Research shows AI-generated code contains more vulnerabilities than human-written code. Here's what the data says and how to protect your applications.
Find vulnerabilities AI introduced to your codebase.
of AI-generated code contains security vulnerabilities
of AI-generated code has at least one security flaw
of developers who used AI wrote less secure code
increase in exposed API keys since AI coding tools became mainstream
AI models trained on tutorial code that skips auth for simplicity
AI suggests example code patterns that use inline secrets
AI generates syntactically correct but insecure query patterns
AI doesn't understand security context, suggests patterns that 'work'
AI trained on older code with outdated crypto practices
AI suggests packages it was trained on, which may be outdated
Developers who believe AI-generated code is secure are more likely to introduce vulnerabilities. The confidence AI provides leads to reduced code review.
Source: Stanford, 2023
AI generates more secure code when given security-specific prompts. Generic prompts produce code optimized for functionality, not security.
Source: Various studies
AI generates code that looks correct by pattern-matching training data. It doesn't understand why certain patterns are insecure.
Source: NYU, 2023
AI models trained on historical code suggest outdated security practices. Crypto recommendations and library versions lag behind current best practices.
Source: Veracode, 2025
Explicitly ask for secure code. Mention specific security requirements in your prompts.
Example: Generate a secure login function using bcrypt for password hashing
Review all AI-generated code, especially authentication, data handling, and security-critical functions.
Example: Establish review checklists for AI-generated code
Use security scanners to catch vulnerabilities AI introduces. Run scans before every deployment.
Example: Integrate SAST/DAST into CI/CD pipelines
Instead of accepting AI-generated security code, use established libraries like Auth0, bcrypt, Helmet.
Example: Replace AI auth code with NextAuth or Clerk
Check that AI-suggested packages exist, are maintained, and are secure. Run npm audit.
Example: Verify package names and run security audits
Our scanner specifically checks for vulnerabilities commonly introduced by AI coding tools - exposed secrets, auth issues, injection vulnerabilities, and more.
Scan Your App FreeResearch suggests yes, on average. Studies show AI-generated code contains more vulnerabilities, and developers using AI are more likely to introduce security flaws. However, AI can also help find vulnerabilities - the key is using it correctly.
No AI coding tool guarantees secure code. Copilot, Cursor, Claude, and others all generate code with similar vulnerability patterns. Security depends on how you use the tool, not which tool you use.
No. Authentication is the most common category of AI-generated vulnerabilities. Always use established auth libraries (Auth0, NextAuth, Clerk) instead of AI-generated auth implementations.
Include security requirements in your prompts ('use parameterized queries', 'hash passwords with bcrypt'). But always review the output - prompting helps but doesn't guarantee security.
No, but use them thoughtfully. AI accelerates development but requires security awareness. Treat AI suggestions as starting points, not finished code. Review everything, especially security-critical code.