101 indexed

ATLASATLAS adversarial ML techniques

101 MITRE ATLAS top-level techniques covering the adversarial-ML attack surface, grouped by tactic. Authored by Adam Lundqvist.

5 in Ai Attack Staging · 101 total

IDTitleSummary
AML.T0005Create Proxy AI ModelAdversaries may obtain models to serve as proxies for the target model in use at the victim organization. Proxy models are used to simulate complete access to …
AML.T0042Verify AttackAdversaries can verify the efficacy of their attack via an inference API or access to an offline copy of the target model. This gives the adversary confidence …
AML.T0043Craft Adversarial DataAdversarial data are inputs to an AI model that have been modified such that they cause the adversary's desired effect in the target model. Effects can range f…
AML.T0088Generate DeepfakesAdversaries may use generative artificial intelligence (GenAI) to create synthetic media (i.e. imagery, video, audio, and text) that appear authentic. These "[…
AML.T0102Generate Malicious CommandsAdversaries may use large language models (LLMs) to dynamically generate malicious commands from natural language. Dynamically generated commands may be harder…
Sourced from MITRE ATLAS. Curated by Adam Lundqvist, Founder at SQUR.