Why Traditional Scanners Miss AI-Generated Code Issues

Traditional static analysis tools were built for human-written code. They assume certain patterns—developers don’t usually hardcode API keys in obvious places, input validation exists somewhere, authentication flows follow standard patterns.

AI-generated code breaks these assumptions constantly. I’ve seen Cursor generate perfectly functional login systems with authentication tokens stored in localStorage. Lovable apps with admin endpoints that accept any JWT. Claude Code implementations where environment variables get committed because the .gitignore was generated after the secrets file.

The scanners that work in 2026 understand these patterns. They’re trained on AI-generated code, they know the common shortcuts, and they flag what matters.

The Scanners Worth Using

Snyk

Snyk remains the most mature option for CI/CD integration. Their AI vulnerability detection improved significantly in late 2025, and they now flag common AI coding patterns like insecure randomness and missing CSRF protection.

What it catches well:

• Dependency vulnerabilities (still their strength)
• Hardcoded secrets in AI-generated code
• Common injection patterns

What it misses:

• Prompt injection vectors
• Context-specific auth bypass
• Business logic flaws

Best for: Teams with existing CI/CD pipelines who want drop-in security.

Semgrep

Semgrep’s custom rule capability makes it powerful for AI code. You can write rules that catch patterns specific to your vibe coding workflow—like flagging any direct database query in a Lovable-generated component.

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rules:
  - id: ai-generated-sql-injection
    patterns:
      - pattern: |
          $DB.query($USER_INPUT)
    message: "Direct user input in database query - common AI code pattern"
    severity: ERROR

Best for: Teams who want control over what gets flagged.

VibeEval

Full disclosure: I built this. VibeEval runs 200+ security checks specifically designed for AI-generated code. It understands prompt injection, catches auth bypass patterns that Lovable commonly generates, and flags the specific issues that AI coding tools create.

What makes it different:

• Rules trained on millions of AI-generated code samples
• Catches prompt injection in LLM-powered features
• Understands vibe coding tool patterns (Cursor, Lovable, Bolt, Replit)

Best for: Anyone shipping AI-generated code to production.

GitHub Advanced Security

GitHub’s CodeQL has added AI-specific queries. If you’re already on GitHub Enterprise, this is worth enabling. The secret scanning catches most hardcoded credentials before they hit main.

Best for: GitHub Enterprise users who want native integration.

How to Actually Use These Tools

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# .pre-commit-config.yaml
repos:
  - repo: https://github.com/returntocorp/semgrep
    rev: v1.45.0
    hooks:
      - id: semgrep
        args: ['--config', 'p/security-audit']

What Scanners Can’t Catch

No scanner catches everything. These still require manual review:

1. Business logic flaws — A scanner can’t know that users shouldn’t access other users’ data
2. Authorization bypass — Context-dependent, requires understanding your app
3. Subtle prompt injection — Indirect injection through stored content
4. Race conditions — Timing issues in async code

Use scanners for the obvious stuff. Reserve human review for the things that require understanding intent.

FAQ

Conclusion