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Subject: Autonomy and Counter-Autonomy

Adversarial ML Threat Matrix: Adversarial Tactics, Techniques, and Common Knowledge of Machine Learning

Adversarial ML Threat Matrix: Adversarial Tactics, Techniques, and Common Knowledge of Machine Learning

• CERT/CC Blog
Jonathan Spring

My colleagues, Nathan VanHoudnos, April Galyardt, Allen Householder, and I would like you to know that today Microsoft and MITRE are releasing their Adversarial Machine Learning Threat Matrix. This is a collaborative effort to bring MITRE's ATT&CK framework into securing production machine learning systems. You can read more at Microsoft's blog and MITRE's blog, as well as find a complete copy of the matrix on GitHub. We hope that you will join us in providing...

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Comments on NIST IR 8269: A Taxonomy and Terminology of Adversarial Machine Learning

Comments on NIST IR 8269: A Taxonomy and Terminology of Adversarial Machine Learning

• CERT/CC Blog
Jonathan Spring

The U.S. National Institute of Standards and Technology (NIST) recently held a public comment period on their draft report on proposed taxonomy and terminology of Adversarial Machine Learning (AML). AML sits at the intersection of many specialties of the SEI. Resilient engineering of Machine Learning (ML) systems requires good data science, good software engineering, and good cybersecurity. Our colleagues have suggested 11 foundational practices of AI engineering. In applications of ML to cybersecurity, we have...

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