Introduction

AI-generated code from tools like GitHub Copilot and Cursor accelerates development but introduces hidden risks: 62% of AI-generated solutions contain security flaws, including hardcoded secrets, SQLi, and insecure dependencies. Traditional SAST tools struggle with probabilistic code patterns, creating a critical gap in modern DevSecOps pipelines.

Endor Labs’ $93M-funded platform addresses this with AI-native static/dynamic analysis, scanning LLM outputs for context-aware vulnerabilities. This guide walks through local setup, CI/CD integration (with GitHub Actions examples), and custom rule creation to secure AI-generated code before deployment.

Why This Matters: 40% of today’s code is AI-generated—expected to hit 80% soon. Without specialized scanning, teams risk deploying vulnerable code at scale.

Prerequisites

Before implementing Endor Labs:

  • Tools:
    • GitHub/GitLab account
    • Python 3.8+ (python --version)
    • Docker (for local sandbox testing)
  • Knowledge:
    • Basic CI/CD concepts (e.g., GitHub Actions workflows)
    • Familiarity with static analysis (SAST)

1. Why AI-Generated Code Needs Specialized Scanners

The Unique Risks of LLM-Generated Code

AI coding assistants often produce:

  • Context-blind vulnerabilities: Example Copilot output suggesting os.system(user_input) without input sanitization.
  • Hallucinated dependencies: Non-existent packages or outdated versions with known CVEs.
  • Placeholder secrets: Temporary API keys in comments (# SECRET_KEY="temp_123").

Traditional SAST tools fail here because:

  1. Rule-based detection lacks probabilistic code understanding.
  2. False negatives spike with unconventional code structures.

How Endor Labs Adapts

Endor uses NLP-trained analyzers to:

  • Detect AI-specific anti-patterns (e.g., insecure prompt-injected code).
  • Build a call graph of application functionality for contextual risk assessment.
  • Flag design flaws (e.g., AI-suggested auth bypasses).

Case Study: Endor’s AI agents caught a Copilot-suggested JWT implementation skipping signature verification in 73% of test cases—a miss for traditional SAST.

2. Setting Up Endor Labs for Local Testing

Installation

pip install endor-labs-scanner  # CLI tool
docker pull endorlabs/sandbox   # Isolated testing

Configuration

Create endor.yaml:

scan:
  ai_model: github_copilot  # Also supports 'cursor', 'general'
  ruleset: security_high    # Options: security_high, balanced, full
exclusions:
  - "**/test_*.py"         # Ignore test files

Pre-Commit Hook

Add to .git/hooks/pre-commit:

#!/bin/sh
endor scan --diff --fail-on=critical

[Diagram: Endor’s Pre-Commit Flow]

Developer → Writes Code → Pre-Commit Hook → Endor Scan 
 → IF Vulnerabilities → Block Commit 
 → ELSE → Proceed

3. Integrating with CI/CD Pipelines (GitHub Actions)

GitHub Actions Workflow

Add to .github/workflows/endor_scan.yml:

name: Endor Security Scan
on: [pull_request]

jobs:
  scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: endor-labs/scan-action@v2
        with:
          fail_on: critical         # Block PR on critical issues
          config: ./.endor.yaml    # Custom rules path
          annotations: true        # Inline PR comments

Handling False Positives

Exclude false positives via .endorignore:

# Example: Ignore safe debug statements
pattern: console.log\(\s*".*password.*"\)  
reason: Debug log, no credential risk

4. Advanced: Custom Rules for AI-Generated Code

Regex Example for Copilot Temp Keys

Add to endor.yaml:

custom_rules:
  - id: AI_TEMP_KEY
    pattern: '#\s*(KEY|SECRET|PASSWORD)\s*=\s*["\'].*["\']'
    severity: high
    message: "AI-generated placeholder secret detected"

Rule Types

  • Pattern Matching: Regex for hardcoded secrets ([A-Za-z0-9]{32}).
  • Contextual: Flag # TEMPORARY comments near auth logic.
  • Dependency: Detect AI-hallucinated packages (import fakelib).

Pro Tip: Combine Endor’s CLI with jq for automated reporting:

endor scan --json | jq '.results[] | select(.severity == "critical")'

5. Troubleshooting & Performance Optimization

Security vs. Performance Trade-offs

Setting Security Impact Speed Impact Use Case
--quick-scan Lower recall 2x Faster PR Checks
--deep-scan Higher precision 3x Slower Release Branches
confidence_threshold: 0.7 Fewer FPs, more FNs 1.5x Faster Noise-Sensitive Teams

Comparison with Other Tools

Tool AI-Code Optimized CI/CD Native Custom Rules Precision*
Endor Labs Yes Yes Yes 92%
Snyk Partial Yes Limited 68%
Semgrep No Yes Yes 74%
*Precision for detecting AI-specific vulnerabilities in Endor benchmark datasets.

Common Pitfalls

  1. Overscanning: Limit to diff scans (--diff) in CI to avoid full-repo latency.
  2. Threshold Misconfiguration: Start with fail_on: critical, then tune.
  3. Docker Overhead: Use --no-sandbox for cloud runners lacking Docker.

Conclusion

Automating vulnerability scanning for AI-generated code with Endor Labs reduces breach risks by 83% (internal benchmarks) while keeping pace with AI-driven development. Key takeaways:

  1. Start Small: Integrate pre-commit hooks before full CI/CD rollout.
  2. Tune Precision: Adjust confidence_threshold based on your FP tolerance.
  3. Extend with Custom Rules: Tailor to your AI tools’ common anti-patterns.

Next Steps:

“AI won’t replace developers—but developers using AI will replace those who don’t. Secure the advantage.” — Varun Badhwar, Endor Labs CEO