That's bragging rights correctly earned, i think! As marketing-y as this post is, definitely something to keep an eye on.

Wow, 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.

We had a few AISLE-generated security reports, and the signal to noise was reasonably good.

The most notable bug/exploit their scanner found was: https://gitlab.com/nbdkit/libnbd/-/commit/e50bbd2681117c2dd8...

The tool basically had to chain two exploits together to reach this. It also came up with a patch to fix which was fairly sensible (but I ended up editing it further for clarity).

Good marketing and definitive proof that local (read: on-prem & air-gapped) models with correct context and tools are good enough to perform on par and above SOTA cloud hosted solutions.

We have seen this point many times before with different technologies. The first computers at university were big and expensive, same as this machine. Give it a few years and this functionality will be a commodity.