Common Vulnerabilities in AI Code
Studies show AI-generated code contains vulnerabilities 30-40% of the time. Common issues: SQL injection via string concatenation, XSS from unsanitized output, hardcoded secrets, missing authentication checks, insecure random number generation, and path traversal vulnerabilities.
Supply Chain Risks
AI may suggest packages that don't exist (hallucinated packages) or outdated versions with known CVEs. Attackers register package names that AI commonly hallucinate — a new form of supply chain attack. Always verify dependencies exist, check their security status, and pin versions.
Prompt Injection
If your app uses AI to generate code from user input, prompt injection can make the AI generate malicious code. Sanitize all user inputs before including them in AI prompts. Use structured outputs and validate generated code against allowlists of safe patterns.
Mitigation Strategies
Run SAST (Static Application Security Testing) on all code, including AI-generated. Use SCA (Software Composition Analysis) to check dependencies. Implement security-focused code review for AI output. Use AI security tools that specifically scan for AI-generated vulnerability patterns.
Security Best Practices
Never let AI handle authentication or cryptography without expert review. Validate AI-generated database queries for injection. Check file operations for path traversal. Review error handling — AI often leaks sensitive information in error messages. Treat AI code as untrusted input that needs validation.
Conclusion
AI coding tools are powerful but security-naive. They optimize for functionality, not security. Every organization using AI coding tools needs security scanning in CI, security-focused code review, and developer training on AI-specific risks. The speed benefits of AI are only valuable if the code is secure.