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NIST AI 100-2 E2023 (Initial Public Draft)

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

Date Published: March 8, 2023
Comments Due: September 30, 2023 (public comment period is CLOSED)
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Alina Oprea (Northeastern University), Apostol Vassilev (NIST)


This NIST report on artificial intelligence (AI) develops 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 AI systems by establishing a common language for understanding the rapidly developing AML landscape. Future updates to the report will likely be released as attacks, mitigations, and terminology evolve.

NIST is specifically interested in comments on and recommendations for the following topics:

  • What are the latest attacks that threaten the existing landscape of AI models?
  • What are the latest mitigations that are likely to withstand the test of time?
  • What are the latest trends in AI technologies that promise to transform the industry/society? What potential vulnerabilities do they come with? What promising mitigations may be developed for them?
  • Is there new terminology that needs standardization?

NIST intends to keep the document open for comments for an extended period of time to engage with stakeholders and invite contributions to an up-to-date taxonomy that serves the needs of the public. 



artificial intelligence; machine learning; attack taxonomy; evasion; data poisoning; privacy breach; attack mitigation; data modality; trojan attack, backdoor attack; chatbot
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