LLMs generate vulnerable code by default. Understand the risks and protect your AI-built applications.
AI often creates API routes without proper auth checks, assuming frontend handles security.
API endpoints accessible without login verificationLLMs frequently use string interpolation instead of parameterized queries.
db.query(`SELECT * FROM users WHERE id = ${userId}`)AI includes API keys and credentials directly in code for 'working examples'.
const apiKey = 'sk-live-...' in source codeGenerated code trusts user input without sanitization or validation.
Accepting any file type for upload without checksAI suggests outdated packages with known vulnerabilities.
Using deprecated crypto libraries or old framework versionsNo authorization checks - authenticated users can access any data.
Any logged-in user can view/edit any other user's dataLLMs learn from public code, which includes insecure examples, outdated patterns, and tutorial code not meant for production.
AI doesn't understand your full application architecture, security requirements, or threat model.
AI prioritizes making code that works over code that's secure. Security is often an afterthought.
LLMs can't test the code they generate for vulnerabilities - they can only pattern match.
Training data has a cutoff date. New vulnerabilities and security best practices may not be included.
Check every API route for proper authentication and authorization before accepting AI code.
Run security scanners on AI-generated code before pushing to production.
Explicitly ask AI to consider security: 'Generate this with input validation and authentication'.
Check suggested packages for vulnerabilities using npm audit or similar tools.
VAS finds the vulnerabilities that AI coding tools introduce - missing auth, exposed secrets, insecure configurations, and more.
Free Security ScanNot inherently, but it often is in practice. AI optimizes for functionality, not security. It can generate secure code when properly prompted, but defaults to patterns that work rather than patterns that are safe. Always review security-critical code regardless of source.
No AI coding tool is 'secure' by default - they all generate similar vulnerability patterns. Claude, GPT-4, and Copilot all produce insecure code when not specifically prompted for security. The difference is in how you use them, not which one you choose.
No - AI dramatically improves productivity. But treat AI like a junior developer: review all code, especially authentication, authorization, and data handling. Use AI for boilerplate and logic, but always verify security yourself or with tools like VAS.
Be explicit about security requirements in prompts. Ask for: input validation, authentication checks, parameterized queries, and error handling. Request explanations of security measures. Still verify - AI can claim code is secure when it isn't.
Sometimes, but unreliably. AI can identify obvious issues when asked to review code, but misses subtle vulnerabilities and often falsely claims code is secure. Use dedicated security tools (SAST, DAST, VAS) rather than relying on AI for security review.
Last updated: January 16, 2026