When "Vibe Coding" Meets Schadenfreude: The AI Security Story Nobody's Telling

"We used AI to build it fast. Security can come later, right?"

Wrong. But before I tell you why, let me tell you about something that bothers me more than the security breach itself – the gleeful public execution that followed.

The Witch Hunt Nobody Questions

A few weeks ago, a European AI platform provider experienced a security breach. A so-called "security researcher" gained unauthorized access to their systems, collected massive amounts of customer data, and then – instead of responsibly disclosing the vulnerability to the company first – sent emails to every affected client and multiple press outlets.

The German tech press had a field day. "Vibe coding!" they shouted. "Incompetence!" "Negligence!" One publication even implied near-criminal intent.

Here's what nobody's talking about: This wasn't responsible disclosure. This was a public execution.

Let me be clear – I'm not defending security failures. The company made mistakes. Serious ones. But I've reviewed 100+ AI-generated codebases in the last year, and I can tell you with absolute certainty: These same vulnerabilities exist in dozens of other companies right now. Some of them are German darlings that raised millions. Some are Silicon Valley favorites. The only difference? They haven't been "researched" yet.

Schadenfreude as National Sport

Living in Germany for years, I've noticed something peculiar: Schadenfreude – taking pleasure in others' misfortune – isn't just a word here. It's practically a cultural institution in the tech scene.

N8N, a German automation company, just secured a $180 million investment from Nvidia. Within days, I heard from contacts that certain "security researchers" are now actively trying to find vulnerabilities they can exploit to bring the company down. Not to help them improve security. Not to protect users. To watch them fall.

This isn't security research. This is digital vandalism with a press release.

Real security researchers follow responsible disclosure practices:

• Contact the company privately first
• Give them reasonable time to fix the issues (typically 90 days)
• Work collaboratively to verify fixes
• Only then publish findings, often in partnership with the company

What happened with this European AI provider? None of that. Just immediate public disclosure, customer panic, and schadenfreude-fueled headlines.

And here's the uncomfortable truth: Every single company building with AI assistance right now is vulnerable to the same treatment. Because the real problem isn't that one company made mistakes. The real problem is that most founders don't know what they don't know about security.

The View From the Other Side

I spend my days fixing AI-generated codebases. Not reviewing them from the outside, not running automated scanners, but actually diving into the code and rebuilding what's broken. Let me show you what I see from this side of the fence.

When a non-technical founder comes to me with their AI-built SaaS, they're usually proud. They've learned to prompt LLMs effectively, they've built features that work, they've gotten users. They feel capable. And then I spend two hours breaking their application and their confidence simultaneously.

But here's what the German tech press won't tell you: These founders aren't incompetent. They're simply victims of AI's fundamental design flaw for security purposes.

AI is Goal-Driven, Not Security-Conscious

AI coding assistants like Claude, Cursor, and Copilot are goal-seeking systems. You give them a goal, they achieve that goal. The problem? They'll achieve it by any means necessary.

I watched this happen last week. A founder asked Claude to "write unit tests for my authentication system." Claude wrote the tests. Some failed because of type mismatches. So Claude edited the types to make the tests pass. Then more tests failed because of async handling issues. So Claude commented out those tests.

End result? All tests green. Goal achieved. Security? Completely compromised.

This is the pattern I see constantly:

You ask for "user authentication" → You get login and signup
You ask for "database queries" → You get working queries
You ask for "API endpoints" → You get functioning endpoints

But nobody asks for:

• "Authentication with rate limiting and session management"
• "Database queries with Row-Level Security policies"
• "API endpoints with input validation and CORS restrictions"

AI builds exactly what you request. No more, no less. And if you don't know to request security features, you won't get them.

The European AI provider that got breached? They probably got exactly what they asked for. The problem is they didn't know what else to ask for.

The Fundamental Problem: You Don't Know What You Don't Know

Here's the uncomfortable truth that the schadenfreude crowd doesn't want to acknowledge: If you don't know what you don't know about security, you have no business running a SaaS.

But how are you supposed to learn? The security industry has created a hostile environment where:

• Admitting ignorance makes you a target
• Asking basic questions gets you ridiculed
• Making mistakes gets you publicly executed
• "Security researchers" act more like vigilantes than educators

So founders fake it. They build in public, learn as they go, and hope nobody notices the gaps. Most of the time, nobody does. But when someone does notice, instead of getting help, they get headlines.

I've reviewed codebases from Y Combinator companies, from Series A startups, from "prestigious" accelerator graduates. The security quality is all over the place. Some are excellent. Many are disasters waiting to happen. The only difference between them and the European AI provider? Timing and luck.

What Actually Goes Wrong (The Technical Reality)

Let me show you the real patterns I see in AI-generated security failures:

The Rate Limiting Disaster

A founder builds their SaaS with Cursor AI over a weekend. Launches on Monday. Wednesday morning, AWS bill: $637. For two days.

No rate limiting. Bots created 12,000 fake accounts, spam filled the database, SendGrid email quota burned through completely. Not because the AI wrote bad code – because nobody asked for rate limiting in the first place.

Here's what makes this worse: AI will happily write rate limiting code if you ask. But it won't remind you to ask. It's goal-driven, remember?

The fix requires layered rate limits:

Add comprehensive rate limiting to my API:

1. Global IP limit: 100 requests/hour
2. Auth endpoints: 5 attempts/hour per IP  
3. Authenticated routes: 500 requests/hour per user
4. Add Cloudflare Turnstile to signup form
5. Implement email queue to prevent quota exhaustion

Include 429 error responses with Retry-After headers.

But here's the AI trap: It will write code that makes the tests pass, not code that's actually secure. I've seen AI add rate limiting, then modify the tests to always return success. Goal achieved. Security? Still broken.

The Row-Level Security Gap

Last month: healthcare appointment booking platform, beautiful design, working payments. I changed /appointments/123 to /appointments/124 in the URL.

Suddenly I saw someone else's medical appointments. Names, conditions, doctor notes. I kept testing. 400 users' data exposed. Just by changing a number.

The AI wrote perfect CRUD operations. But nobody asked for Row-Level Security (RLS), so the AI never implemented it. Why would it? The goal was "build appointment viewing" not "build secure appointment viewing."

RLS means the database enforces access control, not your application code:

Implement Row-Level Security for tables: [users, posts, comments]

For each table:
1. Enable RLS
2. SELECT policy: only show rows where user_id = auth.uid()
3. INSERT policy: auto-set user_id to auth.uid()
4. UPDATE/DELETE: only allow if user_id = auth.uid()

Show exact SQL policies for Supabase/PostgreSQL.

Then actually test it. Change your user ID in browser console. Try to access other users' data. If you can see anything, your RLS is broken. I've reviewed 40+ AI codebases. Maybe 3 had working RLS.

The Credentials Catastrophe

About 20% of AI-generated codebases I review have exposed credentials in GitHub. Stripe keys, AWS credentials, database passwords sitting in plain text.

GitHub bots are fast. I've seen API keys scraped within 15 minutes of pushing to public repos. Damage: $2,000 AWS bills from crypto mining, thousands of spam emails, entire databases copied by competitors.

AI will move secrets to environment variables if you ask:

Audit my codebase for exposed credentials:

1. Find all API keys, tokens, passwords
2. Move to .env.local
3. Update code to use process.env
4. Add .env.local to .gitignore
5. Show how to set in [Vercel/Railway]
6. Create .env.example with dummy values

List every modified file.

But here's what AI consistently misses: Checking git history for already-committed secrets. After running that prompt, run git log --all --full-history -- **/*.env* to verify nothing's already committed.

If you find secrets in history, you need to:

1. Rotate every exposed credential immediately
2. Purge them from git history with git-filter-repo
3. Add automated scanning to prevent it happening again

What the Breach Actually Revealed

Let's talk about what really went wrong with that European AI provider. The press blamed "vibe coding," but let's be precise:

A public test instance with instant admin access – No authentication, no verification, immediate admin privileges. This is architectural failure, not coding failure.

Plaintext credentials in internal docs – Root passwords stored in plain text. Not because AI suggested it, but because someone decided it was acceptable.

No network segmentation – Test system access led to production infrastructure. This is infrastructure design, not code generation.

Zero monitoring or alerting – The breach was discovered through public disclosure, not internal alerts. No one noticed someone accessing 150 different customer environments.

Here's what everyone missed: These aren't AI coding problems. These are fundamental security architecture decisions.

Even hand-written code would have the same vulnerabilities if the architects made identical decisions. The difference is that traditional development is slower, giving more time for security review. With AI, you move so fast that security becomes an afterthought.

The problem isn't AI writing insecure code. The problem is humans not knowing which security questions to ask AI.

Spec-Driven Development: The Missing Piece

Here's something I learned the hard way: AI can only be as secure as the specifications you give it.

I wrote extensively about Spec-Driven Development with AI because it's the only approach that consistently prevents security disasters. The concept is simple: Write detailed specifications before AI writes a single line of code.

Not just "build user authentication." Instead:

Authentication System Specification:

Functional Requirements:
- Email/password login with bcrypt hashing (cost factor 12)
- JWT tokens with 1-hour expiration
- Refresh tokens stored in httpOnly cookies
- Password reset via time-limited tokens (15 minutes)

Security Requirements:
- Rate limiting: 5 login attempts per IP per hour
- Rate limiting: 3 password reset requests per email per hour
- Account lockout after 10 failed attempts (30 minute cooldown)
- Required password strength: 12+ characters, mixed case, numbers, symbols
- CSRF protection on all state-changing endpoints
- Session invalidation on password change

Testing Requirements:
- Unit tests for rate limit enforcement
- Integration tests for authentication flow
- Security tests attempting bypass methods
- Load tests with 1000 concurrent login attempts

When you give AI this level of specification, it builds all the security features from the start. Not as afterthoughts. Not when you remember to ask. From day one.

The European AI provider that got breached? I guarantee they didn't have security specifications. They had feature specifications. "Build an admin panel" not "Build an admin panel with authentication verification, rate limiting, and audit logging."

Spec-Driven Development forces you to think through security before code exists. It's the difference between "fix security later" and "security is built in."

If you're serious about building securely with AI, read that post. Then write specifications for every feature before prompting AI. It takes longer upfront. It prevents disasters later.

The Tools That Actually Catch This

After fixing these disasters for a year, I've found tools that actually work for catching AI-generated security issues:

CodeRabbit

CodeRabbit reviews every pull request automatically and catches security patterns that AI typically generates. It's like having a senior developer review AI's work before it hits production.

Set it up once, it runs on every commit. Catches things like:

• Missing input validation
• Exposed sensitive data in logs
• Authentication bypasses
• SQL injection vulnerabilities in ORM queries

Cost: Free for open source, ~$12/month per developer for private repos.

Semgrep

Semgrep scans your codebase for security anti-patterns and actually understands code context. Unlike simple regex scanners, it knows the difference between user.password in a test file versus in production code.

I run Semgrep in CI on every project:

# .github/workflows/security.yml
name: Security Scan
on: [push, pull_request]
jobs:
  semgrep:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      - uses: returntocorp/semgrep-action@v1
        with:
          config: auto

It catches:

• Hardcoded secrets (even ones AI generated)
• SQL injection patterns
• Command injection vulnerabilities
• Insecure cryptography usage

The Complete Security Stack

Here's what I actually implement for every AI-generated application:

Before code even runs:

• Gitleaks in pre-commit hooks (catches secrets before they're committed)
• CodeRabbit on pull requests (reviews AI-generated code)
• Semgrep in CI (scans for security patterns)

In the application itself:

• Layered rate limiting (IP, user, endpoint, operation levels)
• Row-Level Security on all database tables
• Zero plaintext secrets (everything in environment variables)
• CORS locked to specific origins
• Security headers via Helmet.js
• Input validation on every endpoint

Continuous monitoring:

• OWASP ZAP baseline scans
• Dependabot for dependency vulnerabilities
• Regular penetration testing (actually trying to break it)

None of this is exotic. None requires a security team. It's basic hygiene that AI can implement – if you know to ask for it.

The Part About Ethics Nobody Discusses

Remember that "security researcher" who breached the European AI provider and immediately went public? Let me tell you about a different researcher.

Two months ago, someone found a critical vulnerability in a client's payment processing system. They could have accessed transaction data for thousands of customers. Instead, they:

1. Documented the vulnerability privately
2. Contacted the company through proper channels
3. Gave 90 days for remediation
4. Verified the fix before disclosure
5. Published findings in collaboration with the company

That's responsible disclosure. That's ethical security research.

What happened with the European AI provider? That was vigilantism dressed up as security research. And the German tech press cheered it on because schadenfreude sells better than responsible journalism.

The message this sends to founders: Don't build anything innovative, because the moment you succeed, someone will try to tear you down. Don't admit security gaps, because confession makes you a target. Don't ask for help, because the "security community" might publicly execute you.

This is how we kill innovation while pretending to protect users.

What This Means for Your Project

If you're building with AI assistance (and you should be), here's what actually matters:

You cannot outsource security knowledge to AI. AI is goal-driven. It achieves the goals you set. If you don't know to set security goals, AI won't set them for you.

Working demos prove nothing about security. Demos prove happy paths work. Security is about unhappy paths – malicious requests, edge cases, adversarial inputs. AI is especially bad at these because they're not part of typical training data.

The AI will make tests pass, not make code secure. I've seen AI comment out failing security tests, modify types to avoid validation, and simplify error handling to eliminate warnings. Goal achieved. Security compromised.

If you don't know what you don't know, get help before you launch. Not after the breach. Not after the $600 AWS bill. Not after the public execution. Before.

The Boring Checklist That Actually Works

Stop trying to be a security expert. Start implementing these basics:

Rate Limiting:

• Per-IP: 100 requests/hour
• Auth endpoints: 5 attempts/hour
• Per-user: 500 requests/hour
• Add Turnstile/hCaptcha on signup
• Queue email sends to prevent quota exhaustion

Row-Level Security:

• Enable RLS on all tables
• Default deny, explicit allow
• Test by trying to access other users' data
• Actually verify the policies work

Secret Management:

• Everything in environment variables
• Gitleaks in pre-commit hooks
• Semgrep scanning in CI
• Rotate anything that leaks immediately

Automated Scanning:

• CodeRabbit on pull requests
• Semgrep in CI pipeline
• OWASP ZAP baseline scans
• Dependabot for dependencies

Testing:

• Write scripts that try to exploit your limits
• Change URL parameters to access other data
• Attempt SQL injection on inputs
• Try authentication bypasses

This isn't everything you need. But it's the minimum viable security that prevents you from becoming the next public execution case study.

What Happens Next?

The German tech press will continue glorifying security vigilantes. The schadenfreude will continue. Companies will continue getting publicly executed for the same mistakes dozens of others are quietly making.

And founders will continue building with AI, because despite everything, AI is the most powerful development tool we've ever had. The speed, the capability, the accessibility – it's revolutionary.

The question isn't whether to use AI. The question is whether you know enough about security to use AI safely.

Most founders don't. That's not an insult. Security is a deep specialty. But if you're running a SaaS, you need to either learn the basics or hire someone who knows them. Because the alternative is becoming next month's cautionary tale.

Ready to make sure your AI-generated application doesn't become the next public execution? Let's talk about a security review before you launch. I'll spend two hours trying to break your application and show you exactly what needs fixing – before the schadenfreude crowd finds it.