Glad to see that they brought in humans to validate and patch vulnerabilities. Although, I really wish they linked to the actual patches. Here's what I could find:
Grepping for strcat() is at the "forefront of cybersecurity"? The other one that applied a GitHub comment to a different location does not look too difficult either.
Everything that comes out of Anthropic is just noise but their marketing team is unparalleled.
The post is light on details, and I agree with the sentiment that it reads like marketing. That said, Opus 4.6 is actually a legitimate step up in capability for security research, and the red team at Anthropic – who wrote this post – are sincere in their efforts to demonstrate frontier risks.
Opus 4.6 is a very eager model that doesn't give up easily. Yesterday, Opus 4.6 took the initiative to aggressively fuzz a public API of a frontier lab I was investigating, and it found a real vulnerability after 100+ uninterrupted tool calls. That would have required lots of of prodding with previous models.
If you want to experience this directly, I'd recommend recording network traffic while using a web app, and then pointing Claude Code at the results (in Chrome, this is Dev Tools > Network > Export HAR). It makes for hours of fun, but it's also a bit scary.
This is actually a good concrete example of how to use AI for pen testing (which I've never had time to look at, so I realise it may be common). The issue I'm struggling with is cost - to point O4.6 at network logs, and have it explore...how may tokens/money do you burn?
I just tested this using Calude and at least with 4.5 this does not seem to be possible. The context grows very quickly and the LLM gets lost and starts hallucinating. Maybe I am missing some key ingredient here?
Of course, if you have large team of AI and security experts and an unlimited token budget things can look different.
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[ 2.8 ms ] story [ 46.0 ms ] threadhttps://cgit.ghostscript.com/cgi-bin/cgit.cgi/ghostpdl.git/c...
https://github.com/OpenSC/OpenSC/pull/3554
https://github.com/dloebl/cgif/pull/84
Everything that comes out of Anthropic is just noise but their marketing team is unparalleled.
Opus 4.6 is a very eager model that doesn't give up easily. Yesterday, Opus 4.6 took the initiative to aggressively fuzz a public API of a frontier lab I was investigating, and it found a real vulnerability after 100+ uninterrupted tool calls. That would have required lots of of prodding with previous models.
If you want to experience this directly, I'd recommend recording network traffic while using a web app, and then pointing Claude Code at the results (in Chrome, this is Dev Tools > Network > Export HAR). It makes for hours of fun, but it's also a bit scary.
Yawn.
https://clang-analyzer.llvm.org
Alternatively, testing these projects with ASan enabled:
https://clang.llvm.org/docs/AddressSanitizer.html
Of course, if you have large team of AI and security experts and an unlimited token budget things can look different.