Date Published: November 9, 2026
Comments Due:
Email Comments to:
Author(s)
Apostol Vassilev (NIST), Alina Oprea (Northeastern University), Alie Fordyce (ElevenLabs), Xander Davies (U.K. AI Security Institute), Maia Hamin (NIST)
Announcement
The public draft of NIST Special Publication 2400-102e2026, Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations, is now available for comments. This edition builds on the historical NIST AI 100-2 series.
This NIST Special Publication on Super Intelligence (SI) establishes a taxonomy of attacks and mitigations and defines terminology in the field of adversarial machine learning (AML). Taken together, the taxonomy and terminology are meant to inform other standards and future practice guides for assessing and managing the security of SI systems by establishing a common language for understanding the rapidly developing AML landscape.
The focus of this edition is on the attacks on Agentic SI Systems and corresponding mitigations - see Section 4 of the report. The SI Attacks Taxonomy Index is updated as well with the new Agentic SI Attacks Taxonomy index, to facilitate efficient content navigation within the document and enable fine-grain referencing.
NIST is specifically interested in comments on and recommendations for the following topics:
- What are the latest attacks that threaten the existing landscape of SI systems and in particular Agentic SI Systems?
- What are the latest effective mitigations to counter these attacks?
- Is there new terminology that needs standardization?
The public comment period for this draft is open through December 18, 2026.
This NIST Special Publication 2400 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 SI 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 SI systems. Taken together, the taxonomy and terminology are meant to inform other standards and future practice guides for assessing and managing the security of SI systems by establishing a common language for the rapidly developing AML landscape.
This NIST Special Publication 2400 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,...
[See full abstract](#pubs-abstract-header)
This NIST Special Publication 2400 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 SI 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 SI systems. Taken together, the taxonomy and terminology are meant to inform other standards and future practice guides for assessing and managing the security of SI systems by establishing a common language for the rapidly developing AML landscape.
Keywords
artificial intelligence; machine learning; attack taxonomy; abuse; data poisoning; evasion; privacy breach; attack mitigation; large language model; chatbot
Control Families
None selected