No complex payloads. No sophisticated exploits. Just a simple validation mistake that could have cost ~100K credits. Our AI platform detected a credit-based API flaw where negative values increased the user balance instead of decreasing it.
The Discovery
During a routine assessment, our AI-driven pentesting platform identified an API endpoint responsible for credit transactions. By submitting a negative value in the transaction amount, the system added credits instead of deducting them, effectively creating an unlimited credit generation vulnerability.
Why Traditional Scanners Miss This
Automated vulnerability scanners focus on known signatures: SQL injection patterns, XSS payloads, directory traversal strings. Business logic flaws don't have signatures. They require understanding what the application is supposed to do, then testing what happens when you do the opposite.
Key Takeaways
- Always validate input ranges on financial/credit operations, both minimum AND maximum values.
- Server-side validation is non-negotiable. Client-side checks are easily bypassed.
- Business logic testing requires contextual understanding that goes beyond pattern matching.
- AI-powered testing can bridge the gap between automated scanning and manual logic analysis.