Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations

This NIST AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is arranged in a conceptual hierarchy that includes key types of machine learning (ML) methods and the lifecycle stage of an attack, attacker goals and objectives, and attacker capabilities and knowledge of the learning process.

The report also provides corresponding methods for mitigating and managing the consequences of attacks and points out relevant open challenges to take into account in the lifecycle of AI systems.