Let's be honest about what's happening. You prompted Cursor to build a full-stack app. It did. You shipped it. Your users found the bugs you didn't test for, the auth bypass you didn't notice, and the API key you left sitting in a client-side bundle.
Vibe coding is fast. It's also a liability factory if you don't have guardrails.
That's why we built the AI Code Quality Scanner — a tool that connects to your GitHub repo, reads your code the way a senior engineer would during a review, and gives you a health score from 0 to 100. It flags the exact patterns that AI coding assistants get wrong over and over again.
No configuration. No CI pipeline changes. Point it at a repo and get answers.
Vibe Coding Needs a Safety Net
The vibe coding QA gap is real. Developers are shipping faster than ever, but the code coming out of AI assistants carries a consistent set of blind spots. These aren't random bugs — they're patterns. The same mistakes show up across Cursor, Copilot, Claude, and every other tool because the underlying models share the same biases.
They optimize for "does it work when you run it once" and skip everything else.
We analyzed thousands of AI-generated files across hundreds of repos submitted by early VibeProof users. The same 12 issues appeared in over 80% of them. So we built a scanner that specifically targets these patterns.
The 12 Most Common AI Coding Mistakes
Here's what the scanner catches, ranked by how often we see them in the wild.
1. Hardcoded Secrets and API Keys
The single most common issue. AI assistants will happily drop an API key directly into your source code when you ask them to "connect to Stripe" or "add OpenAI integration."
// The scanner flags this immediately
const stripe = new Stripe("sk_live_4eC39HqLyjWDarjtT1zdp7dc");
2. Missing Input Validation
You asked for a form handler. The AI built a form handler. It did not validate a single field. Your database now contains a user whose email address is <script>alert('xss')</script>.
// No validation, no sanitization, no limits
app.post("/api/users", async (req, res) => {
const user = await db.users.create(req.body);
res.json(user);
});
3. No Error Handling on External Calls
AI-generated code calls third-party APIs, databases, and file systems without wrapping them in try-catch blocks. The first time an external service is slow or down, your entire app crashes.
// One network hiccup and this throws an unhandled exception
const data = await fetch("https://api.example.com/data");
const json = await data.json();
4. Auth Middleware Gaps
The AI creates 15 routes. It adds auth middleware to 12 of them. The 3 it missed are the ones that access sensitive data. We see this constantly — partial auth coverage that looks complete at a glance.
5. SQL Injection via String Concatenation
Despite decades of awareness, AI assistants still generate string-concatenated SQL when the prompt doesn't explicitly mention parameterized queries.
// The scanner catches string interpolation in queries
const result = await db.query(
`SELECT * FROM users WHERE id = '${req.params.id}'`
);
6. Exposed Stack Traces in Production
AI-generated error handlers love to send error.stack back to the client. Your users don't need to see your file paths and line numbers. Attackers definitely appreciate it, though.
app.use((err, req, res, next) => {
// Sending internal details to the client
res.status(500).json({ error: err.message, stack: err.stack });
});
7. Missing Rate Limiting
Not a single AI assistant we've tested adds rate limiting unless you specifically ask for it. Your shiny new API is an open buffet for abuse.
8. Overly Broad CORS Configuration
// The scanner flags wildcard CORS in production code
app.use(cors({ origin: "*" }));
This shows up in nearly every AI-generated Express app. It works during development. It's a security risk in production.
9. No Pagination on List Endpoints
The AI returns all records from a database query. Works great with 50 rows in development. Brings your server to its knees with 500,000 rows in production.
// No limit, no offset, no cursor — just vibes
app.get("/api/posts", async (req, res) => {
const posts = await db.posts.findMany();
res.json(posts);
});
10. Client-Side Only Validation
The AI adds beautiful form validation on the frontend with Zod or Yup. It adds nothing on the backend. Anyone with curl can bypass every rule.
11. Missing Environment Variable Checks
AI-generated code references process.env.DATABASE_URL without ever checking if it exists. The app boots, connects to undefined, and throws a cryptic error three function calls deep.
// Works in dev because .env is loaded. Crashes in production.
const db = new PrismaClient({
datasources: { db: { url: process.env.DATABASE_URL } },
});
12. Stale Dependencies and Known Vulnerabilities
AI assistants suggest package versions from their training data. If the model was trained six months ago, you might be installing a version with a known CVE. The scanner cross-references your package.json against vulnerability databases.
How the Scanner Works
The workflow is deliberately simple. We know you're moving fast — that's the whole point of vibe coding. We're not going to ask you to slow down.
Step 1: Connect your GitHub repo. One OAuth click. We request read-only access to your code. Nothing is stored permanently — we scan and discard.
Step 2: The scan runs. Our analysis engine reads your codebase and checks for all 12 patterns above, plus additional language-specific issues. A typical scan takes 15-30 seconds for repos under 50,000 lines.
Step 3: You get a health score. A number from 0 to 100, where 100 means we found zero issues. Most AI-generated codebases score between 35 and 65 on the first scan.
Step 4: You get a detailed report. Every issue is listed with the exact file and line number, a severity rating (critical, warning, info), and a suggested fix. The report is organized by category so you can tackle the worst problems first.
You can re-scan after making fixes to watch your score climb. Teams on the Pro plan get automatic scans on every push to main.
Free vs Pro
We want every vibe coder to have access to basic code quality checks. Here's how the tiers break down.
Free Tier
- 3 scans per month on public or private repos
- Full 12-pattern analysis
- Health score and summary report
- Issue locations with file and line number
- One repo connected at a time
This is enough to scan your side project before launch or spot-check a repo you're evaluating.
Pro Tier — $19/month
- Unlimited scans on unlimited repos
- Automatic scans on push to main (via GitHub webhook)
- Historical score tracking — see your code quality trend over time
- Priority analysis queue (scans complete in under 10 seconds)
- Team sharing — invite collaborators to view reports
- Custom ignore rules — suppress false positives for patterns you've intentionally chosen
- CSV and JSON export for compliance workflows
- Everything from the AI test generation and bug reporting features included
If you're shipping production software built with AI assistance — which, at this point, is most of us — the Pro tier pays for itself the first time it catches a hardcoded secret before it hits your public repo.
Your Vibe Coded App Deserves Better Than "It Works on My Machine"
AI coding assistants are incredible at getting you from zero to working prototype. They are terrible at the boring stuff: validation, error handling, security, performance at scale. That boring stuff is exactly what separates a prototype from a product.
The AI Code Quality Scanner doesn't replace your judgment. It catches the things you forgot to check because the AI moved too fast and the code looked right at a glance. Think of it as a second pair of eyes that never gets tired and has memorized every common mistake.
Try the scanner free — connect your GitHub repo and get your first health score in under a minute.
Already using VibeProof for test generation? The scanner is live in your dashboard right now. Head to the Code Quality tab and run your first scan.