Proposal Explorer

Add claims to see which ones the mechanisms address.

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Excluded by your filters: Model identity attestation and Network taps and certifiers

Published attempts to break a system, including those that found failures. Testing history does not show that open flaws are resolved.

• Model identity attestation, Attestable Audits: Analysis · excluded by filters
• Network taps and certifiers: Analysis · excluded by filters

Excluded by your filters

• Model identity attestation Readiness R2 is below the minimum of R3.
• Network taps and certifiers Readiness R1 is below the minimum of R3.

Family findings

• Model identity attestation

  Context for Attestable Audits. These findings concern the family or other implementations; applicability must be checked against their stated scope.

  • Underlying attestation can be forged or relayed in Model identity attestation

    Critical for the enclave route against an operator with physical access to affected hardware, or control of an unpatched SEV-SNP hypervisor. It does not apply to the recomputation route. PAL*M excludes physical attacks, and Tinfoil acknowledges this boundary.

    The enclave route inherits the platform-specific TEE attestation failures. Intel TDX forgery and H100 relay were demonstrated with physical access and host control. Battering RAM defeated AMD SEV-SNP attestation on DDR4 servers; RMPocalypse did so from malicious host software on platforms without AMD's fixes. These demonstrate failures of the trust roots, not of each model-commitment protocol. ^{3 4 5 6 7 8}

    Response: The TEE.fail authors report that physical interposer attacks are outside Intel's and AMD's threat models. AMD reports fixes for RMPocalypse.

    Demonstrated attack · Critical · Open · Inherited finding. On the record · Related finding in TEE remote attestation for AI workloads

    Related mechanism R1 Hardware-enabled guarantees (flexHEG) and guarantee processors: A tamper-protected enclosure around the chip is the proposed answer when the party that holds the hardware may attack it physically. A pointer, not evidence that this flaw is mitigated. Add

  • Launch-state attestation does not by itself cover weights loaded later in Model identity attestation

    Attestation measures launch state, and weights are read from disk after boot. A signature checked at load time does not stop a malicious hypervisor from altering the disk afterwards. Tinfoil reports mitigating this with dm-verity checks on every read. Unmeasured runtime configuration remains a general risk. ^{9 10}

    Theoretical argument · Significant · Mitigated. On the record

  • For private models, a user can confirm consistency but not content in Model identity attestation

    When weights are not published, users can check that the same root hash is served each time, but not what the model is. Pairing the hash with an attested evaluation, as in Attestable Audits, is one proposed remedy. ^{1 9}

    Open question · Significant · Open. On the record

  • Recomputation depends on trusted logging and randomness, and its tolerance leaves a covert channel in Model identity attestation

    The recomputation variant assumes that every input, output and seed is logged correctly, and that the attacker can neither predict nor manipulate which messages are sampled for verification. Legitimate nondeterminism concentrates at a few token positions, and slow leaks within the tolerated slack remain possible. An independent study showed that an adversary who controls the prompts roughly doubles the bits leaked per token, reducing the exfiltration slowdown from 146–254 times under benign prompts to 60–118 times. The attack targets the exfiltration bound, not the check that outputs match the declared model. ^{11 12}

    Demonstrated attack · Significant · Open. On the record

    Related mechanism R1 Network taps and certifiers: Taps are proposed to copy and hash traffic on the monitored links, reducing reliance on the prover's own log. This still depends on the monitored boundary and trusted capture. A pointer, not evidence that this flaw is mitigated. In the proposal.

    Related mechanism R3 Deterministic and bit-exact inference: Bit-exact inference would remove the numerical tolerance that leaves this channel. A pointer, not evidence that this flaw is mitigated. Add

Open significant flaws

7 flaws in 2 mechanisms

• Relies on the TEE vendor and inherits TEE attacks in Attestable Audits

  The prototype trusts AWS Nitro, not the Intel TDX or AMD SEV-SNP attestation roots targeted by the cited confidential-VM studies. Those studies are class context, not a demonstrated attack on this Nitro prototype.

  The design depends on trusting the TEE vendor, AWS in the prototype. The authors cite memory-aliasing, ciphertext side-channel and malicious-interrupt attacks on confidential VMs (BadRAM, CIPHERLEAKS, Heckler). Their answer is to revoke vulnerable base images once such attacks are discovered. ¹

  Response: The authors propose revoking vulnerable base images; they do not report a red-team evaluation of the prototype.

  Theoretical argument · Significant · Open · Inherited finding. On the record · Related finding in TEE remote attestation for AI workloads

• Prompt-based model exfiltration is a residual gap in Attestable Audits

  The authors state that "prompt-based model exfiltration during the user interaction step remains a residual gap". ¹

  Open question · Significant · Open. On the record

• Output nondeterminism leaves covert capacity in Network taps and certifiers

  Hashing cannot remove information hidden in the outputs themselves. The Secure Gateway Device paper estimates that about 0.1 bit per token remains even with seed-synchronized replay checks. For a 200k-GPU inference cluster at full load (2,000 tokens per GPU per second), that is about 40 Mbit/s of covert egress, enough to move a 1 TB model in under three days. The paper names this the core remaining challenge and points to deterministic replay or active scrubbing of hardware-induced entropy. An independent study found that an adversary who chooses the prompts roughly doubles the bits leaked per token under Gumbel-based inference verification; see Bounding unexplained information in outputs. ^{12 13}

  Theoretical argument · Significant · Open. On the record

  Related mechanism R3 Deterministic and bit-exact inference: Deterministic replay is one of the two remedies the flaw's source names. A pointer, not evidence that this flaw is mitigated. Add

  Related mechanism R2 Bounding unexplained information in outputs: Bounds the hidden information outputs can carry by measuring what the declared computation fails to predict. A pointer, not evidence that this flaw is mitigated. Add

• Some links cannot be passively tapped in Network taps and certifiers

  Cankaya notes that copper-connected scale-up domains (for example NVL72 racks and TPU v7 cubes) are much harder to tap than fibre, and that optical budgets make passive taps impractical on 400GBASE-SR8 multimode links. Amodo found no taps advertised for 53 GBaud links as of May 2026. ^{14 21}

  Open question · Significant · Open. On the record

• Encrypted fabrics hide plaintext from both parties in Network taps and certifiers

  Cankaya notes that with TEE-protected sessions whose keys are ephemeral and managed inside the TEE, neither the operator nor the manufacturer can recover session keys after the session, so tapped traffic could not be opened for recomputation. For other encrypted fabrics, the operator can retain keys. ¹⁴

  Open question · Significant · Open. On the record

• Residual side channels in simple passive setups in Network taps and certifiers

  Amodo's analysis of its own tapped prototype lists unvalidated header fields, timing of permitted traffic and variation in response formatting as residual channels, and concludes that the passive tap must be replaced by an active one. ¹⁷

  Theoretical argument · Significant · Open. On the record

• Completeness rests on physical monitoring left out of scope in Network taps and certifiers

  The Secure Gateway Device paper assumes the facility is physically monitored, and states that the whole architecture depends on the device being the only communication channel. It names radio emanation, power-line signalling and thermal channels as covert channels beyond that scope. ¹³

  Open question · Significant · Open. On the record

  Related mechanism R1 Side-channel suppression for isolated facilities: Addresses the radio, power-line and thermal channels that network-level designs leave out. A pointer, not evidence that this flaw is mitigated. Add

Open minor flaws

1 mechanism with minor findings

• CPU-only enclaves force small, quantized models and high cost in Attestable Audits

  Memory limits required 4-bit quantization, and the quantized model scored 51.4% on zero-shot MMLU. CPU inference cost 21.7 times as much per token as GPU inference, and the enclave roughly doubled the CPU cost. The authors wrote that H100 confidential computing had no multi-GPU support. NVIDIA's white paper of August 2025 describes a protected-PCIe mode that passes all eight GPUs of a Hopper HGX node to one confidential VM, with NVLink traffic unencrypted. ^{1 2}

  Open question · Minor · Open. On the record

Not yet demonstrated

R1 Network taps and certifiers

Mechanisms on the map that are not in the proposal. Pointers, not recommendations. ?

• R3 Deterministic and bit-exact inference Add
  • Bears on the open significant flaw “Output nondeterminism leaves covert capacity” in Network taps and certifiers. Deterministic replay is one of the two remedies the flaw's source names.
  • Network taps and certifiers waits on it. Nondeterministic inference leaves covert capacity in outputs that hashing cannot remove.
• R3 TEE remote attestation for AI workloads Add
  • Model identity attestation waits on it. The prototype needs porting to GPU confidential computing to handle larger models; the authors expect an overhead as small as 5 times there.
• R3 Sampled inference recomputation Add
  • Network taps and certifiers depends on it.
• R2 Bounding unexplained information in outputs Add

  Excluded by filters: readiness R2

  • Bears on the open significant flaw “Output nondeterminism leaves covert capacity” in Network taps and certifiers. Bounds the hidden information outputs can carry by measuring what the declared computation fails to predict.
• R1 Side-channel suppression for isolated facilities Add

  Excluded by filters: readiness R1

  • Bears on the open significant flaw “Completeness rests on physical monitoring left out of scope” in Network taps and certifiers. Addresses the radio, power-line and thermal channels that network-level designs leave out.
  • Network taps and certifiers waits on it. Radio, power-line and thermal channels are not addressed by network-level designs.
• R2 Tamper evidence for verifier devices Add

  Excluded by filters: readiness R2

  • Network taps and certifiers waits on it. Taps and gateway devices need tamper-evident housing and physical monitoring so that traffic cannot bypass them.

Missing prerequisites

• R3 TEE remote attestation for AI workloads needed by Model identity attestation Add
• R3 Sampled inference recomputation needed by Network taps and certifiers Add
• R3 Deterministic and bit-exact inference needed by Network taps and certifiers Add
• R2 Tamper evidence for verifier devices needed by Network taps and certifiers Add
• R1 Side-channel suppression for isolated facilities needed by Network taps and certifiers Add

Blockers

7 blockers recorded

• Model identity attestation
  • The prototype needs porting to GPU confidential computing to handle larger models; the authors expect an overhead as small as 5 times there. Performance & compatibility. Waits on TEE remote attestation for AI workloads ¹
  • As of September 2026 no code has been released for the prototype. Adversarial validation ¹
• Network taps and certifiers
  • No complete verification tap has been demonstrated at production frontend link rates, and on the tested CPU no hash algorithm reached line rate with minimum-size frames. Performance & compatibility ^{21 22}
  • Nondeterministic inference leaves covert capacity in outputs that hashing cannot remove. Evidence binding. Waits on Deterministic and bit-exact inference ¹³
  • Taps and gateway devices need tamper-evident housing and physical monitoring so that traffic cannot bypass them. Hardware trust. Waits on Tamper evidence for verifier devices ^{13 15}
  • Radio, power-line and thermal channels are not addressed by network-level designs. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities ¹³
  • Red-teaming by specialists is called for but has not been reported. Adversarial validation ¹³

From the family or selected implementation's record. ?

Model weights

Depends on the design for Network taps and certifiers.

Unspecified for Model identity attestation. Check the implementation record.

Inputs and outputs

Depends on the design for Network taps and certifiers.

Unspecified for Model identity attestation. Check the implementation record.

Training data

Depends on the design for Network taps and certifiers.

Unspecified for Model identity attestation. Check the implementation record.

Model identity attestation

• R2 Attestable Audits Research prototype, University of Cambridge
• R2 PAL*M Research prototype, University of Waterloo
• R3 Tinfoil model identity (Modelwrap) Product, Tinfoil

Network taps and certifiers

• R1 AI 2040 inference-only verification stack Proposed architecture, AI Futures Project
• R1 Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute
• R1 SASH confidential network logger Research prototype, Singapore AI Safety Hub (SASH)

1. Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments, C. Schnabl et al. (2025). Original
2. NVIDIA Secure AI with Blackwell and Hopper GPUs (White Paper), NVIDIA (2025). Original
3. TEE.fail: Breaking Trusted Execution Environments via DDR5 Memory Bus Interposition, J. Chuang et al. (2026). Original
4. Battering RAM: Low-Cost Interposer Attacks on Confidential Computing via Dynamic Memory Aliasing, J. De Meulemeester et al. (2026). Original
5. RMPocalypse: How a Catch-22 Breaks AMD SEV-SNP, B. Schlüter & S. Shinde (2025). Original
6. SEV-SNP RMP Initialization Vulnerability (AMD-SB-3020), AMD (2025). Original
7. A primer on secure enclaves, Tinfoil (2026). Original
8. PAL*M: Property Attestation for Large Generative Models, P. Chantasantitam et al. (2026). Original
9. How Tinfoil Proves Exactly What Model Is Running, Tinfoil Team (2026). Original
10. On TEEs for Privacy-Preserving Monitoring in AI Governance, Gloria Z (2026). Original
11. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). Original
12. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
13. Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors, N. Cankaya et al. (2026). Original
14. The Fundamentals and Feasibility of Secure Network Taps for Verifying AI Datacenter Use, N. Cankaya (2026). Original
15. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
16. Verification Plan, R. Dean (2026). Original
17. Fitting a Network TAP to our Inference Verification Prototype, Amodo Design (2026). Original
18. Amodo-Design/Inference-Recomputation-Prototype (GitHub repository), Amodo Design (2026). Original
19. inference-verification: Inference Verification Prototype, Singapore AI Safety Hub (SASH) (2026). Original
20. Internationalising AI Verification, Singapore AI Safety Hub (SASH) (2026). Original
21. Network Tapping for AI Verification: A Technical Assessment, Amodo Design (2026). Original
22. Network Traffic Hashing, Amodo Design (2026). Original