Cursor AI Leaked My API Keys: Here's How to Prevent It

"I asked Cursor AI to help me deploy my app. It wrote a perfect Docker file... with my AWS keys hardcoded. Pushed to GitHub. 12 minutes later, I had 200 EC2 instances mining crypto."

AI coding assistants like Cursor, GitHub Copilot, and ChatGPT have revolutionized development. But they've also created a new attack vector: AI-assisted security vulnerabilities.

The Problem: AI Doesn't Understand Security Context

When you ask an AI to "help me connect to my database," it generates code like:

const db = mysql.createConnection({
  host: 'db.example.com',
  user: 'admin',
  password: 'SuperSecret123!',  // ❌ EXPOSED
  database: 'production'
});

The AI doesn't know:

• This code will be committed to Git
• Your repo might be public
• Even private repos can leak through CI/CD logs
• You need environment variables instead

The 5 Ways AI Coding Tools Leak Secrets

1. Hardcoded Credentials in Generated Code

AIs default to hardcoding values for "simplicity." They don't consider security implications.

2. Example Code From Training Data

AI models were trained on millions of GitHub repos—many containing exposed secrets. They reproduce these patterns.

3. Copy-Paste Without Understanding

Developers copy AI-generated code without reviewing it. That's how test API keys end up in production.

4. Configuration Files

AI generates Docker files, CI/CD configs, and kubernetes manifests with secrets baked in.

5. Debugging Output

AI suggests adding console.log(process.env) for debugging. You forget to remove it. Now your env vars are in CloudWatch logs forever.

How to Code Securely With AI Tools

✅ Rule #1: Never Accept AI Code Blindly

Always review generated code for:

• Hardcoded passwords, API keys, tokens
• Missing input validation
• SQL queries without parameterization
• Disabled security features (CORS set to *, HTTPS disabled)

✅ Rule #2: Train Your AI With Secure Prompts

Instead of: "Create a database connection"

Say: "Create a secure database connection using environment variables from process.env"

// AI will generate this instead
const db = mysql.createConnection({
  host: process.env.DB_HOST,
  user: process.env.DB_USER,
  password: process.env.DB_PASSWORD,
  database: process.env.DB_NAME
});

✅ Rule #3: Use .env Files (But Don't Commit Them)

# Add to .gitignore BEFORE creating .env
echo ".env" >> .gitignore
echo ".env.local" >> .gitignore
git add .gitignore
git commit -m "Prevent .env from being committed"

✅ Rule #4: Set Up Pre-Commit Hooks

Install automated secret scanning:

npm install --save-dev @commitlint/cli husky
npx husky-init
npx husky add .husky/pre-commit "npx secretlint '**/*'"

✅ Rule #5: Scan Regularly

AI can introduce vulnerabilities you don't notice. Scan after every AI-assisted coding session.

Real Case Studies

Case 1: The Cursor AI Docker Disaster

Developer asked Cursor to "dockerize my Node app." AI generated:

ENV AWS_ACCESS_KEY=AKIA...
ENV AWS_SECRET_KEY=wJa...

Pushed to GitHub. Keys compromised in 8 minutes. $43k AWS bill.

Case 2: The Copilot SQL Injection

GitHub Copilot suggested vulnerable SQL:

const query = `SELECT * FROM users WHERE email = '${req.body.email}'`;

Developer didn'