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

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

This NIST Trustworthy and Responsible AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is built on surveying the AML literature and is arranged in a conceptual hierarchy that includes key types of ML methods and lifecycle stages of attack, attacker goals and objectives, and attacker capabilities and knowledge of the learning process. The report provides corresponding methods for mitigating and managing the consequences of attacks, meant to inform standards and practice guides for assessing and managing AI system security by establishing a common language for the AML landscape. The data-driven approach of machine learning introduces security and privacy challenges beyond classical threats. These include the potential for adversarial manipulation of training data, adversarial exploitation of model vulnerabilities, and malicious interaction with models to exfiltrate sensitive information. AML is concerned with studying the capabilities of attackers and their goals, the design of attack methods that exploit ML vulnerabilities during the development, training, and deployment phases, and the design of ML algorithms that can withstand these challenges. The taxonomy of AML is defined with respect to five dimensions of risk assessment: AI system type, stage of the ML lifecycle process, attacker goals, attacker capabilities, and attacker knowledge.

Pillar: AI Governance & Law · Authority: National Institute of Standards and Technology · Version: 1.0.0 · Last updated: 2024-01-02

Primary source: https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-2e2023.pdf

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