The National Institute of Standards and Technology (NIST) has released a comprehensive report on adversarial machine learning (AML) that provides a taxonomy of concepts, terminology, and mitigation methods for AI security. The report, authored by experts from NIST, Northeastern University, and Robust Intelligence, reviewed the AML literature and organized the major types of ML methods, attacker goals, and capabilities into a conceptual hierarchy. It also provides methods for mitigating and managing the consequences of attacks, and highlights open challenges in the lifecycle of AI systems. With a glossary for non-expert readers, the report aims to establish a common language for future AI security standards and best practices. The full 106-page report is very detailed and references real-world attacks such as Prompt Injections. It's available for free.

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Online journalist Matthias is the co-founder and publisher of THE DECODER. He believes that artificial intelligence will fundamentally change the relationship between humans and computers.
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