{
  "node_id": "mitre-atlas-evade-ai-model",
  "title": "MITRE ATLAS Evade AI Model (AML.T0015) - Adversarial Evasion of AI Model Classification at Inference Time",
  "domain": "AI Governance & Law",
  "version": "1.0.1",
  "last_updated": "2026-07-22",
  "bluf": "This node addresses MITRE ATLAS technique AML.T0015 (Evade AI Model). Adversaries can Craft Adversarial Data that prevents an AI model from correctly identifying the contents of the data or Generate Deepfakes that fools an AI model expecting authentic data. This technique can be used to evade a downstream task where AI is utilized. The adversary may evade AI-based virus/malware detection or network scanning towards the goal of a traditional cyber attack. AI model evasion through deepfake generation may also provide initial access to systems that use AI-based biometric authentication. 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.",
  "paywall": {
    "status": "LOCKED",
    "unlock_cost_usd": "0.01",
    "skyfire_id": "41779894-ece2-4163-9761-b3b1b76e19b0"
  },
  "crosswalks": {
    "_available_keys": [
      "nist_framework",
      "iso_standard",
      "industry_mapping",
      "ai_overlay_2026"
    ],
    "_note": "Full crosswalk values included in vault response"
  },
  "dependencies": [
    "nist-ai-rmf-1-0",
    "eu-ai-act-2024",
    "iso-iec-42001-ai-management-system-2023",
    "mitre-atlas-mitigation-model-hardening",
    "mitre-atlas-mitigation-use-ensemble-methods",
    "mitre-atlas-mitigation-use-multi-modal-sensors",
    "mitre-atlas-mitigation-input-restoration",
    "mitre-atlas-mitigation-adversarial-input-detection",
    "mitre-atlas-mitigation-deepfake-detection",
    "mitre-atlas-craft-adversarial-data",
    "mitre-atlas-craft-adversarial-perturbations"
  ],
  "primary_citations_count": 12
}