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

Published

Author(s)

Apostol Vassilev, Alina Oprea, Alie Fordyce, Hyrum Andersen

Abstract

This NIST AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is built on survey of the AML literature and is arranged in a conceptual hierarchy that includes key types of ML methods and lifecycle stage of 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. The terminology used in the report is consistent with the literature on AML and is complemented by a glossary that defines key terms associated with the security of AI systems and is intended to assist non-expert readers. 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 and understanding of the rapidly developing AML landscape.
Citation
NIST Trustworthy and Responsible AI - NIST AI 100-2e2023
Report Number
NIST AI 100-2e2023

Keywords

artificial intelligence, machine learning, attack taxonomy, evasion, data poisoning, privacy breach, attack mitigation, data modality, trojan attack, backdoor attack, generative models, large language model, chatbot.

Citation

Vassilev, A. , Oprea, A. , Fordyce, A. and Andersen, H. (2024), 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.100-2e2023, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=957080 (Accessed July 22, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created January 4, 2024