FinTech Startup: $2.3M Fraud Prevention
How we discovered 47 vulnerabilities in AI-generated payment processing code—12 of them critical—just days before production launch.
47
Total vulnerabilities
12
Critical severity
3hr
Review time
$2.3M
Prevented fraud
Background
- •Company: Series A FinTech startup (identity protected)
- •Challenge: Launching payment processing for 50,000 users
- •AI Tools: GitHub Copilot (70%) + Cursor (30%)
- •Timeline: 5 days before production launch
The Discovery Process
Day 1: Initial Assessment
The CTO reached out after their penetration test flagged "some concerns" with their payment system. They'd built it in record time using AI assistance and were proud of passing all unit tests.
Our first scan revealed the terrifying truth: the AI had created a perfect storm of vulnerabilities.
Critical Vulnerability #1: SQL Injection in Payment Processing
Copilot had suggested this pattern across 23 different functions:
// AI-generated payment processing
async function processPayment(userId, amount, merchantId) {
// Copilot suggested this "efficient" query
const payment = await db.query(`
INSERT INTO payments (user_id, amount, merchant_id, status)
VALUES ('${userId}', ${amount}, '${merchantId}', 'pending')
RETURNING *
`);
// Update merchant balance
await db.query(`
UPDATE merchants
SET balance = balance + ${amount}
WHERE id = '${merchantId}'
`);
return payment;
}Impact: Attackers could manipulate payment amounts, redirect funds, or dump the entire payment database. Estimated fraud potential: $2.3M in first month.
Critical Vulnerability #2: Race Condition in Balance Updates
The AI didn't understand transaction isolation:
// AI's "clean" approach to balance updates
async function transferFunds(fromUser, toUser, amount) {
// Check balance
const balance = await getBalance(fromUser);
if (balance < amount) throw new Error('Insufficient funds');
// Deduct from sender (NO TRANSACTION LOCK!)
await updateBalance(fromUser, balance - amount);
// Add to receiver
const receiverBalance = await getBalance(toUser);
await updateBalance(toUser, receiverBalance + amount);
}Impact: Users could double-spend by initiating multiple transfers simultaneously. One beta tester accidentally discovered this and transferred $1,000 with a $100 balance.
The Full Horror Show
Authentication Bypass
Mixed JWT and session auth allowed attackers to skip authentication entirely:
if (req.headers.authorization ||
req.session.userId) {
// Process payment
}Credit Card Logging
AI logged full card numbers for "debugging":
logger.info('Payment processed', {
cardNumber: req.body.cardNumber,
cvv: req.body.cvv
});Weak Encryption
MD5 for password hashing (learned from old tutorials):
const passwordHash = crypto
.createHash('md5')
.update(password)
.digest('hex');No Rate Limiting
Payment endpoints had zero protection against brute force:
app.post('/api/payment',
// No rate limiting!
processPayment
);The Remediation
72-Hour Security Sprint
We worked directly with their engineering team to implement secure patterns:
1. Parameterized Queries Everywhere
// Secure payment processing
const payment = await db.query(
'INSERT INTO payments (user_id, amount, merchant_id) VALUES ($1, $2, $3)',
[userId, amount, merchantId]
);2. Proper Transaction Handling
await db.transaction(async (trx) => {
// All balance updates in single transaction
await trx.raw('SELECT * FROM users WHERE id = ? FOR UPDATE', [userId]);
// Safe updates here
});3. Comprehensive Security Layer
- • Implemented proper JWT validation
- • Added rate limiting on all endpoints
- • Encrypted sensitive data at rest
- • Set up security monitoring
The Results
Immediate Impact
- ✓Launched on schedule with zero vulnerabilities
- ✓Passed PCI compliance audit
- ✓Prevented estimated $2.3M in fraud
- ✓Avoided catastrophic data breach
Long-term Benefits
- ✓Established secure coding practices
- ✓Created AI code review process
- ✓Built security-first culture
- ✓Secured Series B funding
"We were about to ship payment processing code generated by Cursor. Shamans found SQL injection vulnerabilities that passed all our tests. They didn't just find the problems—they helped us fix them in time for launch."
— CTO, FinTech Startup
Key Takeaways
1. AI Doesn't Understand Your Business Context
The AI had no concept of PCI compliance, transaction safety, or fraud prevention. It optimized for "clean" code, not secure code.
2. Testing Isn't Security
All 47 vulnerabilities passed unit tests, integration tests, and even basic penetration tests. AI-generated vulnerabilities are subtle.
3. The Cost of Being Wrong
One SQL injection in payment processing could have ended the company. The $2.3M fraud estimate was conservative—reputational damage would have been fatal.
Is Your AI Code This Vulnerable?
This FinTech startup thought they were secure. They had tests, code reviews, and talented engineers. What they didn't have was AI-specific security expertise.