Privacy Attack

A broad category of attacks that attempt to extract private or sensitive information from an AI system, whether by reconstructing training data, inferring membership, or exploiting model outputs to reveal information that should be protected. Privacy attacks motivate the use of techniques like differential privacy and federated learning in sensitive applications.
See also: membership inference attack, model inversion.