How to Audit Your AI-Generated Codebase

You Shipped It. Now What?

Your vibecoded product is live. Users are signing up, the core flow works, and you're starting to think about growth. Congratulations — you've done the hardest part.

But before you scale, you need to know what you're sitting on. Not because you did anything wrong, but because AI coding tools optimize for "does it work when I click through it" — not for "is it safe for 10,000 paying users."

We covered the common vulnerabilities in our previous post. This one gives you something more practical: a checklist you can run yourself, today, to find out how much trouble you're in.

Think of it as due diligence on your own product. Every serious founder does it before raising, before going enterprise, and before handing real customer data to a codebase that was built by autocomplete.

The Five-Point Audit

You don't need to be a security engineer to run these checks. You need a browser, your database access, and about an afternoon.

1. Authentication & Authorization

This is the most common and most dangerous problem in AI-generated code. The AI builds login — but it rarely builds proper access control.

What to test: Log in as one user. Find any page that shows user-specific data — a dashboard, a settings page, an order history. Look at the URL or the network request in DevTools. If there's an ID in the URL or API call, change it to a different number.

Can you see another user's data? That's an IDOR vulnerability, and it's the number one issue we find in vibecoded products. Also check: can a regular user access admin endpoints? Can a logged-out user access authenticated routes by going directly to the URL?

2. Input Validation & Injection

AI tools generate code that trusts user input. That's a problem when real users — or attackers — send something unexpected.

What to test: Find any form or input field. Type <script>alert('xss')</script> and submit. If you see an alert box, your app is vulnerable to cross-site scripting. Try a single quote ' in search fields and see if you get a database error — that hints at SQL injection. Look at your codebase for raw database queries that interpolate user input directly instead of using parameterized queries.

3. Secrets & Configuration

AI assistants love hardcoding API keys. They'll put your Stripe secret key in a React component, your database password in a config file that gets committed to git, and your JWT secret in a shared constant.

What to test: Open your app in a browser. Open DevTools, go to the Sources tab, and search the JavaScript bundles for strings like sk_, secret, password, api_key, or token. If you find anything that looks like a credential, it's visible to every user who visits your site. Also run git log --all -p -S 'SECRET' in your repo to find any secrets that were ever committed — even if they were later removed, they're still in the git history.

4. Database Performance

Your app works great with 50 records. But AI-generated code frequently has performance patterns that collapse at scale.

What to test: Open DevTools, go to the Network tab, and load a page that shows a list of items. Count the API calls. If loading a list of 20 items triggers 20+ separate requests, you've got an N+1 query problem. Enable query logging on your database and watch what happens on a single page load. If you see dozens of queries — especially repeated ones — you're missing database indexes or your data fetching needs restructuring.

5. Error Handling

AI builds the happy path. It rarely builds for failure.

What to test: Turn off your network connection and use the app. Submit a form with an empty required field. Upload a file that's 100MB. Hit the back button mid-flow. Send a request with a malformed JSON body. Does the app show a helpful error, or does it crash? Worse — does it show a stack trace with internal details like file paths, database names, or server versions? Stack traces in production are both a bad user experience and an information leak.

What Each Finding Means

Not everything you find needs to be fixed before tomorrow. Here's how to prioritize:

Fix immediately — any issue from checks 1–3. Exposed user data, injection vulnerabilities, and leaked credentials are critical security issues. These can result in data breaches, regulatory penalties, and total loss of user trust. If you found an IDOR or a hardcoded API key, stop reading and fix it now.

Fix before scaling — database performance issues from check 4. These won't hurt you at low traffic, but they'll create cascading failures once you grow. Missing indexes turn millisecond queries into multi-second queries. N+1 problems turn a single page load into hundreds of database round trips.

Fix before enterprise sales — poor error handling from check 5. Enterprise buyers will run their own security assessments. Stack traces in production and ungraceful failures will flag you as immature. This won't lose you data, but it will lose you deals.

Tools You Can Use Today

You don't need expensive security software. These free tools cover the basics:

Browser DevTools — your first line of defense. The Network tab shows every API call, including response bodies that might contain more data than the UI displays. The Sources tab lets you search frontend bundles for leaked secrets. The Console shows errors the UI hides from users.

OWASP ZAP — a free, open-source security scanner that crawls your app and tests for common vulnerabilities from the OWASP Top 10. It won't catch everything, but it'll catch the obvious problems.

Database query logging — every major database supports it. Turn it on, load a few pages, and look at what your app is actually doing. You'll be surprised.

Git history search — git log --all -p -S 'API_KEY' searches every commit in your repo's history for a string. Replace API_KEY with patterns like your provider names (Stripe, AWS, OpenAI) to find credentials that were committed and later removed. They're still in the history, and anyone who clones your repo can find them.

When to Call in Help

Here's the honest answer: if you found issues in the first three checks — auth, injection, or secrets — you almost certainly need an experienced engineer to do a proper code review.

The reason is simple. These problems are rarely isolated. If the AI didn't implement authorization checks on one endpoint, it probably didn't implement them on any endpoint. If there's one hardcoded secret, there are usually more. Surface-level fixes won't catch the patterns.

A professional audit — whether that's a penetration test, a full code review, or a security-focused engineering engagement — will map the full scope of the problem and give you a prioritized remediation plan.

Productera offers exactly this kind of audit for vibecoded products, but we're not the only option. What matters is that someone with security engineering experience looks at your codebase before you scale. The cost of an audit is a rounding error compared to the cost of a data breach.

If your five-point audit came back clean — no auth issues, no injection, no leaked secrets — you're in better shape than most. Focus on performance and error handling, and consider a professional review before your next major milestone.

Related glossary terms: Vibecoding · Code Review · IDOR · OWASP Top 10 · Penetration Testing · SQL Injection · Authentication vs Authorization