Table of Contents

1. Adversarial ML 101
2. Why Adversarial ML Threat Matrix?
3. Structure of Adversarial ML Threat Matrix
4. Things to keep in mind before you use the framework
5. Contributors
6. Feedback and Contact Information
7. Adversarial ML Threat Matrix
8. Case Studies Page
   • ClearviewAI Misconfiguration
   • GPT-2 Model Replication
   • ProofPoint Evasion
   • Tay Poisoning
   • Microsoft Red Team Exercise
   • Bosch Team Experience with EdgeAI
   • MITRE -- Physical Adversarial Examples -- TBD

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The goal of this project is to position attacks on ML systems in an ATT&CK-style framework so that security analysts can orient themselves in this new and upcoming threats.

Contributors

Want to get involved? See Feedback and Contact Information

Organization	Contributors
Microsoft	Ram Shankar Siva Kumar, Hyrum Anderson, Will Pearce, Suzy Shapperle, Blake Strom, Madeline Carmichael, Matt Swann, Nick Beede, Kathy Vu, Andi Comissioneru, Sharon Xia, Mario Goertzel, Jeffrey Snover, Derek Adam, Deepak Manohar, Bhairav Mehta, Peter Waxman, Abhishek Gupta
MITRE	Mikel D. Rodriguez, Christina E Liaghati, Keith R. Manville, Michael R Krumdick
Bosch	Manojkumar Parmar
IBM	Pin-Yu Chen
NVIDIA	David Reber Jr., Keith Kozo, Christopher Cottrell, Daniel Rohrer
Airbus	Adam Wedgbury
Deep Instinct	Nadav Maman
TwoSix	David Slater
University of Toronto	Adelin Travers, Jonas Guan, Nicolas Papernot
Cardiff University	Pete Burnap
Software Engineering Institute/Carnegie Mellon University	Nathan M. VanHoudnos
Berryville Institute of Machine Learning	Gary McGraw, Harold Figueroa, Victor Shepardson, Richie Bonett