{
  "schema_version": "1.3",
  "url": "https://trustbutveri.fyi/explorer/?mechanisms=M-0010,M-0020&cols=claims,hardware,sees",
  "data_generated": "2026-10-09",
  "definitions": {
    "methodology": "https://trustbutveri.fyi/about/methodology/",
    "readiness": "https://trustbutveri.fyi/about/readiness/",
    "filters": [
      {
        "id": "prover",
        "label": "Prover",
        "question": "How far can the party being checked be trusted?",
        "options": [
          {
            "value": "cooperative",
            "label": "Cooperative"
          },
          {
            "value": "semi-trusted",
            "label": "Semi-trusted"
          },
          {
            "value": "adversarial",
            "label": "Adversarial"
          }
        ],
        "rule": "Keeps mechanisms whose threat model holds against at least this prover. Adversarial is the strongest assumption.",
        "about": "The prover is the party being checked. Semi-trusted designs rely on part of its stack: usually the chip vendor's hardware root of trust, its firmware or counters, or its supply-chain records. Adversarial designs aim to hold even if it cheats wherever the checks allow, within their stated assumptions."
      },
      {
        "id": "onsite",
        "label": "Verifier devices on site",
        "question": "May the verifier install its own hardware at the prover's sites?",
        "options": [
          {
            "value": "no",
            "label": "Not allowed"
          }
        ],
        "rule": "\"Not allowed\" removes mechanisms that need a retrofit device, such as a network tap or a sealed sensor.",
        "about": "Some mechanisms need a device the verifier owns or trusts at the prover's facility, such as a network tap, a bandwidth limiter or a sealed sensor. Choose Not allowed when the setting rules that out. Inspectors are not covered."
      },
      {
        "id": "coop",
        "label": "Prover cooperation",
        "question": "How much must the prover take part?",
        "options": [
          {
            "value": "partial",
            "label": "Partial at most"
          },
          {
            "value": "none",
            "label": "Not required"
          }
        ],
        "rule": "\"Partial at most\" removes mechanisms that need the prover's active participation. \"Not required\" keeps only those that work without it.",
        "about": "Required: the prover takes part, for example by logging requests, producing proofs or opening records. Partial: some access, such as installing a device. Not required: works from outside, such as satellite imagery."
      },
      {
        "id": "chips",
        "label": "Chips",
        "question": "May the proposal depend on new chip designs?",
        "options": [
          {
            "value": "existing",
            "label": "Existing chips only"
          }
        ],
        "rule": "\"Existing chips only\" removes mechanisms that need changes to future chip designs.",
        "about": "New chip features take years to reach a deployed fleet and cover only chips made after they ship. Mechanisms that use shipping features, such as trusted execution environments or performance counters, stay."
      },
      {
        "id": "ready",
        "label": "Minimum development status",
        "question": "Development status",
        "options": [
          {
            "value": "R1",
            "label": "Proposed"
          },
          {
            "value": "R2",
            "label": "Research demonstration"
          },
          {
            "value": "R3",
            "label": "Operational use"
          },
          {
            "value": "R4",
            "label": "Legacy independent-evaluation filter",
            "legacy": true
          }
        ],
        "rule": "Keeps mechanisms whose readiness level is at least this one.",
        "about": "A level describes the public evidence for a mechanism's stated use, not its cost or feasibility. R3 can still have open critical flaws."
      },
      {
        "id": "tested",
        "label": "Attack testing",
        "question": "How hard has each mechanism been attacked in public?",
        "options": [
          {
            "value": "analysis",
            "label": "Published analysis"
          },
          {
            "value": "red-teamed",
            "label": "Red-teamed"
          },
          {
            "value": "independent-red-team",
            "label": "Independent red-team"
          }
        ],
        "rule": "Keeps mechanisms whose strongest published attack testing is at least this.",
        "about": "The strongest published attempt to break the mechanism for its verification use: a security analysis, red-teaming by its developers or collaborators, or a red team independent of them."
      },
      {
        "id": "hide",
        "label": "Keep hidden from the verifier",
        "question": "What must the verifier never see?",
        "options": [
          {
            "value": "weights",
            "label": "Model weights"
          },
          {
            "value": "io",
            "label": "Inputs and outputs"
          },
          {
            "value": "training",
            "label": "Training data"
          }
        ],
        "rule": "Removes mechanisms that show the asset to the verifier. Conditional or unspecified exposure stays with a note and needs checking against the privacy requirement.",
        "about": "Model weights: the checked model's parameters. Inputs and outputs: the requests a deployed model serves and its responses. Training data: what a model was trained on. Each mechanism's exposure is the editors' reading of its record: shown, depends on the design (kept, with a note), hidden, not involved, or unspecified for a selected implementation. Code and configuration are not covered yet."
      }
    ],
    "exposure": "For model weights, inputs and outputs, and training data. This is the editors' reading of each mechanism's record (its threat model, how it works and its limitations), not a field of the record. Shown: the verifier sees it. Depends: on the design or variant, or the verifier sees only samples. Hidden: the verifier sees only commitments, hashes, proofs or results. Not involved: the record does not handle it. Unspecified: the selected implementation has no asset-specific assessment here.",
    "claim_status": {
      "addressed": "A mechanism in the proposal is aimed at this claim and is not excluded by the filters.",
      "partly-addressed": "Only supporting mechanisms, or mechanisms aimed at it that the filters exclude.",
      "unaddressed": "No mechanism in the proposal addresses this claim."
    },
    "finding_classification": {
      "failure": {
        "label": "Known failures",
        "singular": "Known failure",
        "anchor": "known-flaws"
      },
      "scope-limitation": {
        "label": "Scope limitations",
        "singular": "Scope limitation",
        "anchor": "scope-limitations"
      },
      "open-question": {
        "label": "Open questions",
        "singular": "Open question",
        "anchor": "open-questions"
      }
    },
    "finding_scope": "Evidence scope describes where a finding was demonstrated; it does not establish applicability to every implementation in the mechanism family.",
    "claim_finding_scope": "open_critical_findings names active failures on the assessed records; open_critical_context names conditional family failures whose implementation applicability is unassessed.",
    "legacy_status": "The status field retains covered/partial/none for compatibility. It names claim links, never successful verification. Use claim_status and status_label for presentation."
  },
  "filters": {
    "prover": "",
    "onsite": "",
    "coop": "",
    "chips": "",
    "ready": "",
    "tested": "",
    "hide": []
  },
  "mechanisms_passing_filters": 25,
  "claims": [],
  "mechanisms": [
    {
      "id": "M-0010",
      "title": "On-chip telemetry from timing, memory and performance counters",
      "url": "https://trustbutveri.fyi/mechanisms/on-chip-telemetry/",
      "assessment_record": {
        "id": "M-0010",
        "title": "On-chip telemetry from timing, memory and performance counters",
        "url": "https://trustbutveri.fyi/mechanisms/on-chip-telemetry/"
      },
      "finding_counts": {
        "failure": 2,
        "scope_limitation": 2,
        "open_question": 1,
        "open_failures": {
          "critical": 0,
          "significant": 2,
          "minor": 0
        }
      },
      "selected_implementation": null,
      "readiness": {
        "level": "R2",
        "scope": "workload evidence from GPU counters and timing, assuming authentic measurements",
        "confidence": "medium",
        "evidence": [
          "S-0033",
          "S-0034",
          "S-0037"
        ]
      },
      "development_status": {
        "code": "R2",
        "label": "Research demonstration",
        "short": "Research demo",
        "rank": 2,
        "legacy_code": "R2"
      },
      "security_evidence": {
        "attack_testing": {
          "status": "red-teamed",
          "label": "Published attack testing",
          "kind": "practical",
          "attribution": "Developers or collaborators"
        },
        "independent_evaluation": {
          "status": "unassessed"
        },
        "formal_proof": {
          "status": "unassessed"
        },
        "deployment_assurance": {
          "status": "unassessed"
        },
        "legacy_evaluation_code": null,
        "scoped_findings": [
          {
            "n": 2,
            "severity": "significant",
            "status": "open",
            "evidence_scope": "unassessed",
            "sources": [
              "S-0037"
            ]
          },
          {
            "n": 4,
            "severity": "significant",
            "status": "open",
            "evidence_scope": "unassessed",
            "sources": [
              "S-0014",
              "S-1200"
            ]
          }
        ],
        "open_failures": {
          "critical": 0,
          "significant": 2,
          "minor": 0
        }
      },
      "assessed_properties": {
        "threat_model": "semi-trusted",
        "hardware_requirement": "existing-features",
        "prover_cooperation": "partial",
        "adversarial_evaluation": "red-teamed"
      },
      "claims": [],
      "exposure": {
        "weights": "partial",
        "io": "partial",
        "training": "partial",
        "note": "Counters do not read weights or data, but richer counters can leak secrets through side channels."
      },
      "family_finding_context": [],
      "filter_issues": []
    },
    {
      "id": "M-0020",
      "title": "Remote detection of data centres",
      "url": "https://trustbutveri.fyi/mechanisms/remote-detection-of-data-centres/",
      "assessment_record": {
        "id": "M-0020",
        "title": "Remote detection of data centres",
        "url": "https://trustbutveri.fyi/mechanisms/remote-detection-of-data-centres/"
      },
      "finding_counts": {
        "failure": 0,
        "scope_limitation": 2,
        "open_question": 1,
        "open_failures": {
          "critical": 0,
          "significant": 0,
          "minor": 0
        }
      },
      "selected_implementation": null,
      "readiness": {
        "level": "R1",
        "scope": "finding undeclared data centres above an agreed compute threshold",
        "confidence": "medium",
        "evidence": [
          "S-1410",
          "S-0002",
          "S-1409",
          "S-1411",
          "S-3301"
        ]
      },
      "development_status": {
        "code": "R1",
        "label": "Proposed",
        "short": "Proposed",
        "rank": 1,
        "legacy_code": "R1"
      },
      "security_evidence": {
        "attack_testing": {
          "status": "analysis",
          "label": "Published security analysis",
          "kind": "analysis"
        },
        "independent_evaluation": {
          "status": "unassessed"
        },
        "formal_proof": {
          "status": "unassessed"
        },
        "deployment_assurance": {
          "status": "unassessed"
        },
        "legacy_evaluation_code": null,
        "scoped_findings": [],
        "open_failures": {
          "critical": 0,
          "significant": 0,
          "minor": 0
        }
      },
      "assessed_properties": {
        "threat_model": "adversarial",
        "hardware_requirement": "none",
        "prover_cooperation": "not-required",
        "adversarial_evaluation": "analysis"
      },
      "claims": [],
      "exposure": {
        "weights": "none",
        "io": "none",
        "training": "none",
        "note": "Works from outside the facility; it does not handle model data."
      },
      "family_finding_context": [],
      "filter_issues": []
    }
  ],
  "strengths": {
    "covered": [],
    "production": [],
    "operationalUse": [],
    "adversarial": [
      "M-0020"
    ],
    "noNewHardware": [
      "M-0010",
      "M-0020"
    ],
    "mitigated": [],
    "notCounted": []
  },
  "properties": {
    "covered": [],
    "production": [],
    "operationalUse": [],
    "adversarial": [
      "M-0020"
    ],
    "noNewHardware": [
      "M-0010",
      "M-0020"
    ],
    "mitigated": [],
    "notCounted": []
  },
  "attack_testing": [
    {
      "id": "M-0010",
      "record": "M-0010",
      "evaluation": "red-teamed",
      "in_setting": true
    },
    {
      "id": "M-0020",
      "record": "M-0020",
      "evaluation": "analysis",
      "in_setting": true
    }
  ],
  "selected_implementations": {},
  "weaknesses": {
    "gaps": [],
    "excluded": [],
    "unlinked": [],
    "critical": [],
    "significant": [
      {
        "mech": "M-0010",
        "n": 2,
        "historical": false,
        "title": "Adversarially disguised fine-tuning partly evades classification",
        "classification": "failure",
        "kind": "demonstrated-attack",
        "severity": "significant",
        "status": "open",
        "evidence_scope": null,
        "scope_note": null,
        "related_finding": null,
        "description": "Across 20 evasion strategy families in five monitor-evader rounds, the classifier's accuracy against the most challenging disguised workloads held out from training was 43–87%. White-box LoRA fine-tuning was the only evasion family detected less than half the time. The evaluation covered single nodes, PyTorch-level evasion and NVIDIA hardware.",
        "response": null,
        "sources": [
          "S-0037"
        ]
      },
      {
        "mech": "M-0010",
        "n": 4,
        "historical": false,
        "title": "Counters leak information about protected workloads",
        "classification": "failure",
        "kind": "theoretical-argument",
        "severity": "significant",
        "status": "open",
        "evidence_scope": null,
        "scope_note": null,
        "related_finding": null,
        "description": "Performance counters have been used as a side channel against TEEs, for example in CounterSEVeillance. NVIDIA disables performance counters in full confidential-computing mode, stating that they could provide an avenue for side-channel attacks. Richer counters for verification therefore pull against confidentiality.",
        "response": null,
        "sources": [
          "S-0014",
          "S-1200"
        ]
      }
    ],
    "criticalMechanisms": [],
    "significantMechanisms": [
      "M-0010"
    ],
    "familyContext": [],
    "scopeLimitations": [
      {
        "mech": "M-0010",
        "n": 1,
        "historical": false,
        "title": "Software-read telemetry can be forged by the operator",
        "classification": "scope-limitation",
        "kind": "theoretical-argument",
        "severity": "critical",
        "status": "open",
        "evidence_scope": null,
        "scope_note": null,
        "related_finding": null,
        "description": "NVML-based classification assumes trustworthy telemetry. Without a tamper-resistant read path, an authenticated telemetry channel and secure boot of the monitoring software, an operator who controls the full software stack could forge counter values. Monfared et al. start from the same premise: current GPUs expose little trusted telemetry and can be modified or virtualized.",
        "response": null,
        "sources": [
          "S-0037",
          "S-0033"
        ],
        "helps": [
          {
            "by": "M-0009",
            "how": "A guarantee processor on the chip would give the tamper-resistant, authenticated telemetry path the flaw says is missing."
          }
        ]
      },
      {
        "mech": "M-0010",
        "n": 3,
        "historical": false,
        "title": "Timing challenges do not identify the individual chip",
        "classification": "scope-limitation",
        "kind": "theoretical-argument",
        "severity": "significant",
        "status": "open",
        "evidence_scope": null,
        "scope_note": null,
        "related_finding": null,
        "description": "GEMM and VDF challenges can be answered by identical GPUs elsewhere, and floating-point fingerprints distinguish GPU models, not individual devices. GPU virtualization adds timing leakage that prevents attributing compute use.",
        "response": null,
        "sources": [
          "S-0033"
        ]
      },
      {
        "mech": "M-0020",
        "n": 1,
        "historical": false,
        "title": "Facilities can be disguised or hidden",
        "classification": "scope-limitation",
        "kind": "theoretical-argument",
        "severity": "significant",
        "status": "open",
        "evidence_scope": null,
        "scope_note": null,
        "related_finding": null,
        "description": "Halstead and Larsen discuss two ways to hide a facility. One is to disguise it as a legitimate industrial site. The other is to build it underground, with cooling that avoids visible heat plumes. They note that the underground option requires bespoke engineering.",
        "response": null,
        "sources": [
          "S-1410"
        ]
      },
      {
        "mech": "M-0020",
        "n": 2,
        "historical": false,
        "title": "Small sites may not be detectable",
        "classification": "scope-limitation",
        "kind": "theoretical-argument",
        "severity": "significant",
        "status": "open",
        "evidence_scope": null,
        "scope_note": null,
        "related_finding": null,
        "description": "Halstead and Larsen conclude that a sufficiently small covert project could not be ruled out with confidence. In their estimates, the chance of detection is lower for smaller sites. Krawec notes that small data centres in existing buildings may lack the distinctive features of large facilities.",
        "response": null,
        "sources": [
          "S-1410",
          "S-1409"
        ],
        "helps": [
          {
            "by": "M-0019",
            "how": "Accounts for chips from the fab onwards, which does not depend on a site being visible."
          }
        ]
      }
    ],
    "openQuestions": [
      {
        "mech": "M-0010",
        "n": 5,
        "historical": false,
        "title": "No quantified error rates or formal thresholds for timing primitives",
        "classification": "open-question",
        "kind": "open-question",
        "severity": "minor",
        "status": "open",
        "evidence_scope": null,
        "scope_note": null,
        "related_finding": null,
        "description": "Monfared et al. state that false-positive and false-negative rates are not quantified and leave hardware-specific formal thresholds to future work.",
        "response": null,
        "sources": [
          "S-0033"
        ]
      },
      {
        "mech": "M-0020",
        "n": 3,
        "historical": false,
        "title": "Search for unknown sites is undemonstrated",
        "classification": "open-question",
        "kind": "open-question",
        "severity": "significant",
        "status": "open",
        "evidence_scope": null,
        "scope_note": null,
        "related_finding": null,
        "description": "Krawec reports that telling data centres apart from other industrial facilities systematically is difficult. Automating detection would need large amounts of training imagery and a purpose-trained model. In Krawec's words, automated data-centre detection \"remains primarily conceptual at present\".",
        "response": null,
        "sources": [
          "S-1409"
        ]
      }
    ],
    "minor": 0,
    "minorFindings": [],
    "minorBy": [],
    "notDemonstrated": [
      "M-0020"
    ],
    "newChip": []
  },
  "findings": [
    {
      "mech": "M-0010",
      "record": "M-0010",
      "n": 1,
      "historical": false,
      "title": "Software-read telemetry can be forged by the operator",
      "classification": "scope-limitation",
      "kind": "theoretical-argument",
      "severity": "critical",
      "status": "open",
      "evidence_scope": null,
      "scope_note": null,
      "related_finding": null,
      "description": "NVML-based classification assumes trustworthy telemetry. Without a tamper-resistant read path, an authenticated telemetry channel and secure boot of the monitoring software, an operator who controls the full software stack could forge counter values. Monfared et al. start from the same premise: current GPUs expose little trusted telemetry and can be modified or virtualized.",
      "response": null,
      "sources": [
        "S-0037",
        "S-0033"
      ],
      "helps": [
        {
          "by": "M-0009",
          "how": "A guarantee processor on the chip would give the tamper-resistant, authenticated telemetry path the flaw says is missing."
        }
      ]
    },
    {
      "mech": "M-0010",
      "record": "M-0010",
      "n": 2,
      "historical": false,
      "title": "Adversarially disguised fine-tuning partly evades classification",
      "classification": "failure",
      "kind": "demonstrated-attack",
      "severity": "significant",
      "status": "open",
      "evidence_scope": null,
      "scope_note": null,
      "related_finding": null,
      "description": "Across 20 evasion strategy families in five monitor-evader rounds, the classifier's accuracy against the most challenging disguised workloads held out from training was 43–87%. White-box LoRA fine-tuning was the only evasion family detected less than half the time. The evaluation covered single nodes, PyTorch-level evasion and NVIDIA hardware.",
      "response": null,
      "sources": [
        "S-0037"
      ]
    },
    {
      "mech": "M-0010",
      "record": "M-0010",
      "n": 3,
      "historical": false,
      "title": "Timing challenges do not identify the individual chip",
      "classification": "scope-limitation",
      "kind": "theoretical-argument",
      "severity": "significant",
      "status": "open",
      "evidence_scope": null,
      "scope_note": null,
      "related_finding": null,
      "description": "GEMM and VDF challenges can be answered by identical GPUs elsewhere, and floating-point fingerprints distinguish GPU models, not individual devices. GPU virtualization adds timing leakage that prevents attributing compute use.",
      "response": null,
      "sources": [
        "S-0033"
      ]
    },
    {
      "mech": "M-0010",
      "record": "M-0010",
      "n": 4,
      "historical": false,
      "title": "Counters leak information about protected workloads",
      "classification": "failure",
      "kind": "theoretical-argument",
      "severity": "significant",
      "status": "open",
      "evidence_scope": null,
      "scope_note": null,
      "related_finding": null,
      "description": "Performance counters have been used as a side channel against TEEs, for example in CounterSEVeillance. NVIDIA disables performance counters in full confidential-computing mode, stating that they could provide an avenue for side-channel attacks. Richer counters for verification therefore pull against confidentiality.",
      "response": null,
      "sources": [
        "S-0014",
        "S-1200"
      ]
    },
    {
      "mech": "M-0010",
      "record": "M-0010",
      "n": 5,
      "historical": false,
      "title": "No quantified error rates or formal thresholds for timing primitives",
      "classification": "open-question",
      "kind": "open-question",
      "severity": "minor",
      "status": "open",
      "evidence_scope": null,
      "scope_note": null,
      "related_finding": null,
      "description": "Monfared et al. state that false-positive and false-negative rates are not quantified and leave hardware-specific formal thresholds to future work.",
      "response": null,
      "sources": [
        "S-0033"
      ]
    },
    {
      "mech": "M-0020",
      "record": "M-0020",
      "n": 1,
      "historical": false,
      "title": "Facilities can be disguised or hidden",
      "classification": "scope-limitation",
      "kind": "theoretical-argument",
      "severity": "significant",
      "status": "open",
      "evidence_scope": null,
      "scope_note": null,
      "related_finding": null,
      "description": "Halstead and Larsen discuss two ways to hide a facility. One is to disguise it as a legitimate industrial site. The other is to build it underground, with cooling that avoids visible heat plumes. They note that the underground option requires bespoke engineering.",
      "response": null,
      "sources": [
        "S-1410"
      ]
    },
    {
      "mech": "M-0020",
      "record": "M-0020",
      "n": 2,
      "historical": false,
      "title": "Small sites may not be detectable",
      "classification": "scope-limitation",
      "kind": "theoretical-argument",
      "severity": "significant",
      "status": "open",
      "evidence_scope": null,
      "scope_note": null,
      "related_finding": null,
      "description": "Halstead and Larsen conclude that a sufficiently small covert project could not be ruled out with confidence. In their estimates, the chance of detection is lower for smaller sites. Krawec notes that small data centres in existing buildings may lack the distinctive features of large facilities.",
      "response": null,
      "sources": [
        "S-1410",
        "S-1409"
      ],
      "helps": [
        {
          "by": "M-0019",
          "how": "Accounts for chips from the fab onwards, which does not depend on a site being visible."
        }
      ]
    },
    {
      "mech": "M-0020",
      "record": "M-0020",
      "n": 3,
      "historical": false,
      "title": "Search for unknown sites is undemonstrated",
      "classification": "open-question",
      "kind": "open-question",
      "severity": "significant",
      "status": "open",
      "evidence_scope": null,
      "scope_note": null,
      "related_finding": null,
      "description": "Krawec reports that telling data centres apart from other industrial facilities systematically is difficult. Automating detection would need large amounts of training imagery and a purpose-trained model. In Krawec's words, automated data-centre detection \"remains primarily conceptual at present\".",
      "response": null,
      "sources": [
        "S-1409"
      ]
    }
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