Proposal Explorer

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Built for an adversarial prover

Network taps and certifiers and Whole-workload recomputation (reproducible packets)

No new hardware needed

On-chip telemetry from timing, memory and performance counters

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.

• On-chip telemetry from timing, memory and performance counters: Red-teamed

Family findings

• Network taps and certifiers

  Context for AI 2040 inference-only verification stack. 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 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}

    Known failure · Theoretical argument · Significant · Open. On the record

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

    Related mechanism Research demo 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 failure 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 15}

    Scope limitation · Open question. 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

    Scope limitation · Open question. 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. ¹⁰

    Known failure · 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

    Scope limitation · Open question. On the record

    Related mechanism Proposed 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 failure is mitigated. Add

  • Verifier dictionary attacks on hashes in Network taps and certifiers

    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. ¹³

    Known failure · Theoretical argument · Minor · Mitigated. On the record

Open significant failures

3 failures in 2 mechanisms

• Adversarially disguised fine-tuning partly evades classification in On-chip telemetry from timing, memory and performance counters

  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. ³

  Known failure · Demonstrated attack · Significant · Open. On the record

• Counters leak information about protected workloads in On-chip telemetry from timing, memory and performance counters

  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. ^{4 5}

  Known failure · Theoretical argument · Significant · Open. On the record

• A recomputation family degrades against prompt-controlling adversaries in AI 2040 inference-only verification stack

  The plan's companion page names DiFR among the recomputation schemes being tested. An independent study found that Gumbel-based inference verification, the family that includes Token-DiFR, leaks roughly twice as many bits per token when the adversary chooses prompts that disrupt the text's structure, across six models of 1 to 32 billion parameters. The slowdown it imposes on a hidden-signalling adversary falls from 146–254 times under benign prompts to 60–118 times. The attack weakens the bound on hidden information in outputs, which the plan relies on to keep undeclared results from leaving. ^{6 7 11 12}

  Known failure · Demonstrated attack · Significant · Open. On the record

Scope limitations

• Software-read telemetry can be forged by the operator in On-chip telemetry from timing, memory and performance counters

  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. ^{1 3}

  Scope limitation · Theoretical argument. On the record

  Related mechanism Proposed Hardware-enabled guarantees (flexHEG) and guarantee processors: A guarantee processor on the chip would give the tamper-resistant, authenticated telemetry path the flaw says is missing. A pointer, not evidence that this failure is mitigated. Add

• Timing challenges do not identify the individual chip in On-chip telemetry from timing, memory and performance counters

  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. ¹

  Scope limitation · Theoretical argument. On the record

• The recomputation server must be trusted in AI 2040 inference-only verification stack

  The plan calls the integrity of the recomputation server an extremely important aspect, and its argument that sampling verifies all outputs assumes that the server's computations and outputs can be trusted. The companion page notes that the server sits inside the prover's facility, possibly under the prover's physical control, and that hardening it against integrity attacks needs significant research. Amodo rates recomputation-server security as not on track. ^{6 7 9}

  Scope limitation · Theoretical argument. On the record

• Spare compute is not verified in AI 2040 inference-only verification stack

  The plan states that it does not verify that spare compute is unused for unapproved workloads, because this seems very challenging. It relies instead on side-channel bounds and memory wipes, so that the only results that persist are verified inference outputs. ⁶

  Scope limitation · Theoretical argument. On the record

• Spare compute is outside the scheme in Whole-workload recomputation (reproducible packets)

  The plan states that it does not verify that spare compute is not used for unapproved workloads, because this seems very challenging. Recomputation checks the correctness of declared work, not its completeness. ^{6 16}

  Scope limitation · Theoretical argument. On the record

  Related mechanism Proposed Proofs of useful work for capacity accounting: Proposed as one input to accounting for spare capacity on declared hardware. A pointer, not evidence that this failure is mitigated. Add

Open questions

• No quantified error rates or formal thresholds for timing primitives in On-chip telemetry from timing, memory and performance counters

  Monfared et al. state that false-positive and false-negative rates are not quantified and leave hardware-specific formal thresholds to future work. ¹

  Open question · Open question. On the record

• Non-compliant work could be encoded inside compliant-looking packets in Whole-workload recomputation (reproducible packets)

  The plan notes that an AI company might try to encode a non-compliant workload inside a workload that looks compliant on the surface. ⁶

  Open question · Theoretical argument. On the record

Not yet demonstrated

Proposed Network taps and certifiers and Proposed Whole-workload recomputation (reproducible packets)

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

• Operational use Deterministic and bit-exact inference Add
  • Whole-workload recomputation (reproducible packets) waits on it. Workloads are not reproducible by default, and achieving reproducibility may cost performance.
• Research demo Tamper evidence for verifier devices Add
  • Network taps and certifiers waits on it. Checking that taps are correctly installed and stay in place at scale is not a solved problem, and hardening the recomputation server inside the prover's facility needs significant research.
• Proposed Hardware-enabled guarantees (flexHEG) and guarantee processors Add
  • On-chip telemetry from timing, memory and performance counters waits on it. Shipping accelerators need a tamper-resistant, authenticated telemetry path.
• Proposed Memory wiping and proofs of secure erasure Add
  • Network taps and certifiers waits on it. Memory wiping may use existing algorithms, but hardware testing is at an early stage.
• Proposed Side-channel suppression for isolated facilities Add
  • Network taps and certifiers waits on it. There is no plan yet for quickly scaling side-channel defences on a frontier cluster; only early theoretical pieces exist.
• Operational use Sampled inference recomputation Add
  • Network taps and certifiers depends on it.
• Operational use TEE remote attestation for AI workloads Add
  • On-chip telemetry from timing, memory and performance counters depends on it.

Missing prerequisites

• Operational use TEE remote attestation for AI workloads needed by On-chip telemetry from timing, memory and performance counters Add
• Operational use Sampled inference recomputation needed by Network taps and certifiers Add
• Operational use Deterministic and bit-exact inference needed by Whole-workload recomputation (reproducible packets) Add

Blockers

14 blockers recorded

• On-chip telemetry from timing, memory and performance counters
  • Shipping accelerators need a tamper-resistant, authenticated telemetry path. Hardware trust. Waits on Hardware-enabled guarantees (flexHEG) and guarantee processors ^{2 3}
  • NVIDIA's full confidential-computing mode disables the hardware performance counters its profiling tools use, so telemetry that needs them conflicts with it. Privacy & leakage ^{4 5}
  • Continuous challenge puzzles cost power and throughput on production workloads. Performance & compatibility ¹
  • Evaluation has not gone beyond single nodes, framework-level evasion and one vendor's hardware. Adversarial validation ³
• Network taps and certifiers
  • A fully reproducible inference stack needs substantial software and tooling, and per-packet network reproducibility may need considerable software, firmware and possibly hardware work. Performance & compatibility. Waits on Whole-workload recomputation (reproducible packets) ⁷
  • Passive optical taps work at 400G, but the 800G and 1600G line rates now arriving in data centres are undemonstrated. Performance & compatibility. Waits on Network taps and certifiers ⁷
  • Checking that taps are correctly installed and stay in place at scale is not a solved problem, and hardening the recomputation server inside the prover's facility needs significant research. Hardware trust. Waits on Tamper evidence for verifier devices ^{7 9}
  • There is no plan yet for quickly scaling side-channel defences on a frontier cluster; only early theoretical pieces exist. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities ⁷
  • Memory wiping may use existing algorithms, but hardware testing is at an early stage. Coverage & hidden compute. Waits on Memory wiping and proofs of secure erasure ⁷
  • Robust red-teaming of recomputation schemes has not started, and most algorithm development remains academic. Adversarial validation ^{7 9}
• Whole-workload recomputation (reproducible packets)
  • Workloads are not reproducible by default, and achieving reproducibility may cost performance. Performance & compatibility. Waits on Deterministic and bit-exact inference ⁶
  • Network packets are not individually reproducible by default; making them so may need considerable software, firmware and hardware work. Amodo rates this 'not on track'. Performance & compatibility ⁹
  • All traffic must reach the recomputation server via network taps, and the server's integrity is critical. Hardware trust. Waits on Network taps and certifiers ^{6 9}
  • Recomputing training steps needs checkpoints: writing one at every step would cost more than 100% overhead, so Amodo's design needs a spare data-parallel replica that tracks the weights instead. Performance & compatibility ¹⁶

From the family or selected implementation's record. ?

Model weights

Depends on the design for On-chip telemetry from timing, memory and performance counters and Whole-workload recomputation (reproducible packets).

Unspecified for Network taps and certifiers. Check the implementation record.

Inputs and outputs

Depends on the design for On-chip telemetry from timing, memory and performance counters and Whole-workload recomputation (reproducible packets).

Unspecified for Network taps and certifiers. Check the implementation record.

Training data

Depends on the design for On-chip telemetry from timing, memory and performance counters and Whole-workload recomputation (reproducible packets).

Unspecified for Network taps and certifiers. Check the implementation record.

On-chip telemetry from timing, memory and performance counters

None on the map

Network taps and certifiers

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

Whole-workload recomputation (reproducible packets)

• Proposed AI 2040 inference-only verification stack Proposed architecture, AI Futures Project

1. Timing and Memory Telemetry on GPUs for AI Governance, S. K. Monfared et al. (2026). Original
2. Guaranteeable Memory: An HBM-Based Chiplet for Verifiable AI Workloads, J. Petrie (2025). Original
3. Detecting Hidden ML Training With Zero-Overhead Telemetry, R. Rahman & S. Tajdari (2026). Original
4. On TEEs for Privacy-Preserving Monitoring in AI Governance, Gloria Z (2026). Original
5. NVIDIA Secure AI with Blackwell and Hopper GPUs (White Paper), NVIDIA (2025). Original
6. Verification Plan, R. Dean (2026). Original
7. Get Involved in Verification, AI Futures Project (2026). Original
8. Verifying international AI deals: Plan A, the state-of-play, and what you can do to help, T. Milton et al. (2026). Original
9. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). Original
10. Fitting a Network TAP to our Inference Verification Prototype, Amodo Design (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. Network Tapping for AI Verification: A Technical Assessment, Amodo Design (2026). Original
16. Example Schemes for Verifying High-Stakes AI Agreements, Amodo Design (2026). Original
17. Scaling Recomputation Inference Verification, Amodo Design (2026). Original
18. Proof-of-Learning is Currently More Broken Than You Think, C. Fang et al. (2023). Original