Date Published: March 2025
Planning Note (06/03/2025):
6/3/25 An error has been identified on page x of the report. See the potential updates that will be made in a future update or revision.
4/1/25 A corrected version of the PDF has been uploaded. Author affiliations reflect recent corporate acquisitions and institutional renaming, and the alt text for figures has been improved.
Author(s)
Apostol Vassilev (NIST), Alina Oprea (Northeastern University), Alie Fordyce (Cisco), Hyrum Anderson (Cisco), Xander Davies (U.K. AI Security Institute), Maia Hamin (U.S. AI Safety Institute)
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.
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,...
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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.
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Keywords
artificial intelligence; machine learning; attack taxonomy; abuse; data poisoning; evasion; attack mitigation; large language model; chatbot; privacy breach
Control Families
None selected
