Security

This domain is intended to codify the landscape of threats to a ML system.

ID		Name	Description
S0100		Software Vulnerability	Vulnerability in system around model—a traditional vulnerability
S0200		Supply Chain Compromise	Compromising development components of a ML model, e.g. data, model, hardware, and software stack.
	S0201	Model Compromise	Infected model file
	S0202	Software compromise	Upstream Dependency Compromise
S0300		Over-permissive API	Unintended information leakage through API
	S0301	Information Leak	Cloud Model API leaks more information than it needs to
	S0302	Excessive Queries	Cloud Model API isn’t sufficiently rate limited
S0400		Model Bypass	Intentionally try to make a model perform poorly
	S0401	Bad Features	The model uses features that are easily gamed by the attacker
	S0402	Insufficient Training Data	The bypass is not represented in the training data
	S0403	Adversarial Example	Input data points intentionally supplied to draw mispredictions. Potential Cause: Over permissive API
S0500		Exfiltration	Directly or indirectly exfiltrate ML artifacts
	S0501	Model inversion	Reconstruct training data through strategic queries
	S0502	Model theft	Extract model functionality through strategic queries
S0600		Data poisoning	Usage of poisoned data in the ML pipeline
	S0601	Ingest Poisoning	Attackers inject poisoned data into the ingest pipeline

NOTE
A number of categories map directly to techniques codified in MITRE ATLAS. In future, we intend to cover the full landscape of adversarial ML attacks under the Security domain.