Thanks for putting N-Day-Bench together - really interesting benchmark design and results.

I'd love to see how the model we serve, Qwen3.5 122B A10B, stacks up against the rest on this benchmark. AI Router Switzerland (aiRouter.ch) can sponsor free API access for about a month if that helps for adding it to the evaluation set.

Definitely possible. In January, I tried using Gemini to perform black-box/white-box testing on an existing system in my company (it's quite old). It successfully exploited a hidden SQL injection vulnerability to penetrate the system and extract password hashes (not particularly strong passwords, successfully decrypted on a public website). In terms of pure skill level, I'd say this is at least the level of a mid-level cybersecurity professional, not even considering the significant efficiency improvement.

> Each case runs three agents: a Curator reads the advisory and builds an answer key, a Finder (the model under test) gets 24 shell steps to explore the code and write a structured report, and a Judge scores the blinded submission. The Finder never sees the patch. It starts from sink hints and must trace the bug through actual code.

Curator, answer key, Finder, shell steps, structured report, sink hints… I understand nothing. Did you use an LLM to generate this HN submission?

It looks like a standard LLM-as-a-judge approach. Do you manually validate or verify some of the results? Done poorly, the results can be very noisy and meaningless.

I worked in AppSec in the past, made sense to me. Maybe you aren't the target audience?

You don't really need manual verification for these, the CVEs (vulnerabilities) are public and can be programmatically validated.

Is this really that hard to parse?

Curator and Finder are the names of the agents. "answer key" - haven't you ever taken a test in high school? It's an explanation of the answer. "shell steps" I presume means it gets to run 24 commands on the shell. "structured report" - do I really need to explain to you what a report is? "sink hints" - I admit I didn't know this one, but a bit of searching indicates that it's a hint at where the vulnerability lies.

> Did you use an LLM to generate this HN submission?

Must have.

> The Finder will never see the patch.

I wasn’t worried that this eval would show the answer to the model before evaluating it. Seems requirements leaked into this post.

I'd love to see some of the open source models in there

It would be helpful to add in some cases that do not contain any vulnerabilities to assess false-positive rate as well.

This is a good idea.

Will incorporate false-positive rates into the rubric from the next run onwards.

At winfunc, we spent a lot of research time taming these models to eradicate false-positive rates (it's high!) so this does feel important enough to be documented. Thanks!

Any code that is certain that it doesn't have any vulnerabilities is going to be pretty trivial to verify.

Very curious how Claude Mythos will perform here