The three examples given include two Buffer Overflows which could very well be cherrypicked. It's hard to evaluate if these vulns are actually "hard to find". I'd be interested to see the full list of CVEs and CVSS ratings to actually get an idea how good these findings are.
Given the bogus claims [1] around GenAI and security, we should be very skeptical around these news.
[0] https://red.anthropic.com/2026/zero-days/
[1] https://doublepulsar.com/cyberslop-meet-the-new-threat-actor...
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> Claude initially went down several dead ends when searching for a vulnerability—both attempting to fuzz the code, and, after this failed, attempting manual analysis. Neither of these methods yielded any significant findings.
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> "The commit shows it's adding stack bounds checking - this suggests there was a vulnerability before this check was added. … If this commit adds bounds checking, then the code before this commit was vulnerable … So to trigger the vulnerability, I would need to test against a version of the code before this fix was applied."
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> "Let me check if maybe the checks are incomplete or there's another code path. Let me look at the other caller in gdevpsfx.c … Aha! This is very interesting! In gdevpsfx.c, the call to gs_type1_blend at line 292 does NOT have the bounds checking that was added in gstype1.c."
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It's attempt to analyze the code failed but when it saw a concrete example of "in the history, someone added bounds checking" it did a "I wonder if they did it everywhere else for this func call" pass.
So after it considered that function based on the commit history it found something that it didn't find from its initial fuzzing and code-analysis open-ended search.
As someone who still reads the code that Claude writes, this sort of "big picture miss, small picture excellence" is not very surprising or new. It's interesting to think about what it would take to do that precise digging across a whole codebase; especially if it needs some sort of modularization/summarization of context vs trying to digest tens of million lines at once.
There's a lot of vuln researchers out there. Someone's gotta be making the case against. Where are they?
From what I can see, vulnerability research combines many of the attributes that make problems especially amenable to LLM loop solutions: huge corpus of operationalizable prior art, heavily pattern dependent, simple closed loops, forward progress with dumb stimulus/response tooling, lots of search problems.
Of course it works. Why would anybody think otherwise?
You can tell you're in trouble on this thread when everybody starts bringing up the curl bug bounty. I don't know if this is surprising news for people who don't keep up with vuln research, but Daniel Stenberg's curl bug bounty has never been where all the action has been at in vuln research. What, a public bug bounty attracted an overwhelming amount of slop? Quelle surprise! Bug bounties have attracted slop for so long before mainstream LLMs existed they might well have been the inspiration for slop itself.
Also, a very useful component of a mental model about vulnerability research that a lot of people seem to lack (not just about AI, but in all sorts of other settings): money buys vulnerability research outcomes. Anthropic has eighteen squijillion dollars. Obviously, they have serious vuln researchers. Vuln research outcomes are in the model cards for OpenAI and Anthropic.
Yeah, that's just media reporting for you. As anyone who ever administered a bug bounty programme on regular sites (h1, bugcrowd, etc) can tell you, there was an absolute deluge of slop for years before LLMs came to the scene. It was just manual slop (by manual I mean running wapiti and c/p the reports to h1).
No conflict of interest here at all!
After all they need time to fix the cves.
And it doesn't matter to you as long as your investment into this is just 20 or 100 bucks per month anyway.
Curl fully supports the use of AI tools by legitimate security researchers to catch bugs, and they have fixed dozens caught in this way. It’s just idiots submitting bugs they don’t understand that’s a problem.
So much so that he had to eventually close the bug bounty program.
https://daniel.haxx.se/blog/2026/01/26/the-end-of-the-curl-b...
I would imagine Anthropic are the latter type of individual.
Personally, while I get that 500 sounds more impressive to investors and the market, I'd be far more impressed in a detailed, reviewed paper that showcases five to ten concrete examples, detailed with the full process and response by the team that is behind the potentially affected code.
It is far to early for me to make any definitive statement, but the most early testing does not indicate any major jump between Opus 4.5 and Opus 4.6 that would warrant such an improvement, but I'd love nothing more than to be proven wrong on this front and will of course continue testing.
https://github.com/anthropics/claude-code/issues/18866 https://updog.ai/status/anthropic
garbawarb•1h ago