Date Published: March 8, 2023
Comments Due:
Email Questions to:
Author(s)
Alina Oprea (Northeastern University), Apostol Vassilev (NIST)
Announcement
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.
This NIST AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is built on survey of the AML literature and is arranged in a conceptual hierarchy that includes key types of ML methods and lifecycle stage of attack, attacker goals and objectives, and attacker capabilities and knowledge of the learning process. The report also provides corresponding methods for mitigating and managing the consequences of attacks and points out relevant open challenges to take into account in the lifecycle of AI systems. The terminology used in the report is consistent with the literature on AML and is complemented by a glossary that defines key terms associated with the security of AI systems and is intended to assist non-expert readers. 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 and understanding of the rapidly developing AML landscape.
This NIST AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is built on survey of the AML literature and is arranged in a conceptual hierarchy that includes key types of ML methods and lifecycle stage of attack,...
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This NIST AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is built on survey of the AML literature and is arranged in a conceptual hierarchy that includes key types of ML methods and lifecycle stage of attack, attacker goals and objectives, and attacker capabilities and knowledge of the learning process. The report also provides corresponding methods for mitigating and managing the consequences of attacks and points out relevant open challenges to take into account in the lifecycle of AI systems. The terminology used in the report is consistent with the literature on AML and is complemented by a glossary that defines key terms associated with the security of AI systems and is intended to assist non-expert readers. 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 and understanding of the rapidly developing AML landscape.
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Keywords
artificial intelligence; machine learning; attack taxonomy; evasion; data poisoning; privacy breach; attack mitigation; data modality; trojan attack, backdoor attack; chatbot
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