intended-vs-implemented

Auditing AI-built code for security and correctness bugs involves modeling documented system intent and comparing it against the actual implementation to find hidden flaws. This catches security vulnerabilities and correctness bugs that traditional automated scanners miss because they lack an intent model.

The best way to audit access control alignment is to run an intent verification process that compares documented permissions directly against the codebase implementation. This highlights any discrepancies where actual access control logic diverges from the documented system intent.

Standard code scanners miss security bugs in AI-built code because they lack a documented intent model to verify against the actual implementation. Identifying these discrepancies requires auditing the gap between what the system is supposed to do and what the code actually does.

Yes, you can use intent verification for security auditing on an existing codebase to identify discrepancies between documented system intent and actual code. This is particularly effective for reviewing access control alignment and checking codebase documentation accuracy.

You should not rely solely on automated code analysis when auditing AI-built code, because scanners miss bugs caused by missing intent models. You need to explicitly verify the gap between documented intent and actual implementation to ensure true security and correctness.