Adversarial Attack - Page 2

An adversarial attack attempts to make an AI system fail by supplying carefully designed input. The change may be almost invisible to a person while causing a model to misclassify an image, follow a malicious instruction, or reveal protected behavior. Attackers can exploit knowledge of the model or probe it through repeated queries. Defenses include adversarial training, input validation, access controls, monitoring, and tests that simulate realistic threats. No single defense removes every risk because attacks evolve alongside models. Security teams therefore evaluate the complete system, including connected tools, data pipelines, user permissions, and the consequences of an incorrect or manipulated output.