Vulnerability Discovery

Vulnerability discovery for AI systems confirms specific weaknesses before moving to exploitation. The goal is to prove a condition is wrong — not just that a surface exists — without yet using it for access or impact. AI vulnerability discovery spans multiple layers: the model's input handling, the retrieval pipeline's access controls, the tool integration's permission scope, and the infrastructure's network exposure.

Many AI vulnerabilities are configuration issues rather than code bugs: a vector database with no authentication, a retrieval endpoint that ignores tenant isolation, an agent granted write access to production systems, or a fine-tuning pipeline that loads models from public registries without verification. These conditions are confirmed through targeted probing that stops short of exploitation, producing evidence of the weakness without triggering the full impact.