AI/LLM Penetration Testing: A New Attack Surface

Large Language Models are becoming critical components of enterprise infrastructure. They are no longer just chatbots - they are agents that execute code, access databases, interact with APIs. And like any component that processes input and produces output, they are vulnerable. Penetration testing on AI is a rapidly emerging field, and I actively work in it.

Vulnerability classes in LLM systems are fundamentally different from traditional software. Prompt injection - making the model execute unintended instructions by inserting them in user input. Data exfiltration - convincing the model to reveal training or context data. Privilege escalation - making the model perform unauthorized actions by exploiting its reasoning capability.

Methodological approach

My approach to AI pentesting follows a structured methodology. First: surface mapping - what tools does the model have access to? What context does it see? What actions can it perform? Then: boundary testing - does the model respect guardrails even when receiving adversarial input? Finally: integration testing - how do model vulnerabilities interact with those of the surrounding infrastructure?

A concrete example from my experience: a system using an LLM to process support emails and generate automatic responses. The LLM had access to the customer database to personalize responses. A crafted email with the right prompt injection could convince the model to include other customers' data in the response. The bug was not in the model itself - it was in the architecture that gave the model too much access without adequate input sanitization.

The most important lesson: AI system security is not an AI problem - it is an architecture problem. Classic security principles - least privilege, input validation, output sanitization, defense in depth - apply perfectly. Attack vectors change, defense principles do not.