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Analemma_yesterday at 8:27 PM4 repliesview on HN

I'm curious: has someone done a lengthy write-up of best practices to get good results out of AI security audits? It seems like it can go very well (as it did here) or be totally useless (all the AI slop submitted to HackerOne), and I assume the difference comes down to the quality of your context engineering and testing harnesses.

This post did a little bit of that but I wish it had gone into more detail.


Replies

j-connyesterday at 11:53 PM

OpenAI just released “codex security”, worth trying (along with other suggestions) if your org has access https://openai.com/index/codex-security-now-in-research-prev...

simonwyesterday at 9:28 PM

The HackerOne slop is because there's a financial incentive (bug bounties) involved, which means people who don't know what they are doing blindly submit anything that an LLM spots for them.

If you're running the security audit yourself you should be in a better position to understand and then confirm the issues that the coding agents highlight. Don't treat something as a security issue until you can confirm that it is indeed a vulnerability. Coding agents can help you put that together but shouldn't be treated as infallible oracles.

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lmeyerovyesterday at 8:55 PM

We split our work:

* Specification extraction. We have security.md and policy.md, often per module. Threat model, mechanisms, etc. This is collaborative and gets checked in for ourselves and the AI. Policy is often tricky & malleable product/business/ux decision stuff, while security is technical layers more independent of that or broader threat model.

* Bug mining. It is driven by the above. It is iterative, where we keep running it to surface findings, adverserially analyze them, and prioritize them. We keep repeating until diminishing returns wrt priority levels. Likely leads to policy & security spec refinements. We use this pattern not just for security , but general bugs and other iterative quality & performance improvement flows - it's just a simple skill file with tweaks like parallel subagents to make it fast and reliable.

This lets the AI drive itself more easily and in ways you explicitly care about vs noise

ares623yesterday at 8:29 PM

No mention of the quality of the engineers reviewing the result?