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Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy

arXiv 2301.07474 ↗First posted 18 Jan 2023cs.CR cs.AI cs.LGcited by 0

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Abstract (arXiv)

In this article, we propose the Artificial Intelligence Security Taxonomy to systematize the knowledge of threats, vulnerabilities, and security controls of machine-learning-based (ML-based) systems. We first classify the damage caused by attacks against ML-based systems, define ML-specific security, and discuss its characteristics. Next, we enumerate all relevant assets and stakeholders and provide a general taxonomy for ML-specific threats. Then, we collect a wide range of security controls against ML-specific threats through an extensive review of recent literature. Finally, we classify the vulnerabilities and controls of an ML-based system in terms of each vulnerable asset in the system's entire lifecycle.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 97 of its 160 references that are in the map. First posted on arXiv 18 Jan 2023.

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97 of 160 references.

References · 97

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38 more
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