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How to Audit AI-Generated Code for Security Issues

A practical guide to reviewing code from Cursor, Claude, Copilot for security vulnerabilities before it reaches production.

AI-generated code can look correct while being insecure. Auditing it effectively requires knowing what to look for and building habits that catch issues before they become problems.

Why Auditing AI Code Is Different

AI assistants are trained on public code, which includes plenty of insecure examples. They’re optimizing for “compiles and runs,” not “secure against malicious input.” This means code that works perfectly in testing might have serious vulnerabilities.

The challenge is that AI-generated code often looks authoritative. It’s formatted correctly, follows patterns that seem reasonable, and comes with the implicit confidence of a tool that “knows” many programming patterns. This can make developers less critical than they’d be reviewing code from a junior colleague.

The Quick Security Check

Every piece of AI-generated code should get a quick security scan before acceptance. This takes about thirty seconds and catches the most common issues.

First, look for hardcoded credentials. Scan the code for strings that look like API keys (often starting with sk_, api_, or similar prefixes), tokens, passwords, or connection strings. AI frequently embeds these directly in code.

Second, check database interactions. If you see user input being inserted into SQL queries via string concatenation, that’s a SQL injection vulnerability. The query should use parameterized statements, not string interpolation.

Third, look at how user input is handled. If data from a request goes directly to a database, file system, or external service without validation, that’s a problem. There should be checks that the input is what you expect before using it.

Fourth, check authentication and authorization. Does the code verify that users have permission for the actions they’re taking? AI often omits these checks in the interest of simplicity.

The Deep Audit

For significant features or before releases, do a more thorough review.

Run automated tools. Secret scanners like detect-secrets or gitleaks catch credentials that escaped your notice. Dependency scanners like npm audit or snyk find vulnerable packages the AI suggested. Static analyzers like ESLint with security plugins catch common vulnerability patterns.

Trace the data flow. Pick a piece of user input and follow it through the code. Where does it go? What operations use it? Is it validated before use? Is it escaped before output? Many vulnerabilities exist at the boundaries where user data meets system operations.

Check error handling. Does the code expose system information in error messages? Are stack traces hidden from users? Does failed authentication give too much information about why it failed?

Review permissions. Does every operation that should require authentication actually require it? Can users only access their own data, or could they manipulate requests to see others’ data? Are admin functions properly protected?

High-Risk Areas

Some types of AI-generated code deserve extra scrutiny.

Authentication and authorization code is where security matters most and where AI frequently takes shortcuts. Never accept AI-generated auth code without careful review.

Payment and billing integration handles money and sensitive data. Review everything, test edge cases, and consider whether a professional security review is warranted.

File handling operations can be vulnerable to path traversal attacks. If the AI generates code that uses user input to construct file paths, verify that input is properly validated.

External API calls might leak credentials, be vulnerable to injection, or create SSRF vulnerabilities. Check what data is sent and how.

User-generated content handling is where XSS vulnerabilities hide. Ensure output is properly escaped.

Making Auditing Sustainable

Security auditing is only useful if you actually do it consistently. A few practices help:

Build it into your workflow rather than treating it as a separate step. Check for security issues at the same time you review functionality.

Use automation for what can be automated. Secret scanning, dependency checking, and static analysis should run automatically. Save your attention for the things that require human judgment.

Start with the highest-risk areas. If you’re short on time, prioritize auth, payments, and data handling over utility functions.

Use tools that work in real-time. mrq includes security scanning that runs as code is generated, catching issues at the earliest possible moment. This is more effective than catching them at commit time or in CI.

When to Get Help

Some security reviews need expertise beyond code auditing. If you’re handling payment data, personal health information, or operating in a regulated industry, consider professional security review. If you’re building authentication systems for significant user counts, likewise.

AI-generated code for these use cases should be treated as a starting point, not a finished solution.

The Sustainable Approach

Perfect security review of every line is impractical. A sustainable approach prioritizes:

Real-time scanning to catch obvious issues immediately, like exposed credentials Quick manual checks for common patterns before accepting code
Deep review for high-risk areas and before releases
Professional audit for production systems handling sensitive data

This layered approach catches most issues without slowing you down unreasonably.

mrq includes automatic security scanning that catches vulnerabilities in real-time, as AI generates code.

Written by mrq team