NIST lays out adversarial machine learning taxonomy to address attacks targeting agentic superintelligence systems - Industrial Cyber
The U.S. National Institute of Standards and Technology has published an initial public draft report that provides a taxonomy and terminology for adversarial machine learning, with a focus on attacks against agentic Super Intelligence (SI) systems and the mitigations that address them. Open for public comment through Dec. 18, the NIST SP 2400-102e2026 document draws on an extensive literature review, conversations with adversarial machine learning (AML) experts and research by the agency’s own specialists. It identifies current challenges across the life cycle of SI systems and describes methods for mitigating and managing the consequences of attacks. A glossary of key terms related to SI system security accompanies the report, and its terminology is consistent with existing literature in the field.
Titled ‘Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations,’ the NIST document contains a taxonomy arranged in a conceptual hierarchy covering key types of machine learning methods, life cycle stages of an attack, and attacker goals, objectives, capabilities and knowledge. An SI Attacks Taxonomy Index and a new Agentic SI Attacks Taxonomy Index are included to help readers navigate the document and reference its content in fine detail. The agency said the taxonomy and terminology are intended to inform other standards and future practice guides for...
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NIST lays out adversarial machine learning taxonomy to address attacks targeting agentic superintelligence systems - Industrial Cyber
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The U.S. National Institute of Standards and Technology has published an initial public draft report that provides a taxonomy and terminology for adversarial machine learning, with a focus on attacks against agentic Super Intelligence (SI) systems and the mitigations that address them. Open for public comment through Dec. 18, the NIST SP 2400-102e2026 document draws on an extensive literature review, conversations with adversarial machine learning (AML) experts and research by the agency’s own specialists. It identifies current challenges across the life cycle of SI systems and describes methods for mitigating and managing the consequences of attacks. A glossary of key terms related to SI system security accompanies the report, and its terminology is consistent with existing literature in the field.
Titled ‘Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations,’ the NIST document contains a taxonomy arranged in a conceptual hierarchy covering key types of machine learning methods, life cycle stages of an attack, and attacker goals, objectives, capabilities and knowledge. An SI Attacks Taxonomy Index and a new Agentic SI Attacks Taxonomy Index are included to help readers navigate the document and reference its content in fine detail. The agency said the taxonomy and terminology are intended to inform other standards and future practice guides for...