Security News > 2020 > October > Microsoft, MITRE Release Adversarial Machine Learning Threat Matrix
Microsoft and MITRE, in collaboration with a dozen other organizations, have developed a framework designed to help identify, respond to, and remediate attacks targeting machine learning systems.
The Adversarial ML Threat Matrix, which Microsoft has released in collaboration with MITRE, IBM, NVIDIA, Airbus, Bosch, Deep Instinct, Two Six Labs, Cardiff University, the University of Toronto, PricewaterhouseCoopers, the Software Engineering Institute at Carnegie Mellon University, and the Berryville Institute of Machine Learning, is an industry-focused open framework that aims to address this issue.
Structured like the ATT&CK framework, the Adversarial ML Threat Matrix is based on observed attacks that have been vetted as effective against production ML systems.
Attacks targeting these systems are possible because of inherent limitations underlying ML algorithms and require a new approach to security and a shift in how cyber adversary behavior is modelled, to ensure the accurate reflection of emerging threat vectors, as well as the fast evolving adversarial machine learning attack lifecycle.
The newly released framework is a first attempt at creating a knowledge base on the manner in which ML systems can be attacked and the partnering companies will modify it with input received from the security and machine learning community.