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MITRE ATLAS Discover AI Artifacts (AML.T0007) - Adversary Discovery of AI Artifacts in Victim Environment

This node addresses MITRE ATLAS technique AML.T0007 (Discover AI Artifacts). Adversaries may search private sources to identify AI learning artifacts that…

What MITRE ATLAS Discover AI Artifacts (AML.T0007) - Adversary Discovery of AI Artifacts in Victim Environment requires

This node addresses MITRE ATLAS technique AML.T0007 (Discover AI Artifacts). Adversaries may search private sources to identify AI learning artifacts that exist on the system and gather information about them. These artifacts can include the software stack used to train and deploy models, training and testing data management systems, container registries, software repositories, and model zoos. This information can be used to identify targets for further collection, exfiltration, or disruption, and to tailor and improve attacks. Defending against this technique is required under EU AI Act, NIST AI RMF, and ISO/IEC 42001 obligations; this node operationalises the documented ATLAS mitigations and cross-instrument controls into a deterministic verification workflow.

Pillar: AI Governance & Law · Authority: MITRE Corporation · Version: 1.0.1 · Last updated:

Primary source: https://raw.githubusercontent.com/mitre-atlas/atlas-data/main/dist/ATLAS.yaml

SHA-256 integrity: 0dd77a7a353eba6c7ab7139860046ebe75619badc950d8a40f53dde014092dcc

Primary Citations — 8 traced to source

  • MITRE ATLAS - Discover AI Artifacts (AML.T0007), https://raw.githubusercontent.com/mitre-atlas/atlas-data/main/dist/ATLAS.yaml#AML.T0007, AML.T0007 technique entry, 2026
  • EU AI Act - Regulation (EU) 2024/1689 on Artificial Intelligence, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689, Article 15 (Accuracy, Robustness, Cybersecurity), 2024

+ 6 more citations (full bibliography, deterministic workflow, actionable schema and crosswalks) included in the vault unlock — $0.01 via Skyfire / L402 / Direct Base USDC.

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