How do I test AI-generated code for security issues?
Manual Review and Static Analysis
Start by reading the code carefully, especially parts that handle user input, authentication, or database queries. Look for patterns like string concatenation in SQL, unsanitized input, and hardcoded secrets.
Use static analysis tools (SAST) like SonarQube, Snyk Code, or language-specific linters. These can catch issues like SQL injection, cross-site scripting (XSS), and insecure dependencies.
- Check for input validation and sanitization.
- Look for hardcoded credentials or API keys.
- Verify that authentication and authorization are properly implemented.
- Scan dependencies for known vulnerabilities.
- Ensure error messages don't leak sensitive information.
Dynamic Testing and Penetration Testing
Run the application in a safe environment and test it like an attacker would. Try common attacks like SQL injection, XSS, and CSRF. Tools like OWASP ZAP or Burp Suite can help automate this.
For critical applications, consider hiring a penetration tester. They can find complex vulnerabilities that automated tools miss.
AI-Specific Risks
AI might generate code that uses outdated or insecure libraries. It might also introduce logic flaws that aren't obvious, like improper access control.
Always treat AI-generated code as untrusted until proven secure. Don't assume it follows best practices.
Common mistakes
- Assuming AI-generated code is secure because it looks clean or works correctly.
- Skipping security testing because the app is just a prototype or internal tool.
- Relying solely on automated tools without manual review or penetration testing.
