What MITRE ATLAS Training Data Poisoning and Backdoor Attack Techniques (AML.T0020) - Corrupting Training and Fine-Tuning Datasets for Persistent Model Manipulation requires
This node addresses MITRE ATLAS technique AML.T0020 (Training Data Poisoning), focusing on adversarial corruption of training and fine-tuning datasets to embed persistent backdoors in AI models. Compliance with frameworks like the EU AI Act and NIST AI RMF is required to implement defenses against such model manipulation risks.
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.T0020
SHA-256 integrity: 074b80c5aa4029c2e4a72a314bfa1d8505917a10e823ead4c24d8279685977db
Primary Citations — 6 traced to source
- MITRE ATLAS - Training Data Poisoning (AML.T0020), https://raw.githubusercontent.com/mitre-atlas/atlas-data/main/dist/ATLAS.yaml#AML.T0020, AML.T0020, 2024
- EU AI Act - Regulation (EU) 2024/1689 on Artificial Intelligence, https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32024R1689, Article 10 (Data Governance), 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.
Access
- Discovery (free): /api/v1/nodes/mitre-atlas-data-poisoning.json — 6-field metadata
- Vault (full node): /api/v1/vault/nodes/mitre-atlas-data-poisoning.json — full 13-key payload, $0.01 USDC (L402/Skyfire/Direct Base)
- Canonical URL: https://bidda.com/intelligence/mitre-atlas-data-poisoning
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