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.

6 in Exfiltration · 101 total

IDTitleSummary
AML.T0024Exfiltration via AI Inference APIAdversaries may exfiltrate private information via [AI Model Inference API Access](/techniques/AML.T0040). AI Models have been shown leak private information a…
AML.T0025Exfiltration via Cyber MeansAdversaries may exfiltrate AI artifacts or other information relevant to their goals via traditional cyber means. See the ATT&CK [Exfiltration](https://attack…
AML.T0056Extract LLM System PromptAdversaries may attempt to extract a large language model's (LLM) system prompt. This can be done via prompt injection to induce the model to reveal its own sy…
AML.T0057LLM Data LeakageAdversaries may craft prompts that induce the LLM to leak sensitive information. This can include private user data or proprietary information. The leaked info…
AML.T0077LLM Response RenderingAn adversary may get a large language model (LLM) to respond with private information that is hidden from the user when the response is rendered by the user's …
AML.T0086Exfiltration via AI Agent Tool InvocationAI agent tools capable of performing write operations may be invoked to exfiltrate data to an adversary. Sensitive information can be encoded into the tool's i…
Sourced from MITRE ATLAS. Curated by Adam Lundqvist, Founder at SQUR.