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Verification mechanisms and implementations.
| Name | Development | Security evidence | Type | Category | Threat model | Hardware |
|---|---|---|---|---|---|---|
| Apple Private Cloud Compute Apple's cloud AI inference service, in which user devices send requests only to servers that attest to running software published in a public transparency log. | Operational useOperational use for showing users which software serves their AI requests, not which model. Medium confidence. Implementation · On-chip · Semi-trusted prover | Published attack testing · independent | Implementation | On-chip | Semi-trusted prover | Existing features |
| Deterministic and bit-exact inference Making model inference reproducible bit for bit, so that a verifier's re-run must match the provider's output exactly rather than approximately. | Operational useOperational use for reproducing open-model inference from receipts in Gensyn's information-market service. Low confidence. Mechanism · Crypto / compute · Adversarial prover | Published analysis or argument | Mechanism | Crypto / compute | Adversarial prover | None |
| Hardware-attested weight binding Checks that a hardware enclave serves committed model weights, by attesting software that tests the weights against a hash commitment when they are read. | Operational useOperational use for showing users, through hardware attestation, that a service runs its committed weights. Medium confidence. Mechanism · Crypto / compute · Semi-trusted prover | Published attack testing · independent | Mechanism | Crypto / compute | Semi-trusted prover | Existing features |
| Pearl proof-of-useful-work blockchain A blockchain whose mining is designed to be a by-product of GPU matrix multiplications in AI workloads, with public node and miner code. | Operational useOperational use for checking matrix-multiplication work proofs for blockchain consensus. Low confidence. Implementation · Crypto / compute · Adversarial prover | Published analysis or argument | Implementation | Crypto / compute | Adversarial prover | None |
| Sampled inference recomputation A verifier re-runs a random sample of an AI provider's logged queries on a trusted copy of the declared model and checks the outputs match. | Operational useOperational use for checking untrusted workers' activations against the declared model, prompt and precision. Low confidence. Mechanism · Crypto / compute · Adversarial prover | Published analysis or argument | Mechanism | Crypto / compute | Adversarial prover | None |
| TEE remote attestation for AI workloads Trusted execution environments (TEEs) in CPUs and GPUs sign reports of launched software, so remote verifiers can check which code an AI workload started with. | Operational useOperational use for showing a verifier which software ran despite distrust of the hardware operator. Medium confidence. Mechanism · On-chip · Semi-trusted prover | Published attack testing · independent | Mechanism | On-chip | Semi-trusted prover | Existing features |
| Tinfoil model identity (Modelwrap) Tinfoil's open-source tool for proving which model weights its enclave-hosted inference service runs, by building a hash of the weights into the hardware attestation. | Operational useOperational use for showing clients that the served weights match a committed hash. Medium confidence. Implementation · Crypto / compute · Semi-trusted prover | Published analysis or argument | Implementation | Crypto / compute | Semi-trusted prover | Existing features |
| TOPLOC TOPLOC is a hashing scheme from Prime Intellect that lets a verifier check whether an inference provider ran the model, prompt and precision it claims. | Operational useOperational use for checking that untrusted providers used the claimed model, prompt and precision. Low confidence. Implementation · Crypto / compute · Adversarial prover | Published analysis or argument | Implementation | Crypto / compute | Adversarial prover | None |
| Verde and RepOps (Gensyn) Gensyn's system for checking machine-learning jobs given to untrusted providers, which settles disputes by re-running one operation with operators Gensyn reports as bit-identical across hardware. | Operational useOperational use for reproducing declared-model inference from receipts in Gensyn's information-market service. Low confidence. Implementation · Crypto / compute · Adversarial prover | Published analysis or argument | Implementation | Crypto / compute | Adversarial prover | None |
| WhatsApp Private Processing Meta's server system for WhatsApp AI features, whose clients check a confidential virtual machine's attestation and transparency-log proofs before connecting. | Operational useOperational use for showing WhatsApp clients which logged software and artifacts serve their AI requests. Medium confidence. Implementation · On-chip · Semi-trusted prover | Published analysis or argument | Implementation | On-chip | Semi-trusted prover | Existing features |
| Attestable Audits A research prototype that runs AI safety benchmarks inside a trusted execution environment (TEE) and publishes TEE-signed records linking the model, audit and results. | Research demonstrationResearch demonstration for attesting a benchmark audit run on a confidential model in an enclave. Low confidence. Implementation · On-chip · Semi-trusted prover | Published analysis or argument | Implementation | On-chip | Semi-trusted prover | Existing features |
| Bandwidth limits and compartmentalization Capping or removing network links between groups of AI chips, so each can serve models but pre-training beyond the current frontier becomes far slower. | Research demonstrationResearch demonstration for monitoring inter-node traffic with operator-run software on four GPUs. Low confidence. Mechanism · Isolation & architecture · Adversarial prover | Published analysis or argument | Mechanism | Isolation & architecture | Adversarial prover | Retrofit device |
| Batch-invariant inference kernels (Thinking Machines) Open-source GPU kernels from Thinking Machines Lab that make language-model outputs independent of batch size, adopted in vLLM and SGLang for reproducible inference. | Research demonstrationResearch demonstration for exact recomputation of served outputs by a verifier, with a cooperating provider. Low confidence. Implementation · Crypto / compute · Cooperative prover | No published adversarial analysis recorded | Implementation | Crypto / compute | Cooperative prover | None |
| Bounding unexplained information in outputs Limits the hidden information a facility's outputs can carry by measuring how much of those outputs the declared computation fails to predict. | Research demonstrationResearch demonstration for bounding how much hidden information can leave in checked inference outputs. Low confidence. Mechanism · Isolation & architecture · Adversarial prover | Published attack testing · independent | Mechanism | Isolation & architecture | Adversarial prover | Retrofit device |
| Confidential multi-party verification Parties who distrust each other run an agreed check on private models or records, using attested enclaves or zero-knowledge proofs, releasing only the result. | Research demonstrationResearch demonstration for audits or evaluations of a private model that reveal neither party's inputs. Medium confidence. Mechanism · Crypto / compute · Semi-trusted prover | Published analysis or argument | Mechanism | Crypto / compute | Semi-trusted prover | Existing features |
| Cove Draft Parties who distrust each other chain enclave-run stages; owners release keys to private data only to stages they approved, and each stage issues a certificate. | Research demonstrationResearch demonstration for composing owner-approved confidential workflow stages on Intel TDX. Medium confidence. Implementation · Crypto / compute · Semi-trusted prover | Published analysis or argument | Implementation | Crypto / compute | Semi-trusted prover | Existing features |
| DiFR (Divergence From Reference) DiFR checks that an inference provider ran its declared model by comparing output tokens or activations with a trusted re-run using the same random seed. | Research demonstrationResearch demonstration for checking that outputs match the declared model, precision and sampling settings. Medium confidence. Implementation · Crypto / compute · Adversarial prover | Published analysis or argument | Implementation | Crypto / compute | Adversarial prover | None |
| EZKL EZKL is a library from Zkonduit that turns neural networks into zero-knowledge circuits, so a prover can show an output came from a committed model. | Research demonstrationResearch demonstration for proving a language model's output follows from committed weights, against a cheating prover. Medium confidence. Implementation · Crypto / compute · Adversarial prover | Published attack testing · independent | Implementation | Crypto / compute | Adversarial prover | None |
| GPU contention probes Draft Three timed GPU puzzles whose solve times lengthen when another workload shares the device, a heuristic sign to a verifier that the GPU is shared. | Research demonstrationResearch demonstration for detecting another workload running on the same GPU. Medium confidence. Implementation · Crypto / compute · Adversarial prover | Published analysis or argument | Implementation | Crypto / compute | Adversarial prover | None |
| Kaizen Draft A trainer proves in zero knowledge that a public training algorithm on committed data produced the model; the proof does not grow with training length. | Research demonstrationResearch demonstration for proving gradient-descent training of small image models on committed data. Medium confidence. Implementation · Crypto / compute · Adversarial prover | Published analysis or argument | Implementation | Crypto / compute | Adversarial prover | None |
| On-chip telemetry from timing, memory and performance counters On-chip measurements, such as task timings, whether data sits in chip memory, and performance counters, used as statistical evidence of what AI chips are running. | Research demonstrationResearch demonstration for workload evidence from GPU counters and timing, assuming authentic measurements. Medium confidence. Mechanism · On-chip · Semi-trusted prover | Published attack testing · developers | Mechanism | On-chip | Semi-trusted prover | Existing features |
| PAL*M Draft A research prototype in which confidential-computing hardware signs hashes of the models, datasets and results involved in training, evaluation and inference. | Research demonstrationResearch demonstration for attesting declared model operations on a confidential CPU–GPU prototype. Medium confidence. Implementation · On-chip · Semi-trusted prover | Published analysis or argument | Implementation | On-chip | Semi-trusted prover | Existing features |
| PySyft double-blind evaluations OpenMined's PySyft lets a model owner and an evaluator run tests in an attested enclave without showing each other the weights or the private prompts. | Research demonstrationResearch demonstration for evaluating a private model on private prompts, neither party seeing the other's inputs. Medium confidence. Implementation · Crypto / compute · Semi-trusted prover | Published analysis or argument | Implementation | Crypto / compute | Semi-trusted prover | Existing features |
| Safeguard attestation TEE-signed evidence that an AI service sent a given response through its declared safeguard path, such as a wrapper that calls a guardrail classifier. | Research demonstrationResearch demonstration for attesting that a declared safeguard mediated a service's responses. Low confidence. Mechanism · Crypto / compute · Semi-trusted prover | Published analysis or argument | Mechanism | Crypto / compute | Semi-trusted prover | Existing features |
| SAGE Draft A software-only attestation scheme in which a GPU must compute a checksum over its own code within a time limit, without trusted GPU hardware. | Research demonstrationResearch demonstration for attesting code execution on a GPU that lacks hardware trusted-execution support. Medium confidence. Implementation · Crypto / compute · Adversarial prover | Published analysis or argument | Implementation | Crypto / compute | Adversarial prover | Existing features |
| Tamper evidence for verifier devices Enclosures, seals and sensors that make physical interference with verification hardware visible, or that destroy the hardware's secrets when tampering occurs. | Research demonstrationResearch demonstration for detecting probing of proposed verifier hardware, using server and electronics prototypes as evidence. Medium confidence. Mechanism · Off-chip devices · Adversarial prover | Published analysis or argument | Mechanism | Off-chip devices | Adversarial prover | Retrofit device |
| 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. | Research demonstrationResearch demonstration for detecting whether a GPU is doing other work. Medium confidence. Mechanism · Crypto / compute · Adversarial prover | Published analysis or argument | Mechanism | Crypto / compute | Adversarial prover | None |
| Training-transcript verification (proof-of-learning) A trainer logs checkpoints, data order and settings, so a verifier can re-run sampled training segments and check that the claimed training happened. | Research demonstrationResearch demonstration for checking from its transcript that a training run followed declared rules. Medium confidence. Mechanism · Crypto / compute · Adversarial prover | Published attack testing · independent | Mechanism | Crypto / compute | Adversarial prover | None |
| VeriLoRA Draft Lets a developer prove that individual low-rank (LoRA) fine-tuning steps on a language model were computed correctly, without revealing private weights or training data. | Research demonstrationResearch demonstration for proving single-sample LoRA fine-tuning steps for 3–13-billion-parameter language models. Medium confidence. Implementation · Crypto / compute · Adversarial prover | Published analysis or argument | Implementation | Crypto / compute | Adversarial prover | None |
| VRAM-residency challenge Draft A timed challenge that tests whether verifier-supplied data is still held in a GPU's on-board memory or has moved to host memory. | Research demonstrationResearch demonstration for detecting whether verifier-supplied data remains in a single GPU's memory. Medium confidence. Implementation · Crypto / compute · Adversarial prover | Published analysis or argument | Implementation | Crypto / compute | Adversarial prover | None |
| Workload classification from telemetry and side channels Inferring from GPU counters or power draw whether chips are training models, serving them or doing non-AI work, without reading weights or data. | Research demonstrationResearch demonstration for telling training from inference and other work using genuine telemetry, including disguised workloads. Medium confidence. Mechanism · Sensing · Adversarial prover | Published attack testing · developers | Mechanism | Sensing | Adversarial prover | Existing features |
| Zero-knowledge proofs of inference A prover produces a cryptographic proof that an output came from running a committed model on a given input, without revealing the weights. | Research demonstrationResearch demonstration for proving a language model's output follows from committed weights, against a cheating prover. Medium confidence. Mechanism · Crypto / compute · Adversarial prover | Published attack testing · independent | Mechanism | Crypto / compute | Adversarial prover | None |
| Zero-knowledge proofs of training constraints Cryptographic proofs that a training run used a dataset and followed a procedure and rules fixed in advance, checkable without seeing the model or data. | Research demonstrationResearch demonstration for proving a training run followed a committed specification and data. Medium confidence. Mechanism · Crypto / compute · Adversarial prover | Published analysis or argument | Mechanism | Crypto / compute | Adversarial prover | None |
| zkLLM zkLLM is a GPU-accelerated zero-knowledge proof system that proves a large language model's output came from committed weights without revealing those weights. | Research demonstrationResearch demonstration for proving an output came from committed weights, against a prover who cheats. Medium confidence. Implementation · Crypto / compute · Adversarial prover | Published analysis or argument | Implementation | Crypto / compute | Adversarial prover | None |
| AI 2040 inference-only verification stack A proposed retrofit that splits data centres into isolated inference units, copies front-end traffic and re-runs random samples to confirm only declared inference runs. | ProposedProposed for showing that retrofitted data centres run only inference. Medium confidence. Implementation · Isolation & architecture · Adversarial prover | No published adversarial analysis recorded | Implementation | Isolation & architecture | Adversarial prover | Retrofit device |
| Attestable zero-knowledge inference prover Attestable's zero-knowledge prover, which the company reports proves large language model outputs came from committed weights at tens of tokens per second. | ProposedProposed for proving an output came from committed weights. Low confidence. Implementation · Crypto / compute · Adversarial prover | No published adversarial analysis recorded | Implementation | Crypto / compute | Adversarial prover | None |
| Chip location verification Timing a chip's signed replies to trusted servers at known places, so that the speed of light bounds how far away the chip can be. | ProposedProposed for bounding how far a chip is from trusted landmark servers when checked. Medium confidence. Mechanism · Accounting · Adversarial prover | Published analysis or argument | Mechanism | Accounting | Adversarial prover | Existing features |
| Chip registries and manufacturing records Recording each AI chip's identity and owner from manufacture onwards, and publishing a cryptographic fingerprint of manufacturing records, so chips can be accounted for later. | ProposedProposed for a checkable record of which chips were made and who declared owning them. Medium confidence. Mechanism · Accounting · Semi-trusted prover | Published analysis or argument | Mechanism | Accounting | Semi-trusted prover | Existing features |
| Data-centre memory challenging Draft A proposed design that times answers from a data centre's memory to check that declared data is present or that no free memory remains. | ProposedProposed for confirming data presence and bounding free memory across data-centre servers. Medium confidence. Implementation · Crypto / compute · Adversarial prover | Published analysis or argument | Implementation | Crypto / compute | Adversarial prover | Retrofit device |
| Hardware performance throttling and licensing Proposed on-chip mechanisms that cut an AI accelerator's performance when a license expires or a trusted trigger fires, bounding what the hardware can do. | ProposedProposed for performance limits a verifier can rely on, against an operator trying to bypass them. Medium confidence. Mechanism · On-chip · Adversarial prover | Published analysis or argument | Mechanism | On-chip | Adversarial prover | New chip design |
| Hardware-enabled guarantees (flexHEG) and guarantee processors A proposed add-on for AI chips: an auditable guarantee processor, sealed in a tamper-protected enclosure, that would check and enforce agreed rules on chip use. | ProposedProposed for checking and enforcing training-compute limits on chips, against adversaries up to states. Medium confidence. Mechanism · On-chip · Adversarial prover | Published analysis or argument | Mechanism | On-chip | Adversarial prover | New chip design |
| Low-trust AI compute verification system overview 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. | ProposedProposed for screening challenged records to show declared inference compute is not training. Medium confidence. Implementation · Isolation & architecture · Adversarial prover | Published analysis or argument | Implementation | Isolation & architecture | Adversarial prover | Retrofit device |
| Lucid sovereignty (location) certificates A draft specification, hosted by Lucid Computing, for short-lived certificates that bound where a workload runs by timing signed messages to servers at known locations. | ProposedProposed for certifying the region where an attested workload ran at a given time. Medium confidence. Implementation · Accounting · Semi-trusted prover | Published analysis or argument | Implementation | Accounting | Semi-trusted prover | Existing features |
| Memory wiping and proofs of secure erasure Overwriting a device's memory in a way a verifier can check, so that data from earlier, undeclared work cannot persist in memory the wipe reaches. | ProposedProposed for showing that no data from earlier work persists in memory the wipe reaches. Medium confidence. Mechanism · Isolation & architecture · Adversarial prover | Published analysis or argument | Mechanism | Isolation & architecture | Adversarial prover | None |
| Model fingerprint attestation A proposed timed challenge that tests whether declared model weights are already in a chip's memory. | ProposedProposed for checking residency of declared model weights with timed challenges. Medium confidence. Implementation · Crypto / compute · Adversarial prover | Published analysis or argument | Implementation | Crypto / compute | Adversarial prover | None |
| Network taps and certifiers Devices on a cluster's network links that copy and hash all traffic, so a verifier can later check sampled records against declared work. | ProposedProposed for committing a complete record of cluster traffic, so declared inference can be checked. Medium confidence. Mechanism · Off-chip devices · Adversarial prover | Published analysis or argument | Mechanism | Off-chip devices | Adversarial prover | Retrofit device |
| Proofs of useful work for capacity accounting Cryptographic evidence of matrix-multiplication work, from which total work is estimated statistically, proposed as one input to bounding the spare capacity of declared hardware. | ProposedProposed for bounding the spare capacity of declared hardware that could run training. Low confidence. Mechanism · Crypto / compute · Adversarial prover | Published analysis or argument | Mechanism | Crypto / compute | Adversarial prover | None |
| RAND secure inference data center (SIDC) design A RAND design for a purpose-built facility that serves already-trained AI models while protecting weights and inference data against state-level attackers. | ProposedProposed for the operator's own weight security, with no outside verification described. Low confidence. Implementation · Isolation & architecture · Semi-trusted prover | Published analysis or argument | Implementation | Isolation & architecture | Semi-trusted prover | Retrofit device |
| Remote detection of data centres Remote detection tracks known large data centres and estimates their power capacity without site access, using satellite imagery, heat signatures and public records. | ProposedProposed for finding undeclared data centres above an agreed compute threshold. Medium confidence. Mechanism · Sensing · Adversarial prover | Published analysis or argument | Mechanism | Sensing | Adversarial prover | None |
| SASH confidential network logger An open-source prototype that routes a facility's inference traffic through a logger and re-runs requests on a separate cluster to check the facility serves inference. | ProposedProposed for telling inference from training on a mutually inspected cluster. Medium confidence. Implementation · Off-chip devices · Semi-trusted prover | No published adversarial analysis recorded | Implementation | Off-chip devices | Semi-trusted prover | Retrofit device |
| Side-channel suppression for isolated facilities Shielding, filtering, jamming and inspecting an AI facility to limit hidden physical communication around monitored network links. | ProposedProposed for bounding physical covert channels out of a verified enclosure. Medium confidence. Mechanism · Off-chip devices · Adversarial prover | Published analysis or argument | Mechanism | Off-chip devices | Adversarial prover | Retrofit device |
| Whole-workload recomputation (reproducible packets) Organizing all AI workloads in a facility into discrete, reproducible units, so that a verifier can recompute a random sample and check each one. | ProposedProposed for recomputing whole workloads to show a cluster runs only declared inference. Medium confidence. Mechanism · Isolation & architecture · Adversarial prover | Published analysis or argument | Mechanism | Isolation & architecture | Adversarial prover | Retrofit device |
No records match these filters.
Legacy code R4 was withdrawn, and no current record uses it.