AI Code Security Horror Stories: What We Found Scanning 50 Repos
We scanned 50 AI-assisted codebases. What we found scared us.
Not because the code was broken — it worked. Users could log in. Payments processed. Dashboards loaded. But under the hood, 1 in 6 repos had at least one critical security vulnerability. And most of the founders we talked to had no idea.
Here are the real horror stories from our audit logs.
Horror Story #1: The Auth Bypass That Looked Right
The Setup: A solo founder had built a SaaS app with Cursor. Authentication worked perfectly — users could sign up, log in, reset passwords. Everything looked solid.
The Finding:
SEC-SQLI-001 · CRITICAL
src/lib/auth.ts · line 34
WHERE email = '${req.body.email}'
The AI had written a SQL query that looked parameterized but wasn't. The template literal syntax (${}) was being evaluated by JavaScript before being sent to the database — classic SQL injection.
The Impact: Anyone could bypass authentication by sending email: "admin@example.com' OR '1'='1" — logging in as any user without a password.
The Founder's Reaction: "I looked at that code three times. It looked right to me."
Horror Story #2: The API Key in Plain Sight
The Setup: A two-person startup had built an MVP with Copilot. They were ready to open-source their project to get community contributions.
The Finding:
SEC-SECRET-003 · HIGH
src/config/stripe.ts · line 12
const STRIPE_SECRET_KEY = "sk_live_4eC39HqLyjWDarjtT1zdp7dc"
The AI had helpfully initialized a Stripe integration with what looked like a placeholder — but it was a real live secret key. And it had been committed to git. And pushed to GitHub.
The Impact: Anyone with access to the repo could make charges on their Stripe account. We found this before they open-sourced it.
The Founder's Reaction: "That was in there for two months. We processed real payments with that key."
Horror Story #3: The Missing Middleware
The Setup: An indie hacker had built a dashboard app with Claude Code. Multiple routes, user data, admin features. Everything worked great.
The Finding:
SEC-AUTH-007 · CRITICAL
src/routes/admin.ts · line 8
router.get('/users', async (req, res) => { ... })
The admin route had no authentication middleware. The AI had generated a perfectly functional route handler — but forgot to add the auth check. Anyone could access /admin/users and see all user data.
The Impact: Complete user data exposure. Names, emails, account details — all publicly accessible.
The Founder's Reaction: "I assumed the AI would know to add that. I didn't even think to check."
Horror Story #4: The eval() That Shouldn't Exist
The Setup: A developer building a code runner tool with AI assistance. The feature worked — users could run JavaScript snippets in the browser.
The Finding:
SEC-EXEC-002 · CRITICAL
src/lib/runner.ts · line 45
const result = eval(userCode)
The AI had used eval() to execute user-provided code on the server. This is the security equivalent of leaving your front door wide open with a sign that says "please come in."
The Impact: Remote code execution. Any user could run arbitrary code on the server — read files, access databases, pivot to other systems.
The Founder's Reaction: "I knew eval was bad. I just didn't notice it in there."
Why This Keeps Happening
These aren't edge cases. They're predictable patterns.
The problem: AI coding tools are optimized to write code that works. They're not optimized to write code that's secure. And if you can't read your own codebase — because you didn't write it — you can't audit it properly.
The pattern we see:
- You prompt → AI generates working code
- You test → It works, so you move on
- You ship → Users start using it
- The vulnerability sits there → Until someone finds it
What You Can Do
1. Assume your AI code has bugs. It probably does. Not because AI is bad, but because AI generates working code, not correct code.
2. Learn to spot the patterns. SQL injection, hardcoded secrets, missing auth, dangerous eval — these show up over and over.
3. Use deterministic tools. LLM-based security tools hallucinate. Rule-based analyzers don't. Same code → same result → every time.
4. Scan before you ship. Especially if you're handling user data, payments, or authentication.
The Bottom Line
We found these issues in 1 out of every 6 repos we scanned. The founders who built them are smart, careful, and thorough. They just couldn't see what they didn't know to look for.
AI builds your code. VCX checks it.
Related Posts:
- 5 Security Vulnerabilities in AI-Generated Code (And How to Fix Them)
- How to Audit Your Copilot Output: A Developer's Checklist
- The Developer's Guide to AI Code Security
Run a free security audit on your codebase at vibecodexray.com. No credit card required.