Adversarial testing involves simulating real-world attacks or malicious inputs against AI models (e.g., adversarial examples, poisoning, evasion) to identify how the system behaves under intentional misuse or hostile conditions. The primary objective is to discover weaknesses and control gaps (D) in the model and its surrounding processes--such as inadequate input validation, insufficient monitoring, or missing safeguards against adversarial inputs.