What does an AI code audit check?
Correctness
Logic that passes a demo and fails on the second case: off-by-one bounds, inverted conditions, wrong operator precedence.
Error handling
Swallowed exceptions, empty catch blocks, and failures that return success to the caller.
Input validation
Whether untrusted input is validated at the boundary or trusted all the way to the database.
Data integrity
Missing transactions, partial writes, and multi-step operations with no rollback.
Concurrency
Race conditions, non-atomic read-modify-write, and shared state assumed to be single-user.
Duplication
The same logic regenerated in four files, each copy drifting separately from the others.
Dead code
Unreachable branches, unused exports, and abandoned scaffolding left behind by earlier prompts.
Type safety
`any` escapes, silenced compiler errors, and types that describe an older shape of the data.
API contracts
Whether the client and server agree on the shape, and whether the response is checked before it is used.
Dependency use
Libraries pulled in for one line, plus reimplementations of what the standard library already does.
Configuration
Hardcoded values that change per environment, and defaults that are only safe in development.
Logging
Personal data and secrets written to logs, and the paths that log nothing at all.
Test quality
Tests that assert the implementation rather than the behaviour, and suites that pass with the feature deleted.
Readability
Naming, structure, and comments, judged by whether a new engineer can safely change the file.
Consistency
Competing patterns for the same job, a common signature of code generated across many sessions.
Other audits
Each audit answers a different question. Most engagements combine two or three.
Build with
confidence.
AI builds the product. Vibecop makes sure it won’t break in production, fail under scale, or expose your users to risk. One audit. Fewer expensive surprises.