The Vibecoding Trap: When Your AI-Built Product Becomes a Liability

You Built It. It Works. Now What?

You described your product to Cursor, Claude, or Bolt. You iterated for a few weeks. Now you have a working app — users can sign up, the core flow works, and you've shown it to investors. It feels like you've built something real.

You have. But you've also probably built something dangerous.

We've audited dozens of vibecoded products over the past year. The pattern is consistent: the app works, the founder is thrilled, and the codebase is hiding problems that would make any security engineer lose sleep.

This isn't about code quality snobbery. These are problems that will lose you customers, expose you to lawsuits, and kill deals with enterprise buyers.

What's Actually Lurking in Your Codebase

Here are real issues we've found in AI-generated codebases from non-technical founders. Every single one came from a product that "worked fine."

Your users' data is probably exposed. We reviewed a fintech app where any logged-in user could access any other user's financial records by changing the ID in the URL. The AI wrote the API endpoints but never added authorization checks. The founder had no idea — the app worked perfectly when testing with one account.

Your API keys are in the code. AI assistants regularly hardcode Stripe keys, database credentials, and third-party API secrets directly into frontend code. This means anyone who opens browser dev tools can see your payment processor credentials. We found a live Stripe secret key in a React component last month.

You have no rate limiting. Without it, anyone can hit your API thousands of times per second. A single script could drain your Stripe account through repeated small charges, flood your database with garbage, or take your app offline.

Your database will choke. AI generates queries that work with 10 records. With 10,000 records, those same queries take 30 seconds. We've seen dashboards that run a separate database query for every single row in a table — invisible with test data, unusable in production.

Why AI Keeps Making These Mistakes

AI coding tools optimize for the thing you can see: does it work when I click through it? They don't optimize for things you can't see: security, performance under load, data integrity, and error handling.

When you tell an AI to "build a user dashboard," it builds a user dashboard. It doesn't think about what happens when two users update the same record simultaneously. It doesn't consider that the file upload should validate file types. It doesn't add monitoring so you know when something breaks at 2 AM.

The AI isn't being lazy. It's doing exactly what you asked. The problem is that production software requires hundreds of things nobody thinks to ask for.

The Scale Wall

Every vibecoded product hits a wall. It usually looks like this:

• 50–100 users: Everything works fine. You're confident.
• 500 users: Page loads start slowing down. Some users report weird bugs.
• 1,000 users: The app occasionally crashes. Database queries timeout. Users see each other's data.
• 5,000 users: You're spending entire days firefighting. New features are impossible because every change breaks something else.

The problem isn't that the code is bad — it's that it was never built for more than a demo. There's no caching, no connection pooling, no background job processing, no CDN. The architecture assumes a single user clicking through a happy path.

What You Should Actually Do

If you've vibecoded a product and it's gaining traction, here's the honest playbook:

Get a security audit before you handle real money or real data. Not after your launch. Not when you raise your Series A. Before your first real customer trusts you with their information. A security review costs a fraction of a data breach.

Don't rewrite — harden. Most vibecoded products don't need to be thrown away. The UI works, the user flow is validated, the idea is proven. What's needed is an experienced engineer to add the invisible infrastructure: auth checks, input validation, error handling, database indexes, rate limiting, and monitoring.

Treat the vibecoded version as a prototype, not a product. The gap between "it works on my laptop" and "it works for 10,000 paying users" is where engineering comes in. This isn't about making the code prettier. It's about making it safe.

The Question to Ask Yourself

Open your app. Find a page that shows user-specific data. Now change the ID in the URL to a different number.

Can you see someone else's data?

If you can — or if you're not sure — that's the problem. And it's just the beginning.