Cursor security audit
Using Cursor? Audit these security gaps before production.
Cursor is the fastest way to ship code in 2026. Speed is the feature; speed is also the problem. Here are the three patterns we find on almost every Cursor-built repo we audit.
Common problems
What we keep finding in Cursor codebases.
AI-generated SQL injection risk
Cursor often writes queries that “work” for the happy path but lack parameterization. String interpolation creeps in whenever the model has to handle a dynamic column or order-by.
Exampledb.query(`SELECT * FROM users WHERE email = ‘$${email}’`)— works for valid emails, fails for anything containing a quote.Missing server validation
Frontend validation exists. Backend trust remains dangerous. Bypassing the form means bypassing every check the form was supposed to enforce.
Broken role segregation
Permissions exist visually — admin views, gated buttons — but the underlying database queries return whatever the user asks for, regardless of role.
Prompt fixes
What to tell the model.
Paste these into your next conversation. They steer generation toward safer defaults — but they aren't a substitute for review.
For: AI-generated SQL injection risk
“Use parameterized queries only. Treat any string interpolation inside a SQL statement as a bug, even for column names — allow-list those instead.”
For: Missing server validation
“Validate all input server-side. Treat the frontend as untrusted regardless of what client-side validation already runs.”
For: Broken role segregation
“Enforce role permissions at the data layer, independent of UI state. Every query that returns sensitive data must filter by the authenticated user’s role.”
Manual verification
The Cursor checklist.
Run through each item by hand before you ship. If anything is unclear, treat it as a red flag, not a green light.
- Grep the codebase for backtick-interpolated SQL and convert each to parameters.
- Fire requests directly at API endpoints with malformed payloads.
- Sign in as a non-admin and request admin-only resources by ID.
- Confirm row-level filtering exists on every list endpoint.
- Audit every server action for an explicit identity check.
If issues are already live
Damage control, in order.
If you suspect any of the above already shipped to real users, work the list top-to-bottom. Don’t skip rotation.
- 1Rotate database credentials and check query logs for SQL injection patterns.
- 2Audit the data your low-privilege users can actually fetch via the API.
- 3Patch every endpoint missing server-side validation, in priority order.
- 4Add a CI rule that fails any new string-interpolated SQL.
- 5Run a clean-room audit before announcing the fix.
Why AI-generated fixes still fail
The model that wrote the bug rarely sees it.
Same blind spots
The patterns that produced the vulnerability are baked into the model's training. Asking it to audit itself reproduces the same assumptions.
Context windows lie
The model sees the file you paste, not your auth middleware, your RLS policies, or the route you forgot to protect. It can't review what it can't see.
Confidence ≠ correctness
AI fixes look polished and read well. That's a signal of fluency, not of safety. Real verification needs a human who can hit the endpoint.
Don’t ask AI to audit AI
You wouldn’t let an intern grade their own homework.
Most builders type “is this secure?” back into the same chat that wrote the code. You need independent verification — someone whose context isn’t poisoned by what just got generated. That’s Kleared.
Cursor helps you ship faster. Attackers love fast shipping too.
Kleared verifies what the IDE didn't — server-side enforcement, parameterized queries, role segregation — and opens fix-PRs that pass your CI.
Before you launch
Run a real human security review.
Not another prompt. Kleared checks the boring stuff that breaks production:
- auth
- permissions
- secrets
- uploads
- database exposure
- API security
- payment flows
- production configs
So you can ship without guessing.