{
  "schema_version": "1.2.0",
  "rubric_version": "1.1",
  "license": "CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)",
  "record": {
    "id": "O-0142",
    "slug": "university-of-cambridge",
    "title": "University of Cambridge",
    "aliases": [],
    "status": "published",
    "last_reviewed": "2026-09-25",
    "review_interval_days": 90,
    "steward": null,
    "provenance": {
      "drafted_by": "ai",
      "reviewed_by": [
        "codex-review"
      ]
    },
    "risk_flags": [],
    "flags": [],
    "kind": "academic-lab",
    "homepage": "https://www.cam.ac.uk/",
    "one_liner": "A UK university; researchers in its Department of Computer Science and Technology developed Attestable Audits, AI benchmarks run and attested inside trusted execution environments.",
    "sources": [
      {
        "source": "S-0009",
        "supports": "Attestable Audits: author affiliations, audit and inference protocols, prototype"
      }
    ],
    "type": "organization",
    "url": "https://trustbutveri.fyi/organizations/university-of-cambridge/",
    "source_file": "content/organizations/university-of-cambridge.md",
    "flags_all": [],
    "body_markdown": "All four authors of Attestable Audits list the University of Cambridge's Department of Computer Science and Technology as their affiliation [[S-0009]]:\n\n- The Attestable Audits paper runs AI safety benchmarks inside a trusted execution environment and publishes an attestation that binds the model hash, the audit code and data, and the result to a transparency log [[S-0009]]; see [[I-0007]] and [[M-0025]].\n- In the paper's inference protocol, each response carries an attestation that links the model, its earlier audit result, the prompt and the response [[S-0009]]; see [[M-0023]] and [[M-0012]].\n- A prototype ran a 4-bit, 8-billion-parameter model in CPU-only AWS Nitro Enclaves [[S-0009]]; see [[M-0008]].",
    "body_text": "All four authors of Attestable Audits list the University of Cambridge's Department of Computer Science and Technology as their affiliation [S-0009]: - The Attestable Audits paper runs AI safety benchmarks inside a trusted execution environment and publishes an attestation that binds the model hash, the audit code and data, and the result to a transparency log [S-0009]; see Attestable Audits and Confidential multi-party verification. - In the paper's inference protocol, each response carries an attestation that links the model, its earlier audit result, the prompt and the response [S-0009]; see Safeguard attestation and Model identity attestation. - A prototype ran a 4-bit, 8-billion-parameter model in CPU-only AWS Nitro Enclaves [S-0009]; see TEE remote attestation for AI workloads.",
    "referenced_by": [
      {
        "id": "M-0025",
        "title": "Confidential multi-party verification",
        "url": "https://trustbutveri.fyi/mechanisms/confidential-multi-party-verification/"
      },
      {
        "id": "M-0012",
        "title": "Model identity attestation",
        "url": "https://trustbutveri.fyi/mechanisms/model-identity-attestation/"
      },
      {
        "id": "M-0023",
        "title": "Safeguard attestation",
        "url": "https://trustbutveri.fyi/mechanisms/safeguard-attestation/"
      },
      {
        "id": "M-0008",
        "title": "TEE remote attestation for AI workloads",
        "url": "https://trustbutveri.fyi/mechanisms/tee-remote-attestation/"
      },
      {
        "id": "I-0007",
        "title": "Attestable Audits",
        "url": "https://trustbutveri.fyi/implementations/attestable-audits/"
      },
      {
        "id": "S-0009",
        "title": "Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments",
        "url": "https://trustbutveri.fyi/sources/schnabl-attestable-audits/"
      }
    ]
  }
}