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 Credential Access · 101 total

IDTitleSummary
AML.T0055Unsecured CredentialsAdversaries may search compromised systems to find and obtain insecurely stored credentials. These credentials can be stored and/or misplaced in many locations…
AML.T0082RAG Credential HarvestingAdversaries may attempt to use their access to a large language model (LLM) on the victim's system to collect credentials. Credentials may be stored in interna…
AML.T0083Credentials from AI Agent ConfigurationAdversaries may access the credentials of other tools or services on a system from the configuration of an AI agent. AI Agents often utilize external tools or…
AML.T0090OS Credential DumpingAdversaries may extract credentials from OS caches, application memory, or other sources on a compromised system. Credentials are often in the form of a hash o…
AML.T0098AI Agent Tool Credential HarvestingAdversaries may attempt to use their access to an AI agent on the victim's system to retrieve data from available agent tools to collect credentials. Agent too…
AML.T0106Exploitation for Credential AccessAdversaries may exploit software vulnerabilities in an attempt to collect credentials. Exploitation of a software vulnerability occurs when an adversary takes …
Sourced from MITRE ATLAS. Curated by Adam Lundqvist, Founder at SQUR.
MITRE ATLAS adversarial ML techniques — by tactic | SQUR Knowledge Base