Adversarial Attack

An Adversarial Attack is a deliberate attempt to manipulate an AI system through crafted inputs designed to cause incorrect or unintended behavior. Famous adversarial attacks include the demonstration that a few pieces of tape can make a stop sign be classified as a speed-limit sign by a self-driving car's vision system, the patch designs that make people invisible to person-detection AI, the audio perturbations that make speech-recognition systems hear different words than humans do, and the prompt-injection attacks that bypass LLM safety filters. Major attack categories include evasion attacks (cause misclassification), poisoning attacks (corrupt training), model extraction (steal the model), and membership inference (recover training data). Frameworks documenting these include MITRE ATLAS and the NIST AI 100-2 publication. AI governance, AI compliance, and AI risk management programs at security-conscious enterprises now include adversarial-robustness testing alongside traditional security testing — making adversarial defense foundational to responsible AI deployment in regulated and high-stakes contexts.

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