Hacker News Reader

Comment by tux3 | Hacker News Reader

The homepage says something about AI guided fuzzing based on libfuzzer or AFL. Looks like they have the LLMs identify a bunch of interesting functions to test, generate some test harnesses, and then sort through the fuzzer findings at a high level, which sounds like a pretty good idea.

View on HN

Thanks for figuring that out. Sort of sounds like AI programming programs to find vulnerabilities, of which fuzzing is one of the proven techniques to do it.

Back when I first heard of it, the HuggingFace hack reminded me about how you don't have to be particularly smart to find vulns, just aggressive in looking for them. AFL on its own "learned" how to construct well-formed JPEGs and PDFs by fuzzing decoders for these formats, back in the pre-LLM dark ages.

Of course the HuggingFace hack proved to be much more than that...

It's an effective approach. Google project zero started doing this in 2024

https://security.googleblog.com/2024/11/leveling-up-fuzzing-...

Also sounds incredibly compute intensive.

Setting a swarm of agents loose for hours to look for software vulnerabilities is far more compute-expensive than fuzzing. The industry has never thrown this kind of compute resources at pure fuzzing, in part because you can't get much VC money for that.

This sounds like a swarm of agents with particular prompting that happens to guide the LLMs toward doing a lot of fuzzing, so it's not either/or; you're getting all the compute requirements of both.

You also quickly get into diminishing returns with fuzzing. Generally a bug is either going to be found relatively quickly with a given fuzzing approach or it's going to be nearly impossible to find. You're usually better coming up with more intelligent fuzzing approaches than you are just dumping compute into it.

Fuzzing or having the LLM sort through where might be most useful to fuzz & sorting the results? Neither seem particularly compute intensive to me, fuzzing is a pretty standard step and having the LLM read through to find the most interesting areas to fuzz sounds a lot more efficient than leaving the whole task to the LLM.

Reply on news.ycombinator.com