{
  "node_id": "mitre-atlas-resource-intensive-queries",
  "title": "MITRE ATLAS Resource-Intensive Queries (AML.T0034.001) - Adversarial Resource-Intensive Queries threat to AI systems",
  "domain": "AI Governance & Law",
  "version": "1.0.1",
  "last_updated": "2026-07-22",
  "bluf": "This node addresses MITRE ATLAS technique AML.T0034.001 (Resource-Intensive Queries). Adversaries may craft inputs specifically designed to increase the compute resources required for processing. For generative AI models, adversaries may use long input sequences, requests for extremely long outputs, or prompts that require complex reasoning as strategies for increasing compute costs [[genai]]. For vision and language models, \"sponge examples\" [[arxiv]] can be used to maximize energy consumption and decision latency. Utilizing fewer resource-intensive queries instead of simply flooding the model with excessive queries may be more difficult to detect and block or limit. 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. Sub-technique of ATLAS AML.T0034.",
  "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"
  ],
  "primary_citations_count": 5
}