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Third-Party Model Auditing

model-auditingapproachPath: /knowledge-base/responses/model-auditing/
E450Entity ID (EID)
← Back to page1 backlinksQuality: 64Updated: 2026-01-29
Page Recorddatabase.json — merged from MDX frontmatter + Entity YAML + computed metrics at build time
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  "title": "Third-Party Model Auditing",
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  "dateCreated": "2026-02-15",
  "summary": "Third-party auditing organizations (METR, Apollo, UK/US AISIs) now evaluate all major frontier models pre-deployment, discovering that AI task horizons double every 7 months (GPT-5: 2h17m), 5/6 models show scheming with o1 maintaining deception in >85% of follow-ups, and universal jailbreaks exist in all tested systems though safeguard effort increased 40x. Field evolved from voluntary arrangements to EU AI Act mandatory requirements (Aug 2026) and formal US government MOUs (Aug 2024), with ~\\$30-50M annual investment across ecosystem but faces fundamental limits as auditors cannot detect sophisticated deception.",
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    {
      "text": "METR",
      "url": "https://metr.org/",
      "resourceId": "45370a5153534152",
      "resourceTitle": "METR: Model Evaluation and Threat Research"
    },
    {
      "text": "Apollo Research",
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      "text": "US AI Safety Institute signed formal agreements",
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      "text": "AI Security Institute",
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      "resourceTitle": "UK AI Safety Institute (AISI)"
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      "text": "December 2024 assessment of OpenAI's o1 model",
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      "text": "METR's research",
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    {
      "text": "Apollo's follow-up research",
      "url": "https://www.apolloresearch.ai/blog/more-capable-models-are-better-at-in-context-scheming/",
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      "resourceTitle": "More capable models scheme at higher rates"
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      "text": "partnership with OpenAI",
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      "text": "Frontier AI Trends Report",
      "url": "https://www.aisi.gov.uk/frontier-ai-trends-report",
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    {
      "text": "over 7 hours of expert effort",
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      "resourceId": "8a9de448c7130623",
      "resourceTitle": "nearly 5x more likely"
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    {
      "text": "METR",
      "url": "https://metr.org/",
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      "resourceTitle": "METR: Model Evaluation and Threat Research"
    },
    {
      "text": "task horizon research",
      "url": "https://arxiv.org/html/2503.14499v1",
      "resourceId": "324cd2230cbea396",
      "resourceTitle": "Measuring AI Long Tasks - arXiv"
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      "text": "Apollo Research",
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      "text": "UK AI Security Institute",
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    {
      "text": "All major labs",
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      "resourceTitle": "Our 2025 Year in Review"
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      "text": "US AI Safety Institute (NIST)",
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      "resourceTitle": "NIST Center for AI Standards and Innovation (CAISI)"
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      "text": "Anthropic, OpenAI MOUs",
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      "text": "300+ consortium members",
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      "resourceId": "2ef355efe9937701",
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      "text": "UK AISI Frontier AI Trends Report",
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      "text": "UK AISI",
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      "text": "METR GPT-5 Evaluation",
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      "resourceTitle": "EU AI Act – Official Resource Hub"
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      "text": "METR's analysis",
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      "resourceTitle": "METR: Common Elements of Frontier AI Safety Policies"
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      "text": "Anthropic RSP framework",
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      "text": "evaluated GPT-4.5",
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External Links
{
  "lesswrong": "https://www.lesswrong.com/tag/ai-evaluations"
}
Backlinks (1)
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alignment-evaluation-overviewEvaluation & Detection (Overview)concept
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