Proposal Explorer

Add claims to see which ones the mechanisms address.

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Excluded by your filters: Bandwidth limits and compartmentalization 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.

• Network taps and certifiers: Analysis · excluded by filters

Excluded by your filters

• Bandwidth limits and compartmentalization Readiness R1 is below the minimum of R3.
• Network taps and certifiers Readiness R1 is below the minimum of R3.

Family findings

• Bandwidth limits and compartmentalization

  Context for AI 2040 inference-only verification stack. These findings concern the family or other implementations; applicability must be checked against their stated scope.

  • Low-communication training reduces the bandwidth training needs in Bandwidth limits and compartmentalization

    DiLoCo matched fully synchronous training on 8 workers while communicating 500 times less. Rahman writes that this family of methods theoretically allows large-scale training with less than 100 Mbps. Lucid includes these methods in its bounds, but notes that extreme activation compression, architectures with unusually small inter-layer widths, or modular paradigms could erode the margin. ^{8 9 10}

    Theoretical argument · Significant · Open. On the record

  • Operator control of pod routing collapses the bound in Bandwidth limits and compartmentalization

    Lucid's analysis finds that if the operator can freely assign pods to routers, it could dedicate a whole cell of 100 or more pods to one pipeline stage. The bound then falls to about 90–220x uncompressed and as low as about 25x with compression. The proposed mitigation, auditor-controlled random assignment that is periodically re-randomized, has not been implemented. ¹⁰

    Theoretical argument · Significant · Open. On the record

  • Undeclared local storage raises per-pod capacity in Bandwidth limits and compartmentalization

    More memory or storage per pod helps an adversary. Lucid requires per-pod storage to be declared, capped and physically inspected. ¹⁰

    Theoretical argument · Significant · Open. On the record

  • Training within one pod is not covered in Bandwidth limits and compartmentalization

    Lucid's bounds concern pre-training models larger than the pods are sized for. Training models that fit in one pod, fine-tuning and reinforcement-learning post-training within one pod are outside the modelled threat. ¹⁰

    Open question · Significant · Open. On the record

  • Parallel scale-up switches are hard enforcement points in Bandwidth limits and compartmentalization

    In GB200 topologies, GPUs reach GPUs in other nodes through NVSwitches without a NIC on the path. Amodo notes that limits are hard to enforce there because many switches work in parallel, so compromising one or two would bypass the limit. ¹¹

    Theoretical argument · Significant · Open. On the record

Open significant flaws

8 flaws in 2 mechanisms

• 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. ^{1 2 4}

  Theoretical argument · Significant · Open. 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. ¹

  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. ^{1 2 6 7}

  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. ^{7 12}

  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. ^{13 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. ¹³

  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. ⁵

  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. ¹²

  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 Bandwidth limits and compartmentalization and 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 Sampled inference recomputation Add
  • Bandwidth limits and compartmentalization depends on it.
  • 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.
  • Bandwidth limits and compartmentalization waits on it. There is no plan yet for quickly scaling side-channel defences on a frontier cluster; only early theoretical pieces exist.
  • 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

  • Bandwidth limits and compartmentalization 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.
  • Network taps and certifiers waits on it. Taps and gateway devices need tamper-evident housing and physical monitoring so that traffic cannot bypass them.
• R1 Memory wiping and proofs of secure erasure Add

  Excluded by filters: readiness R1

  • Bandwidth limits and compartmentalization waits on it. Memory wiping may use existing algorithms, but hardware testing is at an early stage.
• R1 Whole-workload recomputation (reproducible packets) Add

  Excluded by filters: readiness R1

  • Bandwidth limits and compartmentalization waits on it. A fully reproducible inference stack needs substantial software and tooling, and per-packet network reproducibility may need considerable software, firmware and possibly hardware work.

Missing prerequisites

• R3 Sampled inference recomputation needed by Bandwidth limits and compartmentalization and Network taps and certifiers Add
• R1 Whole-workload recomputation (reproducible packets) needed by Bandwidth limits and compartmentalization 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

Shared foundations

• Sampled inference recomputation relied on by Bandwidth limits and compartmentalization and Network taps and certifiers
• Tamper evidence for verifier devices relied on by Bandwidth limits and compartmentalization and Network taps and certifiers
• Side-channel suppression for isolated facilities relied on by Bandwidth limits and compartmentalization and Network taps and certifiers

Blockers

11 blockers recorded

• Bandwidth limits and compartmentalization
  • 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) ²
  • 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 ²
  • 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 ^{2 4}
  • 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 ²
  • 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 ²
  • Robust red-teaming of recomputation schemes has not started, and most algorithm development remains academic. Adversarial validation ^{2 4}
• 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 ¹²
  • 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 ^{12 14}
  • Radio, power-line and thermal channels are not addressed by network-level designs. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities ¹²
  • Red-teaming by specialists is called for but has not been reported. Adversarial validation ¹²

From the family or selected implementation's record. ?

Model weights

Depends on the design for Network taps and certifiers.

Unspecified for Bandwidth limits and compartmentalization. Check the implementation record.

Inputs and outputs

Depends on the design for Network taps and certifiers.

Unspecified for Bandwidth limits and compartmentalization. Check the implementation record.

Training data

Depends on the design for Network taps and certifiers.

Unspecified for Bandwidth limits and compartmentalization. Check the implementation record.

Bandwidth limits and compartmentalization

• R1 AI 2040 inference-only verification stack Proposed architecture, AI Futures Project
• R1 RAND secure inference data center (SIDC) design Proposed architecture, RAND

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. Verification Plan, R. Dean (2026). Original
2. Get Involved in Verification, AI Futures Project (2026). Original
3. Verifying international AI deals: Plan A, the state-of-play, and what you can do to help, T. Milton et al. (2026). Original
4. AI 2040 Plan A — Verification SITREP, Amodo Design (2026). Original
5. Fitting a Network TAP to our Inference Verification Prototype, Amodo Design (2026). Original
6. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). Original
7. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
8. DiLoCo: Distributed Low-Communication Training of Language Models, A. Douillard et al. (2024). Original
9. Does Distributed Training Undermine Compute Governance?, R. Rahman (2026). Original
10. Traffic Shaping for Workload Classification, Lucid Computing (2026). Original
11. The Tray as a Bandwidth Boundary, Amodo Design (2026). Original
12. Fingerprinting All AI Cluster I/O Without Mutually Trusted Processors, N. Cankaya et al. (2026). Original
13. The Fundamentals and Feasibility of Secure Network Taps for Verifying AI Datacenter Use, N. Cankaya (2026). Original
14. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). Original
15. Amodo-Design/Inference-Recomputation-Prototype (GitHub repository), Amodo Design (2026). Original
16. inference-verification: Inference Verification Prototype, Singapore AI Safety Hub (SASH) (2026). Original
17. Internationalising AI Verification, Singapore AI Safety Hub (SASH) (2026). Original
18. Network Tapping for AI Verification: A Technical Assessment, Amodo Design (2026). Original
19. Network Traffic Hashing, Amodo Design (2026). Original