{
  "canonicalUrl": "https://xn--mwe.com/glossary/adversarial-machine-learning/",
  "claimStatus": "RESEARCH FINDING",
  "code": "K01-TERM-165",
  "correctionStatus": "CURRENT K05 RELEASE",
  "definition": "The study and exploitation of ways that crafted inputs, poisoned data, model extraction, or manipulated feedback can cause machine-learning systems to fail.",
  "lastReviewed": "2026-08-16",
  "name": "Adversarial Machine Learning",
  "not": "It does not imply every model failure is an adversarial attack.",
  "plain": "Attackers can target the model and its data, not only the surrounding software.",
  "relatedTermCodes": [
    "K01-TERM-161",
    "K01-TERM-166",
    "K01-TERM-167",
    "K01-TERM-168"
  ],
  "releaseId": "K12-2026-08-16",
  "researchCutoff": "2026-08-16",
  "slug": "adversarial-machine-learning",
  "sourceRevalidatedAt": "2026-08-15T23:00:00Z",
  "status": "RESEARCH FINDING",
  "topic": "autonomous-warfare-systems",
  "type": "DefinedTerm"
}