Proposal Explorer

Add claims to see which ones the mechanisms address.

Built for an adversarial prover

Bounding unexplained information in outputs and Memory wiping and proofs of secure erasure

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.

• Bounding unexplained information in outputs: Independent red-team

Family findings

• Memory wiping and proofs of secure erasure

  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.

  • Memory the wipe cannot reach in Memory wiping and proofs of secure erasure

    Amodo's inventory of a GB200 system lists many memory stores beyond GPU HBM and host DRAM. It notes that SSD controller DRAM sits on a private bus that host commands cannot read or write, and that its optimized algorithm leaves 25 GiB of HBM unattested. It also asks how switch memory could be wiped. ^{11 12}

    Scope limitation · Open question. On the record

  • Outside help during challenges in Memory wiping and proofs of secure erasure

    Classic proofs of secure erasure assume the device is isolated during the protocol. Bursuc et al. relax this to a bound on how close a helper can be, enforced by round-trip times. In data centres, remote memory access has round trips of about 1–2 µs, against about 70–200 ns for local DRAM. The MIRI overview therefore says verification depends on ruling out RDMA by latency or physical disconnection. ^{5 13}

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

    Related mechanism Research demo Timed challenge-response and memory-occupation challenges: Timed challenges bound how far away a helper can be by how quickly it must answer. A pointer, not evidence that this failure is mitigated. Add

    Related mechanism Research demo Bandwidth limits and compartmentalization: Removing or capping links between groups of accelerators limits remote memory access during a challenge. A pointer, not evidence that this failure is mitigated. Add

  • Gap between erased and total memory in Memory wiping and proofs of secure erasure

    Bursuc et al. note that memory left between the erased region and the device's full memory could hold data, and that their bounds are tighter only against a restricted adversary. ¹³

    Scope limitation · Theoretical argument. On the record

Open significant failures

2 failures in 2 mechanisms

• Prompt-controlled entropy inflation widens the covert channel in Bounding unexplained information in outputs

  Gumbel-based inference verification tolerates token choices that honest GPU nondeterminism could produce, and the size of that tolerated set grows with the model's output entropy. Kezins, an independent researcher, showed that an adversary who controls the prompt distribution can raise output entropy and roughly double the bits leaked per token. Across six models of 1 to 32 billion parameters, this cut the slowdown from 146–254 times under benign prompts to 60–118 times. Kezins argues that architectures built on the same unexplained-information bound inherit this attack surface, and recommends calibrating tolerances against local token entropy rather than benign traffic. ^{2 3}

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

  Related mechanism Operational use Deterministic and bit-exact inference: Bit-exact replay would remove the tolerance for numerical noise that sets the size of this channel. The record notes that it needs full hardware and software metadata. A pointer, not evidence that this failure is mitigated. Add

• 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. ^{2 3 6 7}

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

Scope limitations

• Information the declared computation explains is not bounded in Bounding unexplained information in outputs

  The bound limits unexplained bits only. Outputs that the declared computation fully explains can still carry valuable information: a compression study notes that an adversary with inference access can extract more proprietary information per bit than naive transmission allows. ^{1 4}

  Scope limitation · Theoretical argument. On the record

• Channels other than checked outputs are outside the bound in Bounding unexplained information in outputs

  The inference-verification scheme treats side channels as out of scope. A low-trust system design argues that suppressing physical covert bandwidth below kilobits per second is much more achievable than aiming for zero, and that a malicious device can leak one bit of information by deliberately outputting a wrong result. ^{2 5}

  Scope limitation · Theoretical argument. On the record

  Related mechanism Proposed Side-channel suppression for isolated facilities: Physical side channels need separate suppression, which is this mechanism's purpose. A pointer, not evidence that this failure is mitigated. Add

• 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

Open questions

• The facility-level design is untested in Bounding unexplained information in outputs

  The compute-verification architecture is described with protocol details, potential attacks and prototyping plans, but no prototype results have been published. ¹

  Open question · Open question. On the record

Not yet demonstrated

Proposed Memory wiping and proofs of secure erasure

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

• Operational use Deterministic and bit-exact inference Add
  • Bears on the open significant failure “Prompt-controlled entropy inflation widens the covert channel” in Bounding unexplained information in outputs. Bit-exact replay would remove the tolerance for numerical noise that sets the size of this channel. The record notes that it needs full hardware and software metadata.
  • Bounding unexplained information in outputs waits on it. Tolerance for numerical nondeterminism sets the size of the residual channel; bit-exact replay would remove it but needs full hardware and software metadata.
• Research demo Bandwidth limits and compartmentalization Add
  • Bounding unexplained information in outputs waits on it. The prover's compute must be isolated so that all traffic passes through the verifier's interlock; any unmonitored path voids the bound.
• Research demo Tamper evidence for verifier devices Add
  • Memory wiping and proofs of secure erasure 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 Network taps and certifiers Add
  • Memory wiping and proofs of secure erasure waits on it. Passive optical taps work at 400G, but the 800G and 1600G line rates now arriving in data centres are undemonstrated.
  • Bounding unexplained information in outputs depends on it.
• Proposed Side-channel suppression for isolated facilities Add
  • Bounding unexplained information in outputs waits on it. Physical side channels need separate suppression, and one design treats a low residual bandwidth, rather than zero, as the realistic target.
  • Memory wiping and proofs of secure erasure waits on it. There is no plan yet for quickly scaling side-channel defences on a frontier cluster; only early theoretical pieces exist.
• Proposed Whole-workload recomputation (reproducible packets) Add
  • Memory wiping and proofs of secure erasure 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.
• Operational use Sampled inference recomputation Add
  • Bounding unexplained information in outputs depends on it.
  • Memory wiping and proofs of secure erasure depends on it.

Missing prerequisites

• Operational use Sampled inference recomputation needed by Bounding unexplained information in outputs and Memory wiping and proofs of secure erasure Add
• Research demo Bandwidth limits and compartmentalization needed by Bounding unexplained information in outputs Add
• Proposed Side-channel suppression for isolated facilities needed by Bounding unexplained information in outputs Add
• Proposed Network taps and certifiers needed by Bounding unexplained information in outputs Add
• Proposed Whole-workload recomputation (reproducible packets) needed by Memory wiping and proofs of secure erasure Add

Shared foundations

• Sampled inference recomputation relied on by Bounding unexplained information in outputs and Memory wiping and proofs of secure erasure
• Side-channel suppression for isolated facilities relied on by Bounding unexplained information in outputs and Memory wiping and proofs of secure erasure
• Network taps and certifiers relied on by Bounding unexplained information in outputs and Memory wiping and proofs of secure erasure

Blockers

11 blockers recorded

• Bounding unexplained information in outputs
  • The prover's compute must be isolated so that all traffic passes through the verifier's interlock; any unmonitored path voids the bound. Coverage & hidden compute. Waits on Bandwidth limits and compartmentalization ¹
  • Physical side channels need separate suppression, and one design treats a low residual bandwidth, rather than zero, as the realistic target. Coverage & hidden compute. Waits on Side-channel suppression for isolated facilities ^{2 5}
  • Tolerance for numerical nondeterminism sets the size of the residual channel; bit-exact replay would remove it but needs full hardware and software metadata. Protocol soundness. Waits on Deterministic and bit-exact inference ^{3 5}
  • Recomputation over confidential weights and inputs needs a protected setting: prover recomputation in a verifier-controlled enclosure, verifier recomputation in a prover-controlled enclosure, or zero-knowledge proofs. Privacy & leakage ¹
  • No prototype of the facility-level architecture exists to red-team. Adversarial validation ¹
• Memory wiping and proofs of secure erasure
  • 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}

From the family or selected implementation's record. ?

Model weights

Depends on the design for Bounding unexplained information in outputs.

Unspecified for Memory wiping and proofs of secure erasure. Check the implementation record.

Inputs and outputs

Depends on the design for Bounding unexplained information in outputs.

Unspecified for Memory wiping and proofs of secure erasure. Check the implementation record.

Training data

Not involved: Bounding unexplained information in outputs.

Unspecified for Memory wiping and proofs of secure erasure. Check the implementation record.

Bounding unexplained information in outputs

None on the map

Memory wiping and proofs of secure erasure

• 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

1. Verifying AI Compute by Bounding Unexplained Information Exfiltration, J. Petrie & Y. Mühlhäuser (2026). Original
2. Verifying LLM Inference to Detect Model Weight Exfiltration, R. Rinberg et al. (2025). Original
3. Adversarial Entropy Inflation Against Gumbel-Based Inference Verification, N. Kezins (2026). Original
4. Haiku to Opus in Just 10 bits: LLMs Unlock Large Compression Gains, R. Rinberg et al. (2026). Original
5. A System Overview for Near-Term, Low-Trust AI Compute Verification, N. Cankaya (2026). 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. Memory Wipes - Performance Analysis, Amodo Design (2026). Original
12. Improving Disk Wiping Speed for Memory Wipes, Amodo Design (2026). Original
13. Software-Based Memory Erasure with Relaxed Isolation Requirements, S. Bursuc et al. (2024). Original