alt Hacker NewsWow, this announcement is good content marketing.
Don't get me wrong, it's interesting. But there is no technical discussion as to how they did it. It's simply: we did it and Mythos and Codex didn't.
It's good to know that it's possible, but I'd have already expected it. Put a base model versus a base model + harness + whatever else, and yea, if you do it right then you have a better system to find vulnerabilities.
> We then ran AISLE's autonomous AI system against curl.
They don't even mention what models the use under the hood. It wouldn't surprise me if they are from Anthropic and OpenAI.
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It defaults to gpt5.4 nano
https://github.com/weareaisle/nano-analyzer/blob/main/scan.p...
Their system can run with various models, they go into more details in this article.
https://aisle.com/blog/system-over-model-zero-day-discovery-...
It wouldn’t surprise me if AISLE uses many different providers’ models, and what’s holding back OpenAI and Anthropic is only using first-party models. Just because OpenAI and Anthropic have arguably the strongest models overall doesn’t mean their models are the strongest at finding any given class of vulnerability or lead to follow.
> what models the use under the hood
Presumably their own, wouldn’t they?
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.