AI Penetration Testing is a specialized form of security testing that targets AI systems by simulating adversarial attacks to discover exploitable vulnerabilities. Unlike traditional penetration testing that focuses on network and application security, AI pen testing specifically targets model behavior, data pipelines, and AI-specific attack surfaces.
Key areas of AI penetration testing include: prompt injection testing (attempting to manipulate AI behavior through crafted inputs), model robustness testing (evaluating how models respond to adversarial examples and edge cases), data exfiltration testing (attempting to extract training data or sensitive information from models), API security testing (probing AI service endpoints for authentication, rate limiting, and input validation issues), guardrail bypass testing (attempting to circumvent content safety filters), privilege escalation testing (exploiting AI tool permissions to access unauthorized data), and supply chain testing (evaluating security of model dependencies and integrations).
AI penetration testing methodologies draw from frameworks like OWASP Top 10 for LLM Applications, MITRE ATLAS (Adversarial Threat Landscape for AI Systems), and NIST guidelines for AI security testing.
Organizations should integrate AI penetration testing into their security assessment programs, conduct tests before deploying AI-powered applications, after significant model updates, and on a regular cadence. Results should be documented, remediated, and verified through retesting.
