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MITRE ATLAS AI Supply Chain Compromise Techniques (AML.T0010) - Targeting Model Repositories, Pre-Trained Weights, Datasets, and Third-Party AI Pipeline Components

This node addresses adversarial techniques under MITRE ATLAS AML.T0010, focusing on AI supply chain compromises such as targeting model repositories,…

What MITRE ATLAS AI Supply Chain Compromise Techniques (AML.T0010) - Targeting Model Repositories, Pre-Trained Weights, Datasets, and Third-Party AI Pipeline Components requires

This node addresses adversarial techniques under MITRE ATLAS AML.T0010, focusing on AI supply chain compromises such as targeting model repositories, pre-trained weights, datasets, and third-party pipeline components. Compliance frameworks like the EU AI Act and NIST AI RMF mandate robust defenses against these threats to ensure AI system integrity and security.

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#AML.T0010

SHA-256 integrity: 8a1e7a006914b20292869169661332747b0363de3b4653db546051c8c8c6b1ef

Primary Citations — 6 traced to source

  • MITRE ATLAS - AI Supply Chain Compromise (AML.T0010), https://raw.githubusercontent.com/mitre-atlas/atlas-data/main/dist/ATLAS.yaml#AML.T0010, Technique AML.T0010, 2024
  • EU AI Act - Regulation on Artificial Intelligence, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689, Article 15 (Robustness and Cybersecurity), 2024

+ 4 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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