How to Audit Your AI-Generated Code: A Non-Technical Founder's Checklist

You shipped your MVP in a weekend with Bolt, Cursor, or v0. The UI looks great. The demo works. You're ready for users.

But there's a question most non-technical founders don't ask until it's too late: Is the code actually secure?

AI coding tools generate code by learning from public repositories. Some of those repos are well-engineered. Many are not. When you "vibe code" your way to a working product, you inherit both the good patterns and the bad ones — without anyone flagging which is which.

This guide walks through a practical audit you can run on any AI-generated codebase. No CS degree required.

---

Why AI-Generated Code Needs Auditing

AI models don't know your business constraints. They don't know you handle user data, process payments, or operate in regulated industries. They generate code that looks correct — but "looks correct" and "is production-safe" are very different things.

Common issues in AI-generated code:

• Hardcoded secrets — API keys and passwords embedded in source files
• Missing input validation — forms that accept anything, opening injection vulnerabilities
• Placeholder functions — handlePayment() that logs instead of charging cards
• Broken authentication — sessions that don't actually verify passwords
• No error boundaries — entire pages crash when one API call fails

These aren't theoretical. They're found in almost every AI-generated codebase we audit.

---

The 5-Minute Security Checklist

1. Search for hardcoded secrets

Run this search across your repository:

grep -rni "api_key\|password\|secret_token\|private_key" --include="*.js" --include="*.ts" --include="*.py" --include="*.env*"

Red flags:

• Any secret value in a .js, .ts, or .html file (these ship to the browser)
• Database passwords in config files committed to git
• Stripe keys, AWS credentials, or API tokens anywhere in source code

Fix: Move all secrets to environment variables. Never commit .env files. Rotate any exposed keys immediately.

---

2. Check your authentication flow actually works

Log out. Create a new test account with a fake password. Try logging in with the wrong password. Does it reject you?

Red flags:

• Any password works (authentication is stubbed)
• No password hashing visible in the code
• Session tokens never expire
• No logout functionality

Fix: Use a battle-tested auth library (e.g., NextAuth.js, Clerk, Auth0). Don't roll your own authentication.

---

3. Verify input validation on every form

Open your signup form. Try entering:

• An invalid email address
• A 10,000-character name
• JavaScript code in text fields (<script>alert('xss')</script>)

Red flags:

• Any input accepted without error
• Data saved to database without sanitization
• User input rendered directly in HTML without encoding

Fix: Add server-side validation for every field. Sanitize all inputs before storing or displaying them.

---

4. Test your payment flow with real money

If you integrated Stripe or another payment provider, make a real $1 test charge. Then refund it.

Red flags:

• Payment form submits but nothing charges
• Credit card numbers hardcoded in the code
• Webhook signatures not verified
• No receipt or confirmation generated

Fix: Use Stripe's test mode first. Verify webhook endpoints. Never store raw card numbers.

---

5. Check for missing error handling

Disconnect your internet. Refresh your app. Does it gracefully degrade, or show a blank white screen?

Red flags:

• White screens when APIs fail
• No loading states
• Infinite spinners
• Error messages that expose internal details (database names, file paths)

Fix: Wrap every API call in try/catch. Show user-friendly error messages. Log errors server-side for debugging.

---

Going Deeper: Automated Security Scanning

Manual checks catch the obvious issues. But AI-generated codebases often have subtle vulnerabilities that require static analysis tools to detect:

• Semgrep — finds injection vulnerabilities, insecure patterns, and secret leaks
• Bandit — Python-specific security scanner
• ESLint with security plugins — catches unsafe JavaScript patterns
• Dependency checks — flags known vulnerable libraries

Running these tools gives you a baseline security score. But interpreting the output is the hard part — they produce jargon-filled reports that assume you already know what "CWE-89" means.

---

When You Need a Second Pair of Eyes

If any of the following apply, consider a professional audit:

• You handle user data (emails, passwords, personal information)
• You process payments
• You're launching publicly (not just friends-and-family)
• You used multiple AI tools (the complexity compounds)
• You don't have a technical co-founder who can review the code

This is exactly why we built SecondRead — a plain-English code audit service for non-technical founders who built with AI tools.

Here's how it works:

1. Send us your GitHub repo link (or zip the code)
2. We run Semgrep, Bandit, and custom scans across your entire codebase
3. You get a report in plain English — no jargon, just "this is broken, here's why, here's how to fix it"
4. Severity ratings help you prioritize what to fix before launch

Free during beta. No signup required. Email [e-mail] with your repo URL, or visit secondread.me.

---

Summary: Your Pre-Launch Checklist

• [ ] No secrets in source code
• [ ] Authentication actually verifies passwords
• [ ] Every form validates input server-side
• [ ] Payment flow tested with real transactions
• [ ] Error handling tested by disconnecting APIs
• [ ] Dependencies checked for known vulnerabilities
• [ ] One technical review before public launch

Building with AI is the fastest way to validate an idea. But shipping without checking the code is like launching a plane without inspecting the engine.

Take an hour. Run these checks. Your future users will thank you.

---

SecondRead provides plain-English code audits for non-technical founders who built with AI tools. Free during beta at secondread.me.