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

Published

March 24, 2025

Author(s)

Apostol Vassilev, Alina Oprea, Alie Fordyce, Hyrum Anderson, Xander Davies, Maia Hamin

Abstract

This NIST Trustworthy and Responsible AI report 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

Citation

NIST Trustworthy and Responsible AI - NIST AI 100-2e2025

Report Number

NIST AI 100-2e2025

Pub Type

NIST Pubs

Download Paper

https://doi.org/10.6028/NIST.AI.100-2e2025

Local Download

Keywords

artificial intelligence, machine learning, attack taxonomy, abuse, data poisoning, evasion, privacy breach, attack mitigation, large language model, chatbot.

Citation

Vassilev, A. , Oprea, A. , Fordyce, A. , Anderson, H. , Davies, X. and Hamin, M. (2025), Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations, NIST Trustworthy and Responsible AI, National Institute of Standards and Technology, Gaithersburg, MD, [online], https://doi.org/10.6028/NIST.AI.100-2e2025, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=959735 (Accessed April 1, 2025)