AI Code Review Guide
Comprehensive framework for reviewing AI-generated code security. Learn how to systematically audit code from Copilot, Cursor, and other AI assistants for vulnerabilities.
Never Trust AI-Generated Code Blindly
AI coding assistants produce functional code quickly but lack security expertise. Every line of AI-generated code must be reviewed for vulnerabilities, especially authentication, authorization, and cryptographic operations.
AI Code Review Checklist
Follow these 12 steps to review AI-generated code. Critical items must be verified before merging security-sensitive code.
Verify authentication implementation
Check that all authentication logic uses established libraries, not custom implementations. Verify password hashing, session management, and token generation.
Audit input validation
Ensure all user inputs are validated, sanitized, and escaped. Look for SQL injection, XSS, and command injection vulnerabilities.
Review authorization checks
Confirm that every protected endpoint verifies user permissions. Check for privilege escalation and horizontal access vulnerabilities.
Inspect cryptographic operations
Verify use of secure hashing algorithms (bcrypt, argon2), proper random number generation, and encryption at rest.
Check for hardcoded secrets
Search for API keys, passwords, tokens, or credentials embedded in code. Verify use of environment variables or secret managers.
Validate error handling
Ensure errors do not leak sensitive information like stack traces, database details, or system paths in production.
Review API response data
Check that API responses only include necessary fields. Look for exposed internal IDs, sensitive user data, or system information.
Audit file operations
Review file upload validation, path construction, and storage. Check for path traversal, unrestricted file types, and insecure storage.
Verify rate limiting
Confirm rate limiting on authentication endpoints, APIs, and resource-intensive operations to prevent abuse.
Check security headers
Verify CSP, HSTS, X-Frame-Options, and other security headers are properly configured.
Review logging practices
Ensure sensitive data is not logged. Verify security events are captured for monitoring and incident response.
Test business logic
Manually test workflows for logic flaws like race conditions, payment bypasses, or discount abuse.
Common Code Review Mistakes
Trusting AI-Generated Comments
HighCode comments may describe secure behavior while actual implementation is vulnerable
Accepting Plausible Functions
CriticalAI may generate realistic-looking but non-existent security functions
Skipping Manual Testing
HighAutomated scans miss logic flaws and business vulnerabilities requiring manual review
Ignoring Context Windows
MediumAI lacks full codebase context and may introduce inconsistencies with existing security patterns
Related Resources
Automate Your Code Reviews
Manual code reviews are essential, but automated scans can catch common issues faster. VibeEval provides AI-specific security analysis to complement your manual review process.
Start Free Security Analysis