Proposal Explorer

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Excluded by your filters: Zero-knowledge proofs of inference 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.

• Zero-knowledge proofs of inference, EZKL: Independent red-team · excluded by filters
• Network taps and certifiers: Analysis · excluded by filters

Excluded by your filters

• Zero-knowledge proofs of inference Prover cooperation is required; the filter allows not required.
• Network taps and certifiers Prover cooperation is required; the filter allows not required.

Family findings

• Zero-knowledge proofs of inference

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

  • The proof covers a fixed-point approximation, not the floating-point model in Zero-knowledge proofs of inference

    Current ZK inference systems prove a quantised version of the network. zkLLM scales values by 2^16 and reports small perplexity changes. Attestable reports quantising matrix multiplications to 8-bit integers while proving other operations in floating point. A verifier therefore learns about the proof-friendly variant, and must separately accept that this variant is the declared model. Trail of Bits built a ResNet-18 backdoor that is dormant in the full-precision model and active after ezkl's quantisation; whether it persists through proving was left for further investigation. A verification system design calls floating-point emulation in ZKPs an open problem. ^{3 4 5 6}

    Open question · Significant · Open. On the record

  • A proof speaks only for the computations that were proven in Zero-knowledge proofs of inference

    Attestable writes that "a proof of some computation is not a proof of all computation", and that a proof cannot discover a datacenter that was never declared. Proofs of inference do not by themselves show that no other workload ran on the same or other hardware. ⁷

    Theoretical argument · Significant · Open. On the record

    Related mechanism R1 Proofs of useful work for capacity accounting: The record names proof-of-work accounting as the kind of compute accounting needed to show that proven inference was the only work done. A pointer, not evidence that this flaw is mitigated. Add

  • The model architecture is disclosed in Zero-knowledge proofs of inference

    ZKML "requires that the model architecture (but not weights) is revealed", and zkLLM assumes a publicly known model structure. Architecture can be commercially sensitive. ^{4 8}

    Theoretical argument · Minor · Open. On the record

  • Proofs do not bind computational effort (Hollow-LLM) in Zero-knowledge proofs of inference

    Researchers at the University of Southern California show that a proof of inference certifies that an output is consistent with committed weights under the declared architecture, but not how much computation produced it. In their Hollow-LLM attack, a provider keeps the declared architecture and parameter count but commits to "ghost weights". Some layers pass their inputs through unchanged, and wide layers carry the signal in a small subspace, so a much smaller inner model does the real work. The ghost weights satisfy the verification circuit and yield valid proofs.

    The authors ran the attack with the proof procedure of zkGPT, a separate ZK inference system, on a 6-layer, 512-dimensional transformer declared as up to 12 layers and 1,024 dimensions. Outputs were identical to the inner model's, and serving cost stayed at the inner model's level. An honest model of the declared size cost 2.4 times as much to prefill and 3.1 times as much to decode. Proving cost still grew with the declared architecture.

    The authors note that results may be served before any proof, with the provider building the witness only when a call is selected for audit. They describe their constructions as "compatible with state-of-the-art zkLLM pipelines", and state that the attack does not imply a flaw in the proof system itself. They propose challenge-based audits and ablation tests, which raise the cost of cheating but give no guarantee. ⁹

    Demonstrated attack · Significant · Open. On the record

Open significant flaws

6 flaws in 2 mechanisms

• Quantization can activate a backdoor dormant in the full-precision model in EZKL

  EZKL quantizes values to represent them in a finite field. Trail of Bits built a ResNet-18 whose backdoor is dormant at full precision and active after EZKL's quantization. Larger models and smaller quantization scales make the attack easier. Whether the backdoor persists through the witness and proof stages was left for further investigation. The fix was documentation of the risk. ³

  Demonstrated attack · 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. ^{10 17}

  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. ^{11 18}

  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. ¹¹

  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. ¹³

  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. ¹⁰

  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

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

  Excluded by filters: prover cooperation required

  • 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.
• R2 Bounding unexplained information in outputs Add

  Excluded by filters: prover cooperation required

  • 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: prover cooperation partial

  • 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: prover cooperation partial

  • Network taps and certifiers waits on it. Taps and gateway devices need tamper-evident housing and physical monitoring so that traffic cannot bypass them.
• R3 Sampled inference recomputation Add

  Excluded by filters: prover cooperation required

  • Network taps and certifiers depends on it.

Missing prerequisites

• 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

6 blockers recorded

• Zero-knowledge proofs of inference
  • Proving cost grows steeply with model size: a 250,000-parameter nanoGPT took 2,781 s to prove and needed a 219 GB proving key, which South et al. name as the main limit on model size. Performance & compatibility ²
• 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 ^{18 19}
  • Nondeterministic inference leaves covert capacity in outputs that hashing cannot remove. Evidence binding. Waits on Deterministic and bit-exact inference ¹⁰
  • 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 ^{6 10}
  • Radio, power-line and thermal channels are not addressed by network-level designs. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities ¹⁰
  • Red-teaming by specialists is called for but has not been reported. Adversarial validation ¹⁰

From the family or selected implementation's record. ?

Model weights

Depends on the design for Network taps and certifiers.

Unspecified for Zero-knowledge proofs of inference. Check the implementation record.

Inputs and outputs

Depends on the design for Network taps and certifiers.

Unspecified for Zero-knowledge proofs of inference. Check the implementation record.

Training data

Depends on the design for Network taps and certifiers.

Unspecified for Zero-knowledge proofs of inference. Check the implementation record.

Zero-knowledge proofs of inference

• R1 Attestable zero-knowledge inference prover Product, Attestable
• R2 EZKL Product, Zkonduit
• R1 Low-trust AI compute verification system overview Proposed architecture, Machine Intelligence Research Institute
• R2 zkLLM Research prototype, University of Waterloo

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. zkonduit/ezkl (GitHub repository), Zkonduit Inc. (2026). Original
2. Verifiable evaluations of machine learning models using zkSNARKs, T. South et al. (2024). Original
3. Zkonduit EZKL Security Assessment, F. Casal et al. (2025). Original
4. zkLLM: Zero Knowledge Proofs for Large Language Models, H. Sun et al. (2024). Original
5. Proving LLMs at Scale, Attestable (2026). Original
6. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
7. Pacing AI Requires Proof, Attestable (2026). Original
8. ZKML: An Optimizing System for ML Inference in Zero-Knowledge Proofs, B.-J. Chen et al. (2024). Original
9. Hollow-LLM Attack: Computationally Trivial Weights in Zero-Knowledge Verification of LLM Inference, C. Gong et al. (2026). Original
10. Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors, N. Cankaya et al. (2026). Original
11. The Fundamentals and Feasibility of Secure Network Taps for Verifying AI Datacenter Use, N. Cankaya (2026). Original
12. Verification Plan, R. Dean (2026). Original
13. Fitting a Network TAP to our Inference Verification Prototype, Amodo Design (2026). Original
14. Amodo-Design/Inference-Recomputation-Prototype (GitHub repository), Amodo Design (2026). Original
15. inference-verification: Inference Verification Prototype, Singapore AI Safety Hub (SASH) (2026). Original
16. Internationalising AI Verification, Singapore AI Safety Hub (SASH) (2026). Original
17. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
18. Network Tapping for AI Verification: A Technical Assessment, Amodo Design (2026). Original
19. Network Traffic Hashing, Amodo Design (2026). Original