Claude Code Security Checklist: How to Review AI-Generated Code Safely

Claude Code Security Checklist: How to Review AI-Generated Code Safely

Claude Code and other AI coding assistants generate impressive code at speed. But that speed comes with risk. AI models can introduce security vulnerabilities, expose secrets, generate insecure defaults, and create dependencies you never explicitly approved. The NCSC’s 2026 warning about vibe coding highlighted 35 CVEs traced to AI-generated code and a 322% increase in privilege escalation from unchecked AI suggestions.

This checklist walks you through every security review step you should take before merging code generated by Claude Code, Cursor, Copilot, or any AI assistant.

Why AI-Generated Code Needs a Different Review Process

Traditional code review assumes the author understood what they wrote. With AI-generated code, that assumption breaks down. A developer who pastes a Claude Code suggestion into their project may not understand the security implications of every line — especially when the code looks clean and functional.

Key differences from reviewing human-written code:

• Hidden intent: AI code may include patterns that serve the model’s training data rather than your security requirements. A suggestion might use an insecure default because it was more common in the training data.
• Dependency sprawl: AI assistants frequently suggest importing new packages. Each import is a potential supply chain risk. A 2025 study found that 91.5% of applications built with vibe coding contain vulnerabilities.
• Secret leakage: AI models can reproduce real API keys, tokens, and credentials from their training data. If the model suggests a key pattern, it might be an actual leaked credential.
• Overconfidence: Developers trust AI-generated code more than they should. Studies show developers are less likely to question code that “looks right” from an AI assistant.

For a broader overview of AI code security risks, visit our AI code security hub.

The Claude Code Security Checklist

Use this checklist every time you review code generated by Claude Code or any AI assistant:

1. Secret and Credential Check

• ☐ Scan for hardcoded API keys, tokens, passwords, and connection strings
• ☐ Run TruffleHog or Gitleaks on the changed files to catch leaked secrets in diffs
• ☐ Verify that any environment variables referenced are defined in .env.example and documented in README
• ☐ Check that no real credentials are committed — only references to environment variables
• ☐ Confirm .gitignore includes .env, *.key, *.pem

2. Input Validation and Injection Prevention

• ☐ Identify all user inputs (form fields, URL parameters, headers, file uploads)
• ☐ Verify SQL queries use parameterized statements, not string concatenation
• ☐ Check for XSS: ensure HTML output is escaped, not raw-inserted
• ☐ Look for command injection in exec(), system(), os.popen(), or shell pipes
• ☐ Validate file paths against path traversal (../, ..\)
• ☐ Confirm request body size limits are set

3. Dependency and Supply Chain Review

• ☐ List all new dependencies introduced by the AI suggestion
• ☐ Run npm audit, pip audit, or equivalent for each new dependency
• ☐ Check if each dependency is maintained, has recent releases, and reasonable download counts
• ☐ Verify lockfile integrity (package-lock.json, Pipfile.lock, go.sum)
• ☐ Confirm no typosquatted packages (e.g., reqeusts instead of requests)

4. Authentication and Authorization

• ☐ Verify all protected endpoints require authentication
• ☐ Check that authorization checks exist (not just authentication — can user A access user B’s data?)
• ☐ Confirm session management follows best practices (secure cookies, HttpOnly, SameSite)
• ☐ Look for hardcoded roles or permissions that should be configurable
• ☐ Verify CORS settings are not overly permissive (* origins)

5. Error Handling and Information Leakage

• ☐ Confirm error messages don’t expose stack traces, database schemas, or internal paths
• ☐ Verify that 500 errors return generic messages, not debugging details
• ☐ Check that logging doesn’t record sensitive data (passwords, tokens, PII)
• ☐ Ensure debug mode is disabled in production

6. Configuration and Infrastructure

• ☐ Review infrastructure-as-code changes for insecure defaults (open security groups, public S3 buckets)
• ☐ Verify HTTPS is enforced, not optional
• ☐ Check that security headers are present (Content-Security-Policy, X-Frame-Options, X-Content-Type-Options)
• ☐ Confirm Docker images use minimal base images and non-root users
• ☐ Verify CI pipeline secrets are referenced from vault, not hardcoded in YAML

Decision Table: How to Handle Common AI Code Patterns

AI Suggestion Pattern	Risk Level	Action
New import/package	Medium	Audit the package, check maintenance, run vulnerability scan
Database query	High	Verify parameterized, not string-concatenated
File system operation	High	Check for path traversal, validate inputs
API endpoint handler	High	Verify auth, input validation, rate limiting
Environment variable	Medium	Check it’s not a real secret, add to .env.example
Error handler	Medium	Ensure no stack traces or internal details leak
Config file change	Medium	Verify security headers, HTTPS, minimal permissions
Comment/documentation	Low	Quick scan for accidental secret inclusion

What Not to Do When Reviewing AI-Generated Code

• Don’t assume “it works” means “it’s secure.” AI-generated code often works correctly for the happy path but fails on edge cases and security inputs.
• Don’t skip the dependency check. The most dangerous AI habit is importing packages you’ve never heard of. Each new dependency is an attack surface.
• Don’t trust the AI’s self-assessment. If Claude Code tells you “this is secure,” that doesn’t make it true. The model doesn’t have your threat model.
• Don’t merge without running secret detection tools. Use TruffleHog or Gitleaks on every diff before merging AI-generated code.
• Don’t ignore CI pipeline failures. If your CI gates catch a vulnerability, fix it before merging — don’t bypass the check.

Integrating the Checklist Into Your Workflow

Here’s a practical workflow for teams using Claude Code:

1. Pre-commit: Run Gitleaks locally on staged files. If it finds anything, fix it before pushing.
2. Pull request: Use the checklist above as a PR template. Require reviewers to check each item.
3. CI pipeline: Run TruffleHog (with verification) and npm/pip audit on every push. Fail the build on verified secrets or high-severity vulnerabilities.
4. Post-merge monitoring: Set up alerts for new dependency vulnerabilities in production (Dependabot, Snyk, or CodeRiskTools’ own scanners).

For a more detailed CI setup, see our guide on CI gates for AI-generated code and the free 5-point AI code review checklist.

Frequently Asked Questions

Is Claude Code safe to use in production projects?

Claude Code is a powerful tool, but like any AI assistant, it generates code without understanding your specific security requirements. Always review its suggestions against your security checklist before merging. The risk isn’t the tool — it’s skipping the review.

How is reviewing AI code different from reviewing human code?

AI code has no intent behind it. The model doesn’t know your threat model, your compliance requirements, or your internal security policies. You need to check for things a human author would intuitively avoid — like hardcoded secrets from training data, insecure defaults from popular-but-outdated Stack Overflow patterns, and unnecessary dependency imports.

Should I run secret detection on every commit?

Yes. At minimum, run secret detection in pre-commit hooks and CI pipelines. AI assistants can accidentally reproduce real secrets from their training data, and the sooner you catch them, the lower your risk. Use Gitleaks for speed in pre-commit and TruffleHog for depth in CI.

What’s the biggest security risk from AI-generated code?

According to the Keyhole Software 2026 study, 91.5% of applications built with vibe coding contained vulnerabilities, with XSS rates 2.74x higher than manually coded apps. The biggest risk isn’t a single dramatic vulnerability — it’s the accumulation of small insecurities: unvalidated inputs, unnecessary dependencies, and skipped reviews because the code “looks right.”

Next Steps

Reviewing AI-generated code doesn’t have to be slow or complicated — but it does have to be consistent. Use this checklist as a starting point and adapt it to your team’s threat model.

Download the free 5-point AI code review checklist for a condensed version you can use on every pull request. For deeper analysis of how local security tools compare to cloud SaaS alternatives, see our full comparison page.

Explore more resources at the AI code security hub — including guides on secret scanning for AI-generated code, handling Gitleaks findings, and GitHub Actions security.

Need a professional second pair of eyes on your codebase? The Expert AI Code Security Audit gives you a human-reviewed security assessment of your most critical repositories — not just automated scans, but real analysis of AI-generated code risks.