to use LLM-assisted formal verification to rediscover the bug. The same approach can prove security properties of functions and APIs.
Will share writeup and more about formal verification work soon. We will build more Chrome full chains!
We just received a $250,000 Chrome Full Chain bonus! 🎉
We've claimed the second Full Chain bonus of 2026. Only two remain!
Under the rules, every vulnerability had to be a non-duplicate 0-day, and the entire exploit chain had to be completed before any of them were patched.
Huge thanks to @nebusecurity for discovering a V8 vulnerability in just a single day! Also, a big thank you to @_deayzl for collaborating with me on the V8 Heap Sandbox research.
P.S. KVM escapes have been getting a lot of attention. I actually captured the Full VM Escape flag in KVMCTF about three months ago. Hopefully, I'll be able to share that research soon, too!
agree with all of this. especially understanding the cost of prefill. "before local AI is banned" has motivated much of my learning (and purchases) in the past 24 mos. blog post likely incoming.
Before local AI is banned, learn how to host and tune your own models.
Specific learning curves to understand for me were:
- Offloading portions of newer models to SSD rather than RAM
- Prompt caching configs
- Preload tuning
- the right choice of model for the task
- pre optimized models for your hardware and harness
- finding the fastest inference server at the web/api level
turingtest++: has an LLM ever made you laugh out loud? not just *forceful nasal exhale*. I've challenged various LLMs to write lol-worthy jokes and they fall completely flat. but perhaps "surprise" requires an external conscious entity. like how you can't tickle yourself.
wake me up when we have LOLMs that can produce bangers like this xitter.cf/growing_daniel/status/…
`s/A/S/` is doing a LOT of work these days
New: I got a tip a little while ago about a shadowy Pentagon entity called "TSO" with a lot of money and a desire for offensive hacking tools.
Turns out TSO is a new DoD agency. And it does a lot more than hacking.
w/@JackDetsch and @paulmcleary
politico.com/news/2026/10/22…
Let’s set the record straight: @lcfr_hax was the first to discover php2shell and develop a working exploit. See his August 5 tweet: xitter.cf/lcfr_hax/status/208503…
His work convinced the PHP Foundation to invest in fixing this entire bug class. He then worked around the clock to review, test, and retest the patches.
Caleb Gross retweeted
Attack surface reduction is the one thing that reliably works.
Reducing the attack surface of deployed services and apps is a fundamental component of software that is secure by design. Earlier this year we tasked Claude with autonomously mapping and reducing the attack surface of 25 large and widely used OSS codebases. This blog post covers some of our methodology and a preview of the data we collected.
This research resulted in a significant reduction in OpenSSH pre-authentication attack surface (now fixed in 10.6). And over 500 findings where reachability could be reduced and configurations hardened to ensure security controls weren’t silently disabled.
anthropic.com/research/attac…
Caleb Gross retweeted
Just watched the @netflix Instadocs about the @OpenAI cyberattack on @huggingface. Super disappointing, as they went for maximum anthropomorphism of the technology and full doomer mode.
I spent more than two hours with them trying to correct all their mistakes and ground this in reality, explaining that agents are technological systems, not aliens. But it looks like the drive to scare people for maximum views was too strong.
Also, they didn’t mention the lack of containment and monitoring of the agents, how closed-model safeguards blocked our defensive work, or how we defended ourselves with AI using an open model. And that we still lack a lot of information and transparency, including full agent traces and that companies need to remain accountable for what their agents do.
It ends up being a fun sci-fi movie though!





















