The blind spots generated changes share
AI-written code is locally plausible and globally risky: it usually does what you asked in the file you were looking at, while quietly violating an invariant somewhere else. The review job is to check the seams.
- Authorization regressions
A change to a query or endpoint can drop an ownership check. Confirm access control still holds after every edit to data-access code.
- Input trust
Generated handlers often assume well-formed input. Verify validation exists at the boundary, not just in the UI.
- Secret and config handling
Watch for keys moved into client-reachable code or hardcoded during a refactor.
- Silent N+1 and unbounded queries
A refactor that "cleans up" data fetching can reintroduce per-row queries or drop a limit.
Make review scale with output
Manual review can't keep pace with AI-speed code generation - that's the core tension. The answer is to automate the deterministic checks (access control patterns, unsafe queries, exposed secrets, dependency sanity) so human attention goes to the judgment calls.
A per-commit automated review turns "I hope someone reads this" into a scored, line-level report on every push, which is the only way the safety net keeps up with the generation speed.
The pre-launch checklist
- Re-verify authorization after data-access edits
Ownership checks survive refactors.
- Confirm server-side input validation at every boundary
Not just client-side.
- Scan for secrets moved into client code
Catch keys relocated during refactors.
- Check for reintroduced N+1 / unbounded queries
Cleanups can quietly undo performance work.
- Diff dependency changes for hallucinated packages
Confirm every added package is real and intended.
- Automate the deterministic checks
Reserve human review for genuine judgment calls.
Run this checklist on your repo, automatically
PeakStack scores every commit for security, scalability, and cost - with the exact line and a fix.
Request accessFAQ
Does Cursor-generated code need review?
Yes - arguably more than hand-written code, because the volume is higher and the model has consistent blind spots around authorization, input trust, and data-access performance. Review the seams where a local change can break a global invariant.
How can I review AI code without slowing down?
Automate the deterministic checks (access control, unsafe queries, exposed secrets, dependency sanity) and spend human attention only on judgment calls. PeakStack runs that automated review on every commit.
What does Cursor most often get subtly wrong?
Authorization regressions during refactors - a query or endpoint loses its ownership check while still "working." It's invisible in testing and the most exploited class of bug in production.