AI CODE REVIEW SECURITY GUIDE | VIBEEVAL

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.

Automate steps 1-10 of this checklist

VibeEval runs these security checks automatically on every deploy. Free to start.

Common Code Review Mistakes

Trusting AI-Generated Comments

Code comments may describe secure behavior while actual implementation is vulnerable

Accepting Plausible Functions

AI may generate realistic-looking but non-existent security functions

Skipping Manual Testing

Automated scans miss logic flaws and business vulnerabilities requiring manual review

Ignoring Context Windows

AI lacks full codebase context and may introduce inconsistencies with existing security patterns

Related Resources

AI Code Vulnerabilities

Complete taxonomy of AI-generated code vulnerabilities

Secure AI Coding Practices

Best practices for generating secure AI code

AI Security Testing Tools

Tools for analyzing AI-generated code

Cursor Security Guide

Securing code generated with Cursor AI

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.