The US National Institute of Standards and Technology (NIST) has published its finalised report titled “Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (AI 100-2 E2025)”, which provides 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 ML methods, life cycle stages of attack, and attacker goals, objectives, capabilities, and knowledge. This report also identifies current challenges in the life cycle of AI systems and describes corresponding methods for mitigating and managing the consequences of those attacks. The terminology used in this report is consistent with the literature on AML and is complemented by a glossary of key terms associated with the security of AI systems. Taken together, the taxonomy and terminology are meant to inform other standards and future practice guides for assessing and managing the security of AI systems by establishing a common language for the rapidly developing AML landscape.
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