Let’s Talk About AI Security Auditing in Web3

We’ve tried AI scanners in our workflow as well, mostly early on when contracts were still evolving.

They helped with some obvious misses, but honestly most of the issues that caused real pain for us weren’t things a scanner could flag. They showed up when assumptions broke across functions or when state changes played out over multiple calls.

One thing we learned the hard way is that a clean automated scan doesn’t mean much unless someone actually reasons through the system end to end. In fact, the bigger risk for us was teams trusting “no issues found” more than they should.

These days we treat AI checks strictly as hygiene, not a signal of safety, and try to keep human review depth the same regardless of what the tools say.

We went through a similar phase last year when everyone started pushing AI checks as part of “secure by default”.

What stood out for us was that the hardest bugs never came from things a scanner could reason about in isolation. Most of the serious issues showed up only after a few weeks in staging, when edge cases piled up and assumptions started leaking.

The dangerous part wasn’t that AI missed things — that’s expected. It was that people subconsciously relaxed once automated checks were green. Reviews got faster, questions got softer.

We eventually treated automated checks like linting: useful, necessary, but never a reason to reduce manual scrutiny or threat modeling.

Security felt better once we stopped asking “what tools are we using?” and started asking “what assumptions would break if this contract is called in the worst possible order?”

What changed my view on AI security checks was realizing that the main risk is not just “what the tool misses” — it is how quickly teams start relaxing once the scan looks clean.

Most serious smart contract issues I’ve seen were not isolated pattern problems. They showed up when permissions, upgrade assumptions, external calls, or multi-step state changes interacted in messy ways. That is usually where manual reasoning still matters far more than a green report.

So for people here who use AI or automated scanners early: what do you force yourself to reason through manually every single time, even when tooling says things look fine?

This fits the thread well because the original post frames AI as a first line of defense, while both existing replies already push toward the stronger tension: clean automated results can create false confidence, and the deeper risk often appears across functions, calls, and assumptions.