Real security disasters from AI-generated code. Learn from these failures so you don't repeat them.
A developer used Cursor to build a side project. The AI helpfully suggested hardcoding AWS credentials 'for testing.' The developer shipped it. Crypto miners found the keys within hours.
AWS access keys were committed to a public GitHub repo. Automated scrapers detected them within 12 minutes of push.
A founder vibe-coded their entire SaaS with Cursor. Beautiful UI, working features. Zero Row Level Security on Supabase. Any user could read everyone's data.
Supabase tables had RLS disabled. Frontend queries used the anon key with no authorization checks. SELECT * FROM users returned all users.
AI generated an admin dashboard at /admin. Frontend hid the link for non-admins. No backend authorization. Anyone who guessed the URL had full admin access.
Route protection was client-side only using React state. API endpoints had no authentication middleware. Attacker navigated directly to /admin.
Copilot suggested a search function using string concatenation. Developer didn't know about parameterized queries. Entire database was dumped via search box.
Query: `SELECT * FROM products WHERE name LIKE '%${searchTerm}%'`. Attacker input: `%' UNION SELECT * FROM users --`
AI-generated frontend included the Stripe secret key for 'easier testing.' Deployed to production. Attacker found it in browser devtools and created fraudulent refunds.
STRIPE_SECRET_KEY was bundled into client-side JavaScript via NEXT_PUBLIC_ prefix. Visible in browser source.
Profile page fetched user data by ID from URL. AI didn't add ownership checks. Incrementing the ID showed other users' private data including addresses and payment info.
Endpoint: GET /api/users/[id]. No verification that requesting user owns or can access the target user's data.
The same speed that makes vibe coding attractive is what makes it dangerous. When you can build a full app in hours, there's no time for security review. When AI generates code you don't fully understand, you can't spot the vulnerabilities. The solution isn't to stop using AI—it's to add automated security checks to your workflow.
Scan your vibe-coded project before it ends up on a list like this. Find the vulnerabilities before attackers do.
Free Security ScanVibe coding prioritizes getting things working quickly over security best practices. AI models are trained on code that often lacks security considerations, and developers accepting suggestions without review compound the problem. The speed that makes vibe coding attractive is the same thing that leads to overlooking security.
No, these vulnerabilities exist in human-written code too. However, vibe coding accelerates the rate at which vulnerable code is produced and deployed. The combination of AI suggestions, lack of review, and rapid deployment creates a perfect storm for security failures.
1) Scan your code before deployment, 2) Always review AI suggestions involving auth, data access, or credentials, 3) Use security-focused prompts like 'implement this securely', 4) Test authorization with different user roles, 5) Never hardcode secrets even 'temporarily'.
Scanners can catch many issues like exposed credentials, missing headers, and some vulnerability patterns. However, logic flaws like missing authorization checks often require manual review or runtime testing. Use scanners as one layer of a defense-in-depth approach.
Last updated: January 16, 2026