I genuinely dont know who to believe. The people who claim LLMs are writing excellent exploits. Or the people who claim that LLMs are sending useless bug reports. I dont feel like both can really be true.
The quality of output you see from any LLM system is filtered through the human who acts on those results.
A dumbass pasting LLM generated "reports" into an issue system doesn't disprove the efforts of a subject-matter expert who knows how to get good results from LLMs and has the necessary taste to only share the credible issues it helps them find.
> I have written up the verification process I used for the experiments here, but the summary is: an exploit tends to involve building a capability to allow you to do something you shouldn’t be able to do. If, after running the exploit, you can do that thing, then you’ve won. For example, some of the experiments involved writing an exploit to spawn a shell from the Javascript process. To verify this the verification harness starts a listener on a particular local port, runs the Javascript interpreter and then pipes a command into it to run a command line utility that connects to that local port. As the Javascript interpreter has no ability to do any sort of network connections, or spawning of another process in normal execution, you know that if you receive the connect back then the exploit works as the shell that it started has run the command line utility you sent to it.
It is more work to build such "perfect" verifiers, and they don't apply to every vulnerability type (how do you write a Python script to detect a logic bug in an arbitrary application?), but for bugs like these where the exploit goal is very clear (exec code or write arbitrary content to a file) they work extremely well.
The Industrialisation of Intrusion
By ‘industrialisation’ I mean that the ability of an organisation to complete a task will be limited by the number of tokens they can throw at that task. In order for a task to be ‘industrialised’ in this way it needs two things:
An LLM-based agent must be able to search the solution space. It must have an environment in which to operate, appropriate tools, and not require human assistance. The ability to do true ‘search’, and cover more of the solution space as more tokens are spent also requires some baseline capability from the model to process information, react to it, and make sensible decisions that move the search forward. It looks like Opus 4.5 and GPT-5.2 possess this in my experiments. It will be interesting to see how they do against a much larger space, like v8 or Firefox.
The agent must have some way to verify its solution. The verifier needs to be accurate, fast and again not involve a human.
[1] If their experiment even demonstrates what it purports to demonstrate. For anyone to give this article any credence, the exploit really needs to be independently verified that it is what they say it is and that it was achieved the way they say it was achieved.
This applies to exploits, but it applies _extremely_ generally.
The increased interest in TLA+, Lean, etc comes from the same place; these are languages which are well suited to expressing deterministic success criteria, and it appears that (for a very wide range of problems across the whole of software) given a clear enough, verifiable enough objective, you can point the money cannon at it until the problem is solved.
The economic consequences of that are going to be very interesting indeed.
1. I think you have mixed up assistance and expertise. They talk about not needing a human in the loop for verification and to continue search but not about initial starts. Those are quite different. One well specified task can be attempted many times, and the skill sets are overlapping but not identical.
2. The article is about where they may get to rather than just what they are capable of now.
3. There’s no conflict between the idea that 10 parallel agents of the top models can mostly have one that successfully exploits a vulnerability - gated on an actual test that the exploit works - with feedback and iteration BUT random models pointed at arbitrary code without a good spec and without the ability to run code, and just run once, will generate lower quality results.
With the CVE reports some poor maintainer has to go through and triage them, which is far more work, and very asymmetrical because the reporters can generate their spam reports in volume while each one requires detailed analysis.
The proper way to use these tools (like in other verifiable tasks such as math or coding) is to give them a feedback loop and an easily verifiable success criteria. In security exploitation you either capture the flag or not. It's very easy (and cheap) to verify. So you can leave these things to bang their tokens against a wall, and only look at their output once they capture the flag. Or they output something somewhere verifiable (e.g. echo "pwned" > /root/.flag)
An "agent harness" here is software that directly writes and executes code to test that it works. A vulnerability reported by such an agent harness with included proof-of-concept code that has been demonstrated to work is a different thing from an "exploit" that was reported by having a long context model spit out a bunch of random ideas based purely on reading the code.
I'm confident you can still find dumbasses who can mess up at using coding agent harnesses and create invalid, time wasting bug reports. Dumbasses are gonna dumbass.
Another thought is the reproducible builds become more valuable than ever, because it actually becomes feasible for lots and lots of devs to scan the entire codebase for vulns using an LLM and then verify reproducibility.
Note I haven't actually tried Claude Code (not coding due to chronic illness), so I'm mostly extrapolating based on HN discussion etc.
For example if you just ask an LLM in a browser with no tool use to "find a vulnerability in this program", it'll likely give you something but it is very likely to be hallucinated or irrelevant.
However if you use the same LLM model via an agent, and provide it with concrete guidance on how to test its success, and the environment needed to prove that success, you are much more likely to get a good result.
It's like with Claude code, if you don't provide a test environment it will often make mistakes in the coding and tell you all is well, but if you provide a testing loop it'll iterate till it actually works.
For an interesting overview of the above theorem, see here[1].
0 - https://en.wikipedia.org/wiki/Infinite_monkey_theorem
1 - https://www.yalescientific.org/2025/04/sorry-shakespeare-why...
The AI slop you see on curl's bug bounty program[1] (mostly) comes from people who are not hackers in the first place.
In the contrary persons like the author are obviously skilled in security research and will definitely send valid bugs.
Same can be said for people in my space who do build LLM-driven exploit development. In the US Xbow hired quite some skilled researchers [2] had some promising development for instance.
[1] https://hackerone.com/curl/hacktivity [2] https://xbow.com/about
His co-founder on optimyze was Sean Heelan, the author of the OP.