Comment by tptacek
12 hours ago
I'm interested in whether there's a well-known vulnerability researcher/exploit developer beating the drum that LLMs are overblown for this application. All I see is the opposite thing. A year or so ago I arrived at the conclusion that if I was going to stay in software security, I was going to have to bring myself up to speed with LLMs. At the time I thought that was a distinctive insight, but, no, if anything, I was 6-9 months behind everybody else in my field about it.
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.
> 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.
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).
I used to answer security vulnerability emails to Rust. We'd regularly get "someone ran an automated tool and reports something that's not real." Like, complaints about CORS settings on rust-lang.org that would let people steal cookies. The website does not use cookies.
I wonder if it's gotten actively worse these days. But the newness would be the scale, not the quality itself.
I did some triage work for clients at Latacora and I would rather deal with LLM slop than argue with another person 10 time zones away trying to convince me that something they're doing in the Chrome Inspector constitutes a zero-day. At least there's a possibility that LLM slop might contain some information. You spent tokens on it!
The new slop can be much harder to recognize and reject than the old "I ran XYZ web scanner on your site" slop.
POCs are now so cheap that "POC||GTFO" is a perfectly reasonable bar to set on a bounty program.
> I was going to have to bring myself up to speed with LLMs
What did you do beyond playing around with them?
> Of course it works. Why would anybody think otherwise?
Sam Altman is a liar. The folks pitching AI as an investment were previously flinging SPACs and crypto. (And can usually speak to anything technical about AI as competently as battery chemistry or Merkle trees.) Copilot and Siri overpromised and underdelivered. Vibe coders are mostly idiots.
The bar for believability in AI is about as high as its frontier's actual achievements.
I still haven't worked out for myself where my career is going with respect to this stuff. I have like 30% of a prototype/POC active testing agent (basically, Burp Suite but as an agent), but I haven't had time to move it forward over the last couple months.
In the intervening time, one of the beliefs I've acquired is that the gap between effective use of models and marginal use is asking for ambitious enough tasks, and that I'm generally hamstrung by knowing just enough about anything they'd build to slow everything down. In that light, I think doing an agent to automate the kind of bugfinding Burp Suite does is probably smallball.
Many years ago, a former collaborator of mine found a bunch of video driver vulnerabilities by using QEMU as a testing and fault injection harness. That kind of thing is more interesting to me now. I once did a project evaluating an embedded OS where the modality was "port all the interesting code from the kernel into Linux userland processes and test them directly". That kind of thing seems especially interesting to me now too.
Plenty of reasons to be skeptical, but also we know that LLMs can find security vulnerabilities since at least 2024:
https://projectzero.google/2024/10/from-naptime-to-big-sleep...
Some followup findings reported in point 1 here from 2025:
https://blog.google/innovation-and-ai/technology/safety-secu...
So what Anthropic are reporting here is not unprecedented. The main thing they are claiming is an improvement in the amount of findings. I don't see a reason to be overly skeptical.
I'm not sure the volume here is particularly different to past examples. I think the main difference is that there was no custom harness, tooling or fine-tuning. It's just the out of the box capabilities for a generally available model and a generic agent.