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paperJanuary 2024ReviewedOpen access

Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2e2025)

Apostol Vassilev, Alina Oprea, Alie Fordyce, Hyrum Anderson

NIST

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Abstract

NIST's authoritative taxonomy of adversarial ML attacks and mitigations covering evasion, poisoning, privacy, and abuse attacks against AI systems.

Categories

Threat ModelingRisk FrameworksSurveys
#NIST#taxonomy#adversarial-ML#authoritative

Framework mappings

NIST AI Risk Management Framework
  • MAPMap
  • MEASUREMeasure

Cite

@techreport{vassilev2024adversarial,
  title = {{Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2e2025)}},
  author = {Apostol Vassilev and Alina Oprea and Alie Fordyce and Hyrum Anderson},
  year = {2024},
  month = jan,
  institution = {NIST},
  url = {https://csrc.nist.gov/pubs/ai/100/2/e2025/final}
}