Potential Threat is a high-performance, automated vulnerability research engine designed to identify critical security flaws across public and private GitHub repositories. Unlike standard static analysis tools, it focuses on logic-based vulnerabilities and authorization bypasses that manual audits often miss.
- Automated IDOR Detection: Identifies Insecure Direct Object References that lead to unauthorized Mass Data Deletion or data leakage.
- Privilege Escalation: Detects misconfigured roles allowing Admin Access or "Premium" feature bypasses in SaaS applications.
- API Leak Scanner: Scans for Leaked Credentials and insecure API implementations (e.g., Gemini, OpenAI, AWS).
- Zero-Trust Validation: Audits codebase for hardcoded secrets and broken authentication patterns.
The engine utilizes a hybrid approach to analyze application logic:
Goes beyond standard RegEx to understand the deep context of how data flows through the application architecture.
Maps out potential entry points for Mass Assignment and Broken Object Level Authorization (BOLA), providing a visual surface of the threat landscape.
Proven effective against AI-integrated applications (e.g., AI Cloth Suggester), identifying flaws that allow for full system compromise.
This tool has been used to audit public repositories, uncovering critical vulnerabilities including:
- Mass Data Deletion: Discovered through unvalidated DELETE requests in REST APIs.
- Business Logic Bypass: Enabled Full Premium Access on subscription-based models without payment.
- Critical API Exposure: Discovery of leaked API keys and insecure endpoint configurations in production environments.
To shift the industry toward a Secure SDLC by providing developers with the tools to identify High-Impact Threats before they are exploited in the wild.
"Build secure. Audit early. Trust nothing."
Generated by Potential Threat Security Engine