My hunch is that the dumbasses submitting those reports were't actually using coding agent harnesses at all - they were pasting blocks of code into ChatGPT or other non-agent-harness tools and asking for vulnerabilities and reporting what came back.

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.

I've had multiple reports with elaborate proofs of concept that boil down to things like calling dlopen() on a path to a malicious library and saying dlopen has a security vulnerability.

BTW regarding "suggested fixes", an interesting attack would be to report a bug along with a prompt injection which will cause Claude to suggest inserting a vulnerability in the codebase in question. So, it's important to review bug-report-originated Claude suggestions extra carefully. (And watch for prompt injection attacks.)

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.

Would you ever blindly trust it?

Very often they need to understand it well to chain exploits

There is filtering mentioned, it's just not done by a human:

> 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 OP is the filtering expert.

What this is saying is "you need an objective criterion you can use as a success metric" (aka a verifiable reward in RL terms). "Design of verifiers" is a specific form of domain expertise.

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.

A few points:

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.

My expectation is that any organization that attempts this will need subject matter experts to both setup and run the swarm of exploit finding agents for them.

After setting the environment and the verifier you can spawn as many agents as you want until the conditions are met, this is only possible because they run without human assistance, that's the "industrialisation".

Yes, Halvar Flake is pretty well respected in exploit dev circles.