# AI verification proposal

A proposal built with the Proposal Explorer of the AI Verification Tech Map (https://trustbutveri.fyi/), from its records of 2026-10-09. Interactive version: https://trustbutveri.fyi/explorer/?mechanisms=M-0013,M-0016&implementations=M-0013:I-0012

How to read it: a claim is something one party wants to verify about another's AI hardware or software. A mechanism is a general technique for verifying claims; it is "aimed at" a claim when that is its direct purpose, and "supporting" when it contributes without being aimed at it. A claim is addressed when a mechanism in the proposal is aimed at it and is not excluded by the filters; addressed does not mean verified, so check that mechanism's development status, security evidence and findings. Definitions: https://trustbutveri.fyi/about/methodology/ (roles, properties and findings) and https://trustbutveri.fyi/about/readiness/ (development status).

## Filters

Filters apply to mechanisms only and describe the setting the proposal is for.

None set. Every mechanism on the map was available.

## Overview

One row per mechanism, read from its record. Open failures: critical / significant / minor. The last three columns are the editors' reading of what the verifier sees. Findings are grouped as known failures, scope limitations and open questions. Only known failures count as failures. Counts are an inventory of published findings, not a risk score.

| Mechanism | Development | Security evidence | Prover | Attack testing | Hardware | Open failures | Weights | Inputs and outputs | Training data |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Network taps and certifiers / Low-trust AI compute verification system overview | Proposed | Published security analysis | Adversarial | Analysis | Retrofit device | 0 / 0 / 1 | unspecified | unspecified | unspecified |
| Timed challenge-response and memory-occupation challenges | Research demonstration | Published security analysis | Adversarial | Analysis | None | 0 / 1 / 0 | not involved | not involved | not involved |

## Claims

No claims chosen.

## Mechanisms

### Network taps and certifiers

A reference design that existing data centres could add: network taps hash all traffic, and air-gapped, independently sourced checkers later re-run randomly chosen records. ([Network taps and certifiers](https://trustbutveri.fyi/mechanisms/network-taps-and-certifiers/))

- Assessment: selected implementation [Low-trust AI compute verification system overview](https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/).
- Development: Proposed (legacy code R1), assessed for screening challenged records to show declared inference compute is not training.
- Security evidence: Published security analysis. Independent evaluation: unassessed. Formal proof: unassessed. Deployment assurance: unassessed.
- Claims in this proposal: none of them.
- Threat model: adversarial prover. Hardware: retrofit device. Prover cooperation: required. Attack testing: analysis. Category: Isolation & system architectures.
- What the verifier sees: model weights unspecified; inputs and outputs unspecified; training data unspecified. This Explorer has no asset-specific exposure assessment for this implementation. Check its source and deployment assumptions.

### Timed challenge-response and memory-occupation challenges

A verifier times answers to unpredictable questions designed so that answering correctly and in time requires holding specified data locally or dedicating specified resources. ([Timed challenge-response and memory-occupation challenges](https://trustbutveri.fyi/mechanisms/timed-challenge-response/))

- Assessment: mechanism family.
- Development: Research demonstration (legacy code R2), assessed for detecting whether a GPU is doing other work.
- Security evidence: Published security analysis. Independent evaluation: unassessed. Formal proof: unassessed. Deployment assurance: unassessed.
- Claims in this proposal: none of them.
- Threat model: adversarial prover. Hardware: none. Prover cooperation: required. Attack testing: analysis. Category: Cryptographic & computational.
- What the verifier sees: model weights not involved; inputs and outputs not involved; training data not involved. Uses verifier-chosen challenges; it does not handle model data.


## Properties

**Built for an adversarial prover**

- Network taps and certifiers
- Timed challenge-response and memory-occupation challenges

**No new hardware needed**

- Timed challenge-response and memory-occupation challenges


## Attack testing

Attack testing records published testing for this use. It does not by itself show independent review, a formal proof or that a deployed system is secure.

**Testing history**

- Network taps and certifiers / Low-trust AI compute verification system overview: Analysis
- Timed challenge-response and memory-occupation challenges: Analysis


## Limits

**Open significant failures**

- Remote memory narrows the timing margin (known failure, theoretical argument, in Timed challenge-response and memory-occupation challenges; https://trustbutveri.fyi/mechanisms/timed-challenge-response/evidence/flaws/2/) [1]. Data-centre remote memory access returns in about 1–2 µs, against about 70–200 ns for local DRAM. The MIRI overview says verification of memory saturation depends on ruling out remote access by latency or physical disconnection. It names pre-staging data into local memory as the remaining evasion and proposes an unpredictable, capacity-filling challenge to close it.

  Related mechanism: Bandwidth limits and compartmentalization (R2, not in the proposal). Physical disconnection is proposed to exclude remote memory between the separated groups during a challenge. It depends on the isolation boundary being enforced.

**Family finding context**

- Context for Low-trust AI compute verification system overview. Findings from the mechanism family appear here as context. They apply to an implementation only when its own record lists them, under the conditions stated there. Output nondeterminism leaves covert capacity (known failure, theoretical argument, in Network taps and certifiers; https://trustbutveri.fyi/mechanisms/network-taps-and-certifiers/evidence/flaws/1/) [2][3]. 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.

  Related mechanism: Deterministic and bit-exact inference (R3, not in the proposal). Deterministic replay is one of the two remedies the flaw's source names.

  Related mechanism: Bounding unexplained information in outputs (R2, not in the proposal). Bounds the hidden information outputs can carry by measuring what the declared computation fails to predict.
- Context for Low-trust AI compute verification system overview. Findings from the mechanism family appear here as context. They apply to an implementation only when its own record lists them, under the conditions stated there. Some links cannot be passively tapped (scope limitation, open question, in Network taps and certifiers; https://trustbutveri.fyi/mechanisms/network-taps-and-certifiers/evidence/flaws/2/) [4][5]. 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.
- Context for Low-trust AI compute verification system overview. Findings from the mechanism family appear here as context. They apply to an implementation only when its own record lists them, under the conditions stated there. Encrypted fabrics hide plaintext from both parties (scope limitation, open question, in Network taps and certifiers; https://trustbutveri.fyi/mechanisms/network-taps-and-certifiers/evidence/flaws/3/) [4]. 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.
- Context for Low-trust AI compute verification system overview. Findings from the mechanism family appear here as context. They apply to an implementation only when its own record lists them, under the conditions stated there. Residual side channels in simple passive setups (known failure, theoretical argument, in Network taps and certifiers; https://trustbutveri.fyi/mechanisms/network-taps-and-certifiers/evidence/flaws/4/) [6]. 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.
- Context for Low-trust AI compute verification system overview. Findings from the mechanism family appear here as context. They apply to an implementation only when its own record lists them, under the conditions stated there. Completeness rests on physical monitoring left out of scope (scope limitation, open question, in Network taps and certifiers; https://trustbutveri.fyi/mechanisms/network-taps-and-certifiers/evidence/flaws/5/) [2]. 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.

  Related mechanism: Side-channel suppression for isolated facilities (R1, not in the proposal). Addresses the radio, power-line and thermal channels that network-level designs leave out.
- Context for Low-trust AI compute verification system overview. Findings from the mechanism family appear here as context. They apply to an implementation only when its own record lists them, under the conditions stated there. Verifier dictionary attacks on hashes (known failure, theoretical argument, in Network taps and certifiers; https://trustbutveri.fyi/mechanisms/network-taps-and-certifiers/evidence/flaws/6/) [2]. Hashes of very short outputs could be brute-forced by the verifier. The paper recommends hashing at least 5 tokens together, or at least 10 if the attacker filters for likely tokens.

**Open minor failures**

- Deliberate faults leak a bit each (known failure, theoretical argument, in Low-trust AI compute verification system overview; https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/evidence/flaws/2/) [1]. A malicious device can leak one bit by deliberately outputting a wrong result, which blocks a disclosure when the cross-comparison fails. The design therefore needs a pre-agreed budget of tolerated faults.

**Scope limitations**

- Mismatches cannot be attributed to cheating or error (scope limitation, theoretical argument, in Low-trust AI compute verification system overview; https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/evidence/flaws/1/) [1]. A failed hash or replay does not show whether it came from an evasion attempt, a random bit flip or an evaluation error. The author notes that if detected anomalies can plausibly be waved off as malfunctions, deterrence becomes less effective, so the parties need an agreed escalation procedure that ends in attribution.

**Open questions**

- Inspector agents may be manipulable (open question, open question, in Low-trust AI compute verification system overview; https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/evidence/flaws/3/) [1]. Automated compliance screening with LLM-based inspector agents must resist prompt-injection attacks. Adversarially trained systems might hide malicious actions with steganography, which makes backdoor detection an open problem.
- Error rates not quantified (open question, open question, in Timed challenge-response and memory-occupation challenges; https://trustbutveri.fyi/mechanisms/timed-challenge-response/evidence/flaws/3/) [8]. Monfared et al. show separable timing distributions. Their acceptance rule passes a GPU when its mean time per round stays at or below a chosen maximum, and an appendix outlines statistical tests for the proof-of-work puzzle. They leave hardware-specific threshold values to future work and report no false-positive or false-negative rates.

**Not yet demonstrated**

- Network taps and certifiers: Proposed (legacy code R1), assessed for screening challenged records to show declared inference compute is not training


## Possible additions

Mechanisms on the map, not in the proposal, that the records connect to an unaddressed or partly addressed claim, an open failure or a dependency. Pointers, not recommendations: each brings its own readiness level and findings, and none is claimed to close a failure.

- **Bandwidth limits and compartmentalization** (Research demonstration (legacy code R2), assessed for monitoring inter-node traffic with operator-run software on four GPUs)
  - Bears on the open significant failure "Remote memory narrows the timing margin" in Timed challenge-response and memory-occupation challenges. Physical disconnection is proposed to exclude remote memory between the separated groups during a challenge. It depends on the isolation boundary being enforced.
  - Timed challenge-response and memory-occupation challenges waits on it: Outside help, such as remote memory, must be excluded during challenges.
- **Deterministic and bit-exact inference** (Operational use (legacy code R3), assessed for reproducing open-model inference from receipts in Gensyn's information-market service)
  - Network taps and certifiers waits on it: Exact replay needs complete hardware and software metadata, and the tolerable slowdown from emulation is an open question.
- **Tamper evidence for verifier devices** (Research demonstration (legacy code R2), assessed for detecting probing of proposed verifier hardware, using server and electronics prototypes as evidence)
  - Network taps and certifiers waits on it: Tamper-evident, rapidly mass-manufacturable and retrofittable enclosures for side-channel defence are an open research question, and physical security against covert communication in every monitored data centre is challenging.
- **Side-channel suppression for isolated facilities** (Proposed (legacy code R1), assessed for bounding physical covert channels out of a verified enclosure)
  - Network taps and certifiers waits on it: A mass-manufacturable, good-enough side-channel defence, particularly power-line filtering, has not been constructed or red-teamed.


## Dependencies

**Missing prerequisites**

- Deterministic and bit-exact inference (Operational use (legacy code R3), assessed for reproducing open-model inference from receipts in Gensyn's information-market service), needed by Network taps and certifiers

**Blockers**

- Network taps and certifiers: Empirical feasibility of passive optical splitting at 53–112 GBaud under realistic conditions is an open question. (performance & compatibility; waits on Network taps and certifiers) [1]
- Network taps and certifiers: Exact replay needs complete hardware and software metadata, and the tolerable slowdown from emulation is an open question. (performance & compatibility; waits on Deterministic and bit-exact inference) [1]
- Network taps and certifiers: Tamper-evident, rapidly mass-manufacturable and retrofittable enclosures for side-channel defence are an open research question, and physical security against covert communication in every monitored data centre is challenging. (hardware trust; waits on Tamper evidence for verifier devices) [1]
- Network taps and certifiers: A mass-manufacturable, good-enough side-channel defence, particularly power-line filtering, has not been constructed or red-teamed. (coverage & hidden compute; waits on Side-channel suppression for isolated facilities) [1]
- Network taps and certifiers: Distinguishing one server's DRAM contents from another's by challenge-response timing, and a general challenge-response protocol for diverse data types, are open. (coverage & hidden compute; waits on Timed challenge-response and memory-occupation challenges) [1]
- Network taps and certifiers: The threat model is under-developed and needs input from cybersecurity and AI threat-modelling experts. (adversarial validation) [1]
- Timed challenge-response and memory-occupation challenges: No network-level memory challenge across data-centre servers has been demonstrated. (adversarial validation) [1]
- Timed challenge-response and memory-occupation challenges: Challenges that fill memory displace workloads; filling a pod's volatile memory takes tens of minutes and SSDs take hours. (performance & compatibility) [1][8]
- Timed challenge-response and memory-occupation challenges: Outside help, such as remote memory, must be excluded during challenges. (coverage & hidden compute; waits on Bandwidth limits and compartmentalization) [1]


## What the verifier sees

- Model weights: shown by none; depends on the design for none; hidden by none; not involved in Timed challenge-response and memory-occupation challenges; unspecified for Network taps and certifiers.
- Inputs and outputs: shown by none; depends on the design for none; hidden by none; not involved in Timed challenge-response and memory-occupation challenges; unspecified for Network taps and certifiers.
- Training data: shown by none; depends on the design for none; hidden by none; not involved in Timed challenge-response and memory-occupation challenges; unspecified for Network taps and certifiers.

## Implementations

- Network taps and certifiers: [AI 2040 inference-only verification stack](https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/) (R1, proposed architecture); [Low-trust AI compute verification system overview](https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/) (R1, proposed architecture); [SASH confidential network logger](https://trustbutveri.fyi/implementations/sash-confidential-network-logger/) (R1, research prototype)
- Timed challenge-response and memory-occupation challenges: [Data-centre memory challenging](https://trustbutveri.fyi/implementations/data-centre-memory-challenging/) (R1, proposed architecture); [GPU contention probes](https://trustbutveri.fyi/implementations/gpu-contention-probes/) (R2, research prototype); [Low-trust AI compute verification system overview](https://trustbutveri.fyi/implementations/low-trust-compute-verification-system-overview/) (R1, proposed architecture); [SAGE](https://trustbutveri.fyi/implementations/sage-gpu-attestation/) (R2, research prototype); [VRAM-residency challenge](https://trustbutveri.fyi/implementations/vram-residency-challenge/) (R2, research prototype)

## Sources

1. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). https://intelligence.org/wp-content/uploads/2026/06/A-system-overview-for-near-term-low-trust-AI-compute-verification.pdf
2. Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors, N. Cankaya et al. (2026). https://arxiv.org/abs/2606.10724
3. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). https://arxiv.org/abs/2608.23375
4. The Fundamentals and Feasibility of Secure Network Taps for Verifying AI Datacenter Use, N. Cankaya (2026). https://nacicankaya.substack.com/p/research-note-the-fundamentals-and
5. Network Tapping for AI Verification: A Technical Assessment, Amodo Design (2026). https://amododesign.com/notes/2026-05-03-network-tapping/
6. Fitting a Network TAP to our Inference Verification Prototype, Amodo Design (2026). https://amododesign.com/notes/2026-09-15-network-tap-inference-verification/
7. Verification Plan, R. Dean (2026). https://ai-2040.com/supplements/verification-plan
8. Timing and Memory Telemetry on GPUs for AI Governance, S. K. Monfared et al. (2026). https://arxiv.org/abs/2602.09369
9. SAGE: Software-based Attestation for GPU Execution, A. Ivanov et al. (2023). https://www.usenix.org/conference/atc23/presentation/ivanov
10. SWATT: SoftWare-based ATTestation for Embedded Devices, A. Seshadri et al. (2004). https://netsec.ethz.ch/publications/papers/swatt.pdf
11. Proofs of Space, S. Dziembowski et al. (2015). https://eprint.iacr.org/2013/796
12. Secure Code Update for Embedded Devices via Proofs of Secure Erasure, D. Perito & G. Tsudik (2010). https://link.springer.com/chapter/10.1007/978-3-642-15497-3_39
13. Software-Based Memory Erasure with Relaxed Isolation Requirements, S. Bursuc et al. (2024). https://ieeexplore.ieee.org/document/10664348/
14. On the Difficulty of Software-Based Attestation of Embedded Devices, C. Castelluccia et al. (2009). https://s3.eurecom.fr/docs/ccs09_Castelluccia.pdf
15. Refutation of "On the Difficulty of Software-Based Attestation of Embedded Devices", A. Perrig & L. van Doorn (2010). https://netsec.ethz.ch/publications/papers/perrig-ccs-refutation.pdf