{
  "answer": "Use layered systems rather than one model: deterministic boundaries, diverse sensors and analyzers, adversarially trained models, immutable baselines, deception, moving-target defenses, Byzantine-resilient coordination, model and data provenance, runtime assurance, and recovery that assumes some agents will fail or be compromised.",
  "canonicalUrl": "https://xn--mwe.com/questions/how-should-machine-intelligence-defend-against-machine-intelligence/",
  "claimStatus": "PROJECT TECHNICAL PROPOSAL",
  "correctionStatus": "CURRENT K05 RELEASE",
  "id": "K01-ANSWER-101",
  "lastReviewed": "2026-08-16",
  "question": "How should Machine Intelligence defend against adversarial Machine Intelligence?",
  "releaseId": "K12-2026-08-16",
  "researchCutoff": "2026-08-16",
  "shortAnswer": "Use layered systems rather than one model: deterministic boundaries, diverse sensors and analyzers, adversarially trained models, immutable baselines, deception, moving-target defenses, Byzantine-resilient coordination, model and data provenance, runtime assurance, and recovery that assumes some agents will fail or be compromised.",
  "slug": "how-should-machine-intelligence-defend-against-machine-intelligence",
  "sourceRevalidatedAt": "2026-08-15T23:00:00Z",
  "status": "PROJECT TECHNICAL PROPOSAL",
  "topic": "autonomous-warfare-systems",
  "type": "Question"
}