DryRun Security’s 2026 Agentic Coding Security Report found that Claude, when operating as an autonomous coding agent, produces more unresolved high-severity security flaws than Codex or Gemini. But here’s the thing: all AI coding agents produce security vulnerabilities. The model matters less than your review process.

This guide walks you through a practical security audit workflow for AI-generated code, applicable regardless of which model or agent you’re using.

Before You Start: Understand the Risk Profile

AI-generated code has specific vulnerability patterns that differ from human-written code. Knowing what to look for saves time.

Common AI coding vulnerabilities:

  • SQL injection — AI agents often construct queries by string concatenation rather than parameterized queries, especially when moving quickly through tasks
  • Hardcoded credentials — agents sometimes embed API keys or passwords directly in code rather than using environment variables
  • Overly broad permissions — agents tend to request more access than needed, following patterns from training data
  • Missing input validation — agents optimize for “working” code, not necessarily code that handles unexpected inputs safely
  • Insecure defaults — SSL verification disabled, debug mode enabled, verbose error messages in production configs

Step 1: Automated Static Analysis (5 minutes)

Before human review, run automated tools. These catch the mechanical issues quickly.

For Python projects:

# Install bandit (Python security linter)
pip install bandit

# Run against your AI-generated code
bandit -r ./your-project/ -f txt

# Focus on high-severity issues first
bandit -r ./your-project/ -l high -f txt

For JavaScript/Node.js:

# Install eslint with security plugin
npm install eslint eslint-plugin-security --save-dev

# Add to your .eslintrc.json
{
  "plugins": ["security"],
  "extends": ["plugin:security/recommended"]
}

# Run the scan
npx eslint ./src/

For any language — Semgrep (free, open-source):

# Install semgrep
pip install semgrep

# Run with the security-focused rule set
semgrep --config=p/security-audit ./your-project/

# Or target specific frameworks
semgrep --config=p/django ./your-django-app/
semgrep --config=p/express ./your-express-app/

What to do with results: Triage by severity. Fix HIGH findings before proceeding. Document why you’re accepting any MEDIUM findings you choose not to fix.

Step 2: Credential and Secret Scan (2 minutes)

AI agents occasionally embed secrets directly in code. Catch these before git commit.

# Install truffleHog (open-source secret scanner)
pip install trufflehog

# Scan your directory
trufflehog filesystem ./your-project/

# Or scan your git history (critical if you've already committed)
trufflehog git file://./your-project/

Alternatively, use gitleaks:

# Install gitleaks
brew install gitleaks  # macOS
# or download binary from github.com/gitleaks/gitleaks

# Scan staged files before commit
gitleaks detect --staged

# Scan entire repository
gitleaks detect

If you find secrets in git history: Rotate the credentials immediately. Then clean the history (git-filter-repo or BFG Repo Cleaner). Never assume “nobody saw it” — treat every committed secret as compromised.

Step 3: Dependency Vulnerability Check (3 minutes)

AI agents pick dependencies from training data, which may include outdated packages with known CVEs.

Python:

pip install pip-audit
pip-audit

Node.js:

npm audit
# Fix automatically where safe:
npm audit fix

Any language — OWASP dependency-check:

# Docker version (no install required)
docker run --rm \
  -v $(pwd):/src \
  owasp/dependency-check \
  --project "my-project" \
  --scan /src \
  --out /src/reports

Step 4: Manual Review Checklist

Automated tools miss logic flaws and context-specific vulnerabilities. Spend 15-30 minutes on manual review focused on these areas:

Authentication and Authorization

  • Are all endpoints that should require authentication actually protected?
  • Can users access other users’ data by changing IDs in requests?
  • Are admin functions restricted to admin roles?

Input Handling

  • Is all user input validated before use?
  • Are file uploads restricted to expected types and sizes?
  • Are error messages sanitized (no stack traces or internal paths in production)?

Data Storage

  • Are passwords hashed (bcrypt, argon2, scrypt — not MD5 or SHA1)?
  • Is sensitive data encrypted at rest?
  • Are database queries parameterized (no string concatenation with user input)?

API and Network

  • Are all external API calls using HTTPS?
  • Is SSL certificate verification enabled (not verify=False in requests)?
  • Are rate limits implemented on public endpoints?

Configuration

  • Are environment variables used for all secrets (nothing hardcoded)?
  • Is debug mode disabled in production config?
  • Are CORS settings restrictive (not * for production APIs)?

Step 5: Test the Actual Behavior

Code that passes static analysis can still be vulnerable. Test the running application.

Quick penetration test with OWASP ZAP (free):

# Run ZAP in automation mode against your local server
docker run -t owasp/zap2docker-stable zap-baseline.py \
  -t http://localhost:8080

Test SQL injection manually:

  • In any form or URL parameter, try entering: ' OR '1'='1
  • If you get unexpected results (wrong data, errors), you likely have SQL injection

Test authentication:

  • Try accessing protected endpoints without a token
  • Try modifying a JWT token’s payload and resending it
  • Try accessing user B’s resources while authenticated as user A

Step 6: Integrate Into Your CI/CD Pipeline

One-time audits aren’t enough. AI-generated code is often updated rapidly. Automate security checks in your pipeline.

GitHub Actions example:

name: Security Audit
on: [push, pull_request]

jobs:
  security:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Run Bandit (Python)
        run: |
          pip install bandit
          bandit -r . -f json -o bandit-report.json || true
          
      - name: Check for secrets
        uses: trufflesecurity/trufflehog@main
        with:
          path: ./
          
      - name: Upload security reports
        uses: actions/upload-artifact@v3
        with:
          name: security-reports
          path: '*-report.*'

The DryRun Lesson, Applied

The DryRun Security report found Claude-generated code had more unresolved flaws. “Unresolved” is the operative word — these were flaws in final output, presumably code that was shipped or was ready to ship.

The mitigations above address exactly the “unresolved” problem: they’re the checkpoints that should catch vulnerabilities before they reach production. None of them are exotic. All of them are free or cheap. The barrier is process discipline, not tooling cost.

If you’re deploying AI-generated code without running at minimum steps 1-3 above, you’re shipping code you haven’t reviewed. The DryRun findings are a reason to start doing so — regardless of which model is generating your code.

Tools Referenced

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260311-2000

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