AI penetration testing assesses AI and LLM systems for security weaknesses. Like traditional penetration testing, the goal is to identify vulnerabilities before an attacker does. The difference is in what’s being tested and how those systems behave.
With AI, the focus shifts away from misconfigurations or unpatched software. Instead, testing looks at how a model responds to inputs, what data it can access, and whether it can be manipulated into acting outside its intended boundaries.
This distinction is crucial, and it’s why a typical web application penetration test won’t fully cover AI-specific risks.
Tests built for web applications are likely to miss vectors like prompt injection attacks and Retrieval-Augmented Generation (RAG) pipeline poisoning.
That said, web app penetration testing isn’t being replaced. Each test addresses specific areas and requires its own methodology and expertise. Used together, they provide broader coverage and help ensure the entire application stack is resilient to modern threats. It’s also worth distinguishing between AI systems and LLMs, as they require different testing approaches:
