# 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-0022,M-0014&implementations=M-0014:I-0010

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 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Side-channel suppression for isolated facilities | Proposed | Published security analysis | Adversarial | Analysis | Retrofit device | 0 / 0 / 0 | not involved | not involved | not involved |
| Bandwidth limits and compartmentalization / RAND secure inference data center (SIDC) design | Proposed | Published security analysis | Semi-trusted | Analysis | Retrofit device | 0 / 0 / 0 | unspecified | unspecified | unspecified |

## Claims

No claims chosen.

## Mechanisms

### Side-channel suppression for isolated facilities

Shielding, filtering, jamming and inspecting an AI facility to limit hidden physical communication around monitored network links. ([Side-channel suppression for isolated facilities](https://trustbutveri.fyi/mechanisms/side-channel-suppression/))

- Assessment: mechanism family.
- Development: Proposed (legacy code R1), assessed for bounding physical covert channels out of a verified enclosure.
- 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: partial. Attack testing: analysis. Category: Off-chip devices & sensors.
- What the verifier sees: model weights not involved; inputs and outputs not involved; training data not involved. Shields and filters a facility; it does not handle model data.

### Bandwidth limits and compartmentalization

A RAND design for a purpose-built facility that serves already-trained AI models while protecting weights and inference data against state-level attackers. ([Bandwidth limits and compartmentalization](https://trustbutveri.fyi/mechanisms/bandwidth-limits-and-compartmentalization/))

- Assessment: selected implementation [RAND secure inference data center (SIDC) design](https://trustbutveri.fyi/implementations/rand-secure-inference-data-centers/).
- Development: Proposed (legacy code R1), assessed for the operator's own weight security, with no outside verification described.
- Security evidence: Published security analysis. Independent evaluation: unassessed. Formal proof: unassessed. Deployment assurance: unassessed.
- Claims in this proposal: none of them.
- Threat model: semi-trusted 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.


## Properties

**Built for an adversarial prover**

- Side-channel suppression for isolated facilities


## 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**

- Side-channel suppression for isolated facilities: Analysis
- Bandwidth limits and compartmentalization / RAND secure inference data center (SIDC) design: Analysis


## Limits

**Family finding context**

- Context for RAND secure inference data center (SIDC) design. 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. Low-communication training reduces the bandwidth training needs (open question, theoretical argument, in Bandwidth limits and compartmentalization; https://trustbutveri.fyi/mechanisms/bandwidth-limits-and-compartmentalization/evidence/flaws/1/) [3][4][5]. 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.
- Context for RAND secure inference data center (SIDC) design. 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. Operator control of pod routing collapses the bound (known failure, theoretical argument, in Bandwidth limits and compartmentalization; https://trustbutveri.fyi/mechanisms/bandwidth-limits-and-compartmentalization/evidence/flaws/2/) [5]. 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.
- Context for RAND secure inference data center (SIDC) design. 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. Undeclared local storage raises per-pod capacity (scope limitation, theoretical argument, in Bandwidth limits and compartmentalization; https://trustbutveri.fyi/mechanisms/bandwidth-limits-and-compartmentalization/evidence/flaws/3/) [5]. More memory or storage per pod helps an adversary. Lucid requires per-pod storage to be declared, capped and physically inspected.
- Context for RAND secure inference data center (SIDC) design. 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. Training within one pod is not covered (scope limitation, open question, in Bandwidth limits and compartmentalization; https://trustbutveri.fyi/mechanisms/bandwidth-limits-and-compartmentalization/evidence/flaws/4/) [5]. 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.
- Context for RAND secure inference data center (SIDC) design. 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. Parallel scale-up switches are hard enforcement points (known failure, theoretical argument, in Bandwidth limits and compartmentalization; https://trustbutveri.fyi/mechanisms/bandwidth-limits-and-compartmentalization/evidence/flaws/5/) [6]. 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.

**Scope limitations**

- Openings for airflow, power and optics weaken shielding (scope limitation, theoretical argument, in Side-channel suppression for isolated facilities; https://trustbutveri.fyi/mechanisms/side-channel-suppression/evidence/flaws/2/) [1]. Cankaya notes that keeping attenuation high while passing high-power airflow, cabling and optical links adds complexity beyond existing shielded-enclosure specifications.
- Everything rests on the trusted setup (scope limitation, theoretical argument, in RAND secure inference data center (SIDC) design; https://trustbutveri.fyi/implementations/rand-secure-inference-data-centers/evidence/flaws/1/) [2]. Reference measurements for model weights and reference data are established in a trusted setup phase. The report states that the system cannot detect compromise that happened before ingestion if the trusted setup itself is compromised.
- Security weakens over long operation (scope limitation, theoretical argument, in RAND secure inference data center (SIDC) design; https://trustbutveri.fyi/implementations/rand-secure-inference-data-centers/evidence/flaws/2/) [2]. The authors claim that the facility can withstand attacks at the OC5 level for a five-year operational period. They expect its ability to withstand long OC5 campaigns to become less robust the longer the facility remains in operation.

**Open questions**

- Supply-chain implants may evade inspection (open question, theoretical argument, in Side-channel suppression for isolated facilities; https://trustbutveri.fyi/mechanisms/side-channel-suppression/evidence/flaws/1/) [1]. Cankaya identifies malicious hardware embedded deep in purchased components as a residual risk that visual inspection and disassembly may not catch. He notes that radiographic examination under high-security standards could mitigate it.
- Inspection assumptions may not hold (open question, open question, in Side-channel suppression for isolated facilities; https://trustbutveri.fyi/mechanisms/side-channel-suppression/evidence/flaws/3/) [1]. The design's statistical argument assumes that visual or disassembly inspection catches every flaw that is present in a sampled unit. Cankaya is unsure whether destructive teardowns are defence-dominant or offence-dominant.

**Not yet demonstrated**

- Side-channel suppression for isolated facilities: Proposed (legacy code R1), assessed for bounding physical covert channels out of a verified enclosure
- Bandwidth limits and compartmentalization: Proposed (legacy code R1), assessed for the operator's own weight security, with no outside verification described


## 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.

None found.



## Dependencies

**Blockers**

- Side-channel suppression for isolated facilities: No prototype or red-team exists; the design is a first-pass viability study. (adversarial validation) [1]
- Side-channel suppression for isolated facilities: Volume costs of TEMPEST-grade power-line filters are uncertain, because existing products are mostly made to order. (performance & compatibility) [1]
- Bandwidth limits and compartmentalization: No prototype exists; RAND recommends prototyping key security features and integration now. (adversarial validation) [2]
- Bandwidth limits and compartmentalization: The report describes internal integrity checks, audit logging and accreditation, but no way for a party outside the operator to verify the facility's properties. (access & governance) [2]
- Bandwidth limits and compartmentalization: Human review of every prompt and response makes each request take three to five minutes, with the review steps as the rate-limiting factor. (performance & compatibility) [2]
- Bandwidth limits and compartmentalization: Detailed design information is withheld from the public report and is to be evaluated privately with stakeholders, which limits independent public scrutiny. (access & governance) [2]


## What the verifier sees

- Model weights: shown by none; depends on the design for none; hidden by none; not involved in Side-channel suppression for isolated facilities; unspecified for Bandwidth limits and compartmentalization.
- Inputs and outputs: shown by none; depends on the design for none; hidden by none; not involved in Side-channel suppression for isolated facilities; unspecified for Bandwidth limits and compartmentalization.
- Training data: shown by none; depends on the design for none; hidden by none; not involved in Side-channel suppression for isolated facilities; unspecified for Bandwidth limits and compartmentalization.

## Implementations

- Side-channel suppression for isolated facilities: [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); [RAND secure inference data center (SIDC) design](https://trustbutveri.fyi/implementations/rand-secure-inference-data-centers/) (R1, proposed architecture)
- Bandwidth limits and compartmentalization: [AI 2040 inference-only verification stack](https://trustbutveri.fyi/implementations/ai-2040-inference-only-verification-plan/) (R1, proposed architecture); [RAND secure inference data center (SIDC) design](https://trustbutveri.fyi/implementations/rand-secure-inference-data-centers/) (R1, proposed architecture)

## Sources

1. Suppressing Side Channels in an Untrusted Data Center via Retrofitted Defenses, N. Cankaya (2026). https://techgov.intelligence.org/blog/suppressing-side-channels-in-an-untrusted-data-center-via-retrofitted-defenses
2. Highly Secure Inference Data Centers: A Vertically Integrated Strategy for Security Engineering, S. F. Comer et al. (2026). https://www.rand.org/pubs/research_reports/RRA4827-1.html
3. DiLoCo: Distributed Low-Communication Training of Language Models, A. Douillard et al. (2024). https://arxiv.org/abs/2311.08105
4. Does Distributed Training Undermine Compute Governance?, R. Rahman (2026). https://arxiv.org/abs/2605.29359
5. Traffic Shaping for Workload Classification, Lucid Computing (2026). https://lucidcomputing.substack.com/p/traffic-shaping-for-workload-classification
6. The Tray as a Bandwidth Boundary, Amodo Design (2026). https://amododesign.com/notes/2026-03-16-dpu-bandwidth-limiter/