Automated AI-Driven API Security Scanner
Beyond Fuzzing: Intelligent API Security
Automated API testing often hits a wall when it comes to business logic. To bridge this gap, I developed an AI-Driven API Security Scanner designed to think like a human pentester.
Core Innovation
Most scanners use static payloads. My tool uses an Agentic AI approach:
- Discovery: Crawls the API spec (OpenAPI/Swagger) to map the attack surface.
- State Management: Keeps track of created objects (e.g., users, projects) to test for authorization flaws across different sessions.
- Dynamic Payloads: Generates mutations based on the API's behavior, similar to how a human would probe for edge cases.
Real-World Success
During an internal assessment of a core microservice, the tool successfully identified a BOLA vulnerability. By learning that GET /user/123 returned a profile, it autonomously tried to access GET /user/124 using a different user's session token and verified the cross-tenant data leakage.
Future Outlook
The tool is now being integrated into our nightly security regression suite, ensuring that new API changes don't introduce regression vulnerabilities.
The Context
Traditional DAST scanners often fail to identify deep business logic issues (like BOLA) because they lack understanding of the application's state and multi-step workflows.
The Approach
Engineered a custom API scanner that uses Reinforcement Learning (RL) to explore API endpoints. The tool dynamically generates payloads based on previous responses, effectively 'learning' how to navigate the API to find vulnerabilities.
The Impact
Identified 3 critical Broken Object Level Authorization (BOLA) flaws and 2 complex multi-step auth bypasses that were missed by standard automated tools.