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.
7 in Persistence · 101 total
| ID | Title | Summary |
|---|---|---|
| AML.T0018 | Manipulate AI Model | Adversaries may directly manipulate an AI model to change its behavior or introduce malicious code. Manipulating a model gives the adversary a persistent chang… |
| AML.T0061 | LLM Prompt Self-Replication | An adversary may use a carefully crafted [LLM Prompt Injection](/techniques/AML.T0051) designed to cause the LLM to replicate the prompt as part of its output.… |
| AML.T0070 | RAG Poisoning | Adversaries may inject malicious content into data indexed by a retrieval augmented generation (RAG) system to contaminate a future thread through RAG-based se… |
| AML.T0080 | AI Agent Context Poisoning | Adversaries may attempt to manipulate the context used by an AI agent's large language model (LLM) to influence the responses it generates or actions it takes.… |
| AML.T0081 | Modify AI Agent Configuration | Adversaries may modify the configuration files for AI agents on a system. This allows malicious changes to persist beyond the life of a single agent and affect… |
| AML.T0099 | AI Agent Tool Data Poisoning | Adversaries may place malicious content on a victim's system where it can be retrieved by an AI Agent Tool. This may be accomplished by placing documents in a … |
| AML.T0110 | AI Agent Tool Poisoning | Adversaries may achieve persistence by poisoning tools used by AI agents including built-in tools or tools available to the agent via Model Context Protocol (M… |