Threats, Vulnerabilities, and Controls of Machine Learning Based Systems: A Survey and Taxonomy. (arXiv:2301.07474v2 [cs.CR] UPDATED)

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 …

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