Proposal Explorer
ResetA verification proposal from the AI Verification Tech Map, from its records of 2026-10-08. https://trustbutveri.fyi/explorer/?mechanisms=M-0018,M-0008&prover=semi-trusted&cols=prover,tested
Analysis
Applied filters: Prover: Semi-trusted. Not set (Any): Verifier devices on site, Prover cooperation, Chips, Minimum readiness, Attack testing, Keep hidden from the verifier.
Overview
| Mechanism | Readiness | Open flaws | Prover | Attack testing |
|---|---|---|---|---|
| Chip location verification | R1 | 4 significant | Adversarial | Analysis |
| TEE remote attestation for AI workloads | R3 | 2 critical5 significant | Semi-trusted | Independent red-team |
- Open flaws: n critical n significant n minor
Claim coverageNo claims yet
Add claims to see which ones the mechanisms address.
Properties1 in production · 1 built for an adversarial prover
- In production
- R3 TEE remote attestation for AI workloads for showing which software ran to a party that distrusts the operator holding the hardware
- Built for an adversarial prover
- Chip location verification
- No new hardware needed
- Chip location verification and TEE remote attestation for AI workloads
Attack testing2 mechanisms with published testing
Published attempts to break a system, including those that found failures. Testing history does not show that open flaws are resolved.
- Chip location verification: Analysis
- TEE remote attestation for AI workloads: Independent red-team
Limits1 mechanism with open critical findings · 1 not yet demonstrated · 2 mechanisms with open significant findings
- Open critical flaws
DDR5 memory-bus interposers forge Intel TDX attestations and break SEV-SNP protections (TEE.fail, DDRop) in TEE remote attestation for AI workloads
Critical when the verifier must resist physical access plus host control on the affected DDR5 platforms. The Intel demonstrations defeat attestation; TEE.fail's AMD demonstration extracts a guest key, not an AMD attestation key. These results do not cover every TEE architecture.
Independent researchers placed an interposer, built for under $1000, on the DDR5 memory bus of servers running Intel TDX and AMD SEV-SNP. Server TEEs encrypt memory deterministically, without integrity or freshness protection, and the researchers exploited this to recover secrets. The attack needs physical access and root privileges.
- On Intel, they extracted the provisioning certification key from a machine that Intel's service rated fully up to date. This per-CPU key signs the keys used in SGX and TDX attestation. With it they forged SGX and TDX attestations.
- On AMD SEV-SNP with ciphertext hiding enabled, they recovered an ECDSA private key used by OpenSSL inside the virtual machine. It was not an AMD attestation key. Other independent attacks did break SEV-SNP attestation. Battering RAM did so with a DDR4 interposer, and RMPocalypse and Fabricked from malicious host software.
A second team, from KU Leuven, ETH Zurich, Durham University and Google, built DDRop, an active DDR5 interposer with a bill of materials of $159. It silently drops memory writes, which memory encryption without freshness protection cannot detect. With brief physical access and control of the host software and BIOS, the researchers forced trust domains into debug mode and forged attestation reports on an up-to-date Intel TDX platform. The same primitive breaks the integrity of Scalable SGX and SEV-SNP, though the authors report no SEV-SNP attestation forgery.
The TEE.fail authors report that Intel and AMD consider interposer attacks out of scope, which leaves physical security as the only mitigation. The DDRop authors report the same position, and that both vendors issued security advisories on disclosure in September 2026. PAL*M lists this attack class as out of its scope, and Tinfoil's documentation acknowledges it. Gloria Z calls key extraction through bus interposition "relatively low-hanging fruit" in an international treaty scenario. 9 10 13 14 19 21 25 26
Demonstrated attack · Critical · Open · Mechanism-class evidence. On the record
Related mechanism R1 Hardware-enabled guarantees (flexHEG) and guarantee processors: A tamper-protected enclosure around the chip is the proposed answer when the party that holds the hardware may attack it physically. A pointer, not evidence that this flaw is mitigated. Add
DDR4 memory-bus interposers forge SGX and SEV-SNP attestation (Battering RAM, WireTap) in TEE remote attestation for AI workloads
Critical for the tested DDR4 SGX and SEV-SNP configurations against a physical host attacker. The authors exclude DDR5 from these demonstrations, including TDX servers; the DDR5 attacks have a separate finding.
Two independent teams broke server TEE attestation on DDR4 memory with interposers they built themselves. Both attacks need physical access to install the device and root privileges on the host.
- Battering RAM, by researchers at KU Leuven and the Universities of Birmingham and Durham, uses an interposer with a bill of materials of $47.62. It creates memory aliases at runtime, which bypasses the boot-time alias checks that AMD and Intel introduced against static aliasing attacks such as BadRAM. On Intel Scalable SGX it gained arbitrary read and write access to enclave plaintext and extracted SGX's platform provisioning key, which lets an attacker forge attestation certificates for arbitrary quoting enclaves. On up-to-date AMD SEV-SNP servers it captured the launch digests of genuine VMs and replayed them into modified VMs, so that backdoored VMs pass attestation.
- WireTap, by researchers at Purdue University and Georgia Tech, uses an interposer built for under $1000 that records DDR4 bus traffic. On a Xeon Scalable server in fully trusted status it recovered the ECDSA attestation key of SGX's Quoting Enclave in 45 minutes and forged SGX quotes. The authors then showed end-to-end attacks on SGX-based blockchain deployments.
Both attacks are limited to DDR4 systems. The Battering RAM authors state that all commercial TDX machines use DDR5, and the WireTap authors state that 4th and 5th generation Xeon Scalable processors need DDR5 and are not affected by their current work. According to the Battering RAM authors, Intel and AMD acknowledged the findings but consider physical attacks on DRAM out of scope for their current products. The WireTap authors report that Intel considers their attack outside the SGX threat model, and that there is no mitigation besides running servers in secure physical environments. 19 20
Demonstrated attack · Critical · Open · Mechanism-class evidence. On the record
Related mechanism R1 Hardware-enabled guarantees (flexHEG) and guarantee processors: A tamper-protected enclosure around the chip is the proposed answer when the party that holds the hardware may attack it physically. A pointer, not evidence that this flaw is mitigated. Add
- Open significant flaws
9 flaws in 2 mechanisms
Extracting a chip's key lets another device answer for it in Chip location verification
Ping-based protocols rely on cryptographic keys stored on the chip. Tee and Happel argue that an adversary with physical access could extract these keys and so compromise location verification. They propose GPU fingerprints as a mitigation, so far tested on 24 GPUs. Brass and Aarne assume the keys are stored securely, for example in a TPM. 1 6
Theoretical argument · Significant · Open. On the record
Added delay can shift an estimated position in Chip location verification
Brass and Aarne cite internet-geolocation research in which artificially increased round-trip times moved the estimated location by up to 1,000 km, with a 74% chance of avoiding detection. Avellar and Grunewald list inflated ping times from circuitous routing as an evasion route. Added delay only loosens a distance bound, and Brass and Aarne propose a hard time limit as the counter: a chip that replies too slowly cannot be ruled out of a restricted location. 1 4
Demonstrated attack · Significant · Open. On the record
Faster-than-assumed network paths in Chip location verification
Brass and Aarne list dark fibre and other private high-speed interconnects as ways to lower measured delays artificially. They judge that leasing dark fibre would probably not be a considerable challenge for covertly or openly adversarial actors. Avellar and Grunewald note that this can make a chip appear to be somewhere else entirely. A limit set at the vacuum speed of light cannot be beaten, but it makes honest chips fail more often. 1 4
Theoretical argument · Significant · Open. On the record
Compromised landmarks can falsify measurements in Chip location verification
A party that controls landmark servers can report false timing. Brass and Aarne cite research in which manipulating a third of the landmarks shifted the estimated location by about 700 km. Avellar and Grunewald note that compromised landmarks let adversaries spoof travel-time measurements directly. The draft specification asks verifiers to require anchors in diverse places, run by several independent operators. 1 4 5
Theoretical argument · Significant · Open. On the record
H100 attestation not bound to a specific confidential VM in TEE remote attestation for AI workloads
The demonstration combines a genuine H100 report with forged Intel TDX evidence. It depends on the CPU attestation already being defeated; a GPU report alone does not demonstrate that an intact CPU-to-GPU trust chain was bypassed.
The TEE.fail authors fetched genuine H100 confidential-computing attestations from a rented server running their TDX VM. They combined these with forged TDX quotes. A proxy running outside any TEE then passed both the TDX and the GPU attestation checks. The authors attribute this to NVIDIA not binding the H100 to the identities of specific VMs. Their site states more generally that NVIDIA's attestation reports are not bound to a specific confidential VM or CPU. Intel, AMD, NVIDIA and the affected deployments acknowledged the findings, according to the authors, and the affected deployments were working on mitigations. The attack does not target NVIDIA's confidential-computing components directly, so the authors state that there are no mitigations on the NVIDIA side. 13
Demonstrated attack · Significant · Open · Mechanism-class evidence. On the record
Side channels and other attacks by the host on CPU and GPU TEEs in TEE remote attestation for AI workloads
The cited studies concern particular CPU and GPU platforms and attack prerequisites. StackWarp has AMD microcode patches; the open class-level entry does not mean every cited defect is unmitigated or applies to every TEE-backed implementation.
PAL*M and Attestable Audits cite published side-channel, single-stepping, interrupt-injection and memory-aliasing attacks on Intel TDX and AMD SEV, including T-Time, TDXploit, CIPHER-LEAKS, Heckler and BadRAM. PAL*M treats them as out of scope. Attestable Audits proposes revoking vulnerable enclave images. Gloria Z notes that performance counters have themselves been used as a side channel, for example in CounterSEVeillance. New attacks of this kind continue to appear. In StackWarp, researchers at CISPA showed that a malicious hypervisor can shift the stack pointer of an SEV-SNP guest on AMD Zen 1 to Zen 5 processors with simultaneous multithreading enabled, which fully breaks the guest's integrity. AMD released microcode patches. On the GPU side, an independent analysis of NVIDIA's confidential computing by IBM Research and Ohio State University found that bulk command and data transfers are protected, but some metadata, timing behaviour and coordination signals remain in unprotected shared memory. The authors report that these can reveal computational behaviour and in some cases allow manipulation of operations. They disclosed the findings to NVIDIA. 8 9 10 28 29
Demonstrated attack · Significant · Open · Mechanism-class evidence. On the record
Attestation covers launch state, and measurements can be incomplete in TEE remote attestation for AI workloads
A general measurement-boundary limitation. The concrete WhatsApp configuration findings were fixed before launch; Apple's research-environment configuration flaw was also fixed. Their fixes do not remove the need to bind each deployment's runtime inputs.
Attestation measures launch state, not runtime state. Data loaded later, such as model weights, must be bound separately. Gloria Z argues that gaps in measuring feature flags, environment variables and invocation arguments are "perhaps the most likely failure mode". She also warns that a badly designed hashing scheme could let two models with significantly different properties share a hash "without breaking the hash function itself". Independent reviews of production systems have found such gaps. In WhatsApp's deployment, Trail of Bits found environment variables and ACPI tables loaded outside the measurement, and rated both high severity. Meta fixed them. On an Apple PCC node running in Apple's research environment, a researcher reports that tampered configuration files left the attestation unchanged. 10 23 24 30 31
Theoretical argument · Significant · Open · Mechanism-class evidence. On the record
Deployment-level attestation does not cover the whole chip in TEE remote attestation for AI workloads
A limitation of deployment-level evidence when the claim concerns all activity on a chip. It does not defeat a narrower claim about which software served one attested request.
An attestation shows what one confidential VM runs. It does not show what else the hypervisor runs on the same hardware. Gloria Z calls the difference between deployment-level attestation and chip-wide monitoring "the gaping hole in this plan". This matters most for negative claims such as the absence of training. 10
Theoretical argument · Significant · Open · Mechanism-class evidence. On the record
Related mechanism R2 On-chip telemetry from timing, memory and performance counters: On-chip counters are a proposed route to evidence about everything a chip runs, which attestation of one workload does not give. A pointer, not evidence that this flaw is mitigated. Add
Root of trust concentrated in a few hardware vendors in TEE remote attestation for AI workloads
Vendor trust is an assumption of the attestation chain. The root-seed extraction study concerns AMD EPYC Milan and firmware downgrade with privileged host and platform-flash access; it is not evidence of the same failure on Intel, NVIDIA or all AMD generations.
The root of trust is the certificate authorities of a small number of vendors (AMD, Intel and NVIDIA), which generate the keys and fuse them onto the chips. Gloria Z notes that whoever has access to a hardware key, or can certify one, can in principle produce valid reports for arbitrary measurements without the physical chip. Attestable Audits notes that the approach holds only "as long as the vendor of the secure hardware is trusted". A 2026 preprint reports that a host with root control and the ability to rewrite platform flash can downgrade an AMD EPYC Milan processor to legacy security-processor firmware and extract the hardware root seed from which SEV-SNP attestation keys are derived. The authors state that this lets them forge attestation reports for any firmware version. AMD describes the firmware-loader flaw the attack starts from as a legacy attack mitigated in 2021. 8 10 32 33
Theoretical argument · Significant · Open · Mechanism-class evidence. On the record
- Not yet demonstrated
- R1 Chip location verification
Possible additions2 for open flaws · 2 for dependencies
Mechanisms on the map that are not in the proposal. Pointers, not recommendations.
-
- Bears on the open critical flaw “DDR5 memory-bus interposers forge Intel TDX attestations and break SEV-SNP protections (TEE.fail, DDRop)” in TEE remote attestation for AI workloads. A tamper-protected enclosure around the chip is the proposed answer when the party that holds the hardware may attack it physically.
- Bears on the open critical flaw “DDR4 memory-bus interposers forge SGX and SEV-SNP attestation (Battering RAM, WireTap)” in TEE remote attestation for AI workloads. A tamper-protected enclosure around the chip is the proposed answer when the party that holds the hardware may attack it physically.
- TEE remote attestation for AI workloads waits on it. Vendor threat models exclude sophisticated physical attacks, but in international verification the prover holds the hardware.
-
- Bears on the open significant flaw “Deployment-level attestation does not cover the whole chip” in TEE remote attestation for AI workloads. On-chip counters are a proposed route to evidence about everything a chip runs, which attestation of one workload does not give.
- TEE remote attestation for AI workloads waits on it. Negative claims such as "no undeclared training" need chip-wide accounting of all workloads, which attestation does not provide.
Dependencies9 blockers
- Blockers
9 blockers recorded
- Chip location verification
- No public code or reproducible end-to-end location results are available for the reported H100 prototype. Adversarial validation 2 3
- Per-chip keys must be provisioned and protected against extraction; hardware-integrated, tamper-resistant versions still need R&D. Hardware trust 1 6 34
- The time limit forces a trade-off: a limit at the speed of light in fibre can be beaten by faster links, while one at the vacuum speed of light makes honest chips fail often. Protocol soundness 1
- A trusted landmark network must be built and secured, and who should operate it, under what oversight, is unsettled. Access & governance 1 4
- TEE remote attestation for AI workloads
- Vendor threat models exclude sophisticated physical attacks, but in international verification the prover holds the hardware. Hardware trust. Waits on Hardware-enabled guarantees (flexHEG) and guarantee processors 11 13 19 20 25 35
- Negative claims such as "no undeclared training" need chip-wide accounting of all workloads, which attestation does not provide. Coverage & hidden compute. Waits on On-chip telemetry from timing, memory and performance counters 10
- Multi-GPU and multi-node coverage is incomplete, because Hopper leaves NVLink traffic unencrypted and NVIDIA's April 2026 release notes list no multi-node confidential mode. Performance & compatibility 11 36
- Rival parties have not agreed on trust roots and key provenance they would accept. Access & governance 10
- CPU-only enclaves are costly for large models, because in the Attestable Audits prototype CPU inference cost 21.7 times as much per token as GPU inference and the enclave roughly doubled the CPU cost. Performance & compatibility 8
- Chip location verification
What the verifier seesNo outright disclosure specified
From the family or selected implementation's record.
- Model weights
Hidden by TEE remote attestation for AI workloads.
Not involved: Chip location verification.
- Inputs and outputs
Hidden by TEE remote attestation for AI workloads.
Not involved: Chip location verification.
- Training data
Hidden by TEE remote attestation for AI workloads.
Not involved: Chip location verification.
Exposure notes
- Chip location verification: Times signed replies from chips; it does not handle model data.
- TEE remote attestation for AI workloads: The enclave keeps what runs inside it from the host and the verifier; the verifier sees signed measurements. This relies on the chip vendor's hardware.
Implementations6 systems
- Chip location verification
- R1 Lucid sovereignty (location) certificates Standard, Lucid Computing
- TEE remote attestation for AI workloads
- R3 Apple Private Cloud Compute Product
- R2 Attestable Audits Research prototype, University of Cambridge
- R2 Cove Open-source project
- R2 PAL*M Research prototype, University of Waterloo
- R3 Tinfoil model identity (Modelwrap) Product, Tinfoil
Sources36 cited
- Location Verification for AI Chips, A. Brass & O. Aarne (2024). Original
- Location Verification for AI Chips (issue brief), A. Brass (2025). Original
- Ping-based Location, Ulyssean (2025). Original
- Near-Term Verification Methods for AI Chip Exports, B. Avellar & E. Grunewald (2026). Original
- Sovereignty Certificates: draft specification, version 0.1.0, Sovereignty Certificates Working Group (2025). Original
- GPU Fingerprinting for Location Verification, W. Tee & J. Happel (2026). Original
- Secure, Governable Chips: Using On-Chip Mechanisms to Manage National Security Risks from AI & Advanced Computing, O. Aarne et al. (2024). Original
- Attestable Audits: Verifiable AI Safety Benchmarks Using Trusted Execution Environments, C. Schnabl et al. (2025). Original
- PAL*M: Property Attestation for Large Generative Models, P. Chantasantitam et al. (2026). Original
- On TEEs for Privacy-Preserving Monitoring in AI Governance, Gloria Z (2026). Original
- NVIDIA Secure AI with Blackwell and Hopper GPUs (White Paper), NVIDIA (2025). Original
- Now in General Availability: NVIDIA H100 GPUs in Microsoft Azure Confidential Virtual Machines, C. Su (2024). Original
- TEE.fail: Breaking Trusted Execution Environments via DDR5 Memory Bus Interposition, J. Chuang et al. (2026). Original
- A primer on secure enclaves, Tinfoil (2026). Original
- Backend infrastructure, Tinfoil (2026). Original
- How verification works in Tinfoil, Tinfoil (2026). Original
- modelwrap: Reproducible dm-verity read-only image of Huggingface models, Tinfoil (2026). Original
- Private Cloud Compute: A new frontier for AI privacy in the cloud, Apple Security Engineering and Architecture (SEAR) (2024). Original
- Battering RAM: Low-Cost Interposer Attacks on Confidential Computing via Dynamic Memory Aliasing, J. De Meulemeester et al. (2026). Original
- WireTap: Breaking Server SGX via DRAM Bus Interposition, A. Seto et al. (2025). Original
- RMPocalypse: How a Catch-22 Breaks AMD SEV-SNP, B. Schlüter & S. Shinde (2025). Original
- SEV-SNP RMP Initialization Vulnerability (AMD-SB-3020), AMD (2025). Original
- What we learned about TEE security from auditing WhatsApp's Private Inference, Trail of Bits (2026). Original
- Meta WhatsApp Private Processing (security review), Trail of Bits (2025). Original
- DDRop: Active Memory Interposer Attacks on Confidential VMs by Dropping DDR5 Writes, J. De Meulemeester et al. (2026). Original
- Fabricked: Misconfiguring Infinity Fabric to Break AMD SEV-SNP, B. Schlüter et al. (2026). Original
- SEV-SNP Routing Misconfiguration (AMD-SB-3034), AMD (2026). Original
- Blueprint, Bootstrap, and Bridge: A Security Look at NVIDIA GPU Confidential Computing, Z. Gu et al. (2026). Original
- StackWarp: Breaking AMD SEV-SNP Integrity via Deterministic Stack-Pointer Manipulation through the CPU's Stack Engine, R. Zhang et al. (2026). Original
- How Tinfoil Proves Exactly What Model Is Running, Tinfoil Team (2026). Original
- Beyond Prompt Injection: Hacking Apple's Private Cloud Compute, D. Selmanaj (2026). Original
- Insecure Despite Proven Updated: Extracting the Root VCEK Seed on EPYC Milan via a Software-Only Attack, M. Shen & Y. Qin (2026). Original
- MilanLaunchy Firmware Loader (AMD-SB-3045), AMD (2026). Original
- Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification, S. Ansari (2026). Original
- Technical Options for Flexible Hardware-Enabled Guarantees, J. Petrie & O. Aarne (2025). Original
- NVIDIA Trusted Computing Solutions Release Notes (R595 TRD1), NVIDIA (2026). Original
Share the link to this proposal. This proposal is also available as plain text and JSON.
Filter mechanisms
Filters apply to mechanisms only. They describe the setting a proposal is for, and all are off by default. A mechanism that a filter rules out is flagged and does not count towards claim coverage. Selected implementations use their own record fields. A match means not excluded; conditional or unspecified exposure stays with a note. Passing a filter does not establish that the assumptions hold in a deployment.
25 of 25 mechanisms match · Clear all
Prover
How far can the party being checked be trusted?
The prover is the party being checked. Semi-trusted designs rely on part of its stack: usually the chip vendor's hardware root of trust, its firmware or counters, or its supply-chain records. Adversarial designs aim to hold even if it cheats wherever the checks allow, within their stated assumptions.
Keeps mechanisms whose threat model holds against at least this prover. Adversarial is the strongest assumption. Definitions
Verifier devices on site
May the verifier install its own hardware at the prover's sites?
Some mechanisms need a device the verifier owns or trusts at the prover's facility, such as a network tap, a bandwidth limiter or a sealed sensor. Choose Not allowed when the setting rules that out. Inspectors are not covered.
"Not allowed" removes mechanisms that need a retrofit device, such as a network tap or a sealed sensor. Definitions
Prover cooperation
How much must the prover take part?
Required: the prover takes part, for example by logging requests, producing proofs or opening records. Partial: some access, such as installing a device. Not required: works from outside, such as satellite imagery.
"Partial at most" removes mechanisms that need the prover's active participation. "Not required" keeps only those that work without it. Definitions
Chips
May the proposal depend on new chip designs?
New chip features take years to reach a deployed fleet and cover only chips made after they ship. Mechanisms that use shipping features, such as trusted execution environments or performance counters, stay.
"Existing chips only" removes mechanisms that need changes to future chip designs. Definitions
Minimum readiness
How mature must each mechanism be?
- Any (selected)25 match
- R1 Proposed25 match
- R2 Demonstrated15 match
- R3 In production4 match
- R4 Deployment-ready0 match
A level describes the public evidence for a mechanism's stated use, not its cost or feasibility. R3 can still have open critical flaws.
Keeps mechanisms whose readiness level is at least this one. Definitions
Attack testing
How hard has each mechanism been attacked in public?
The strongest published attempt to break the mechanism for its verification use: a security analysis, red-teaming by its developers or collaborators, or a red team independent of them.
Keeps mechanisms whose strongest published attack testing is at least this. Definitions
Keep hidden from the verifier
What must the verifier never see? Choose any.
Model weights: the checked model's parameters. Inputs and outputs: the requests a deployed model serves and its responses. Training data: what a model was trained on. Each mechanism's exposure is the editors' reading of its record: shown, depends on the design (kept, with a note), hidden, not involved, or unspecified for a selected implementation. Code and configuration are not covered yet.
Removes mechanisms that show the asset to the verifier. Conditional or unspecified exposure stays with a note and needs checking against the privacy requirement.
Claims
A claim is something one party wants to verify about another party's AI hardware or software. Each claim's number shows how the proposal addresses it.
- Addressed. A mechanism in the proposal is aimed at this claim and is not excluded by the filters.
- Partly addressed. Only supporting mechanisms, or mechanisms aimed at it that the filters exclude.
- Unaddressed. No mechanism in the proposal addresses this claim.
Addressed means a mechanism in the proposal is aimed at the claim and is not excluded by your filters. It does not mean the claim is verified: check its assessed use, readiness, assumptions and open flaws.
Mechanisms
A mechanism is a general technique for verifying claims. Its badge is its readiness level for its stated use. An optional implementation choice narrows its assumptions, assessed use and claim links to that record. Lines join it to the claims it addresses. Click a line for details.Under its name it lists the claims it is aimed at or supports.
- Aimed at the claim: verifying it is a direct purpose of the mechanism.
- Supports the claim: helps verify it without being aimed at it.
- Faint: excluded by your filters, so it does not count towards claim coverage.
Overview
One row per mechanism in the proposal. Every mark comes from that mechanism's record, as listed in the panels below; what the verifier sees is the editors' reading of the record's text. Flaw counts are per mechanism. Summary counts name mechanisms with open findings, not a sum of attacks. Choosing an implementation narrows each row to that record's assessed use; family findings remain as context.
What the verifier sees
For model weights, inputs and outputs, and training data. This is the editors' reading of each mechanism's record (its threat model, how it works and its limitations), not a field of the record. Shown: the verifier sees it. Depends: on the design or variant, or the verifier sees only samples. Hidden: the verifier sees only commitments, hashes, proofs or results. Not involved: the record does not handle it. Unspecified: the selected implementation has no asset-specific assessment here.
Possible additions
Mechanisms on the map, not in the proposal, that the records connect to an unaddressed or partly addressed claim, an open flaw or a dependency. They are pointers, not recommendations: each brings its own readiness level and flaws, and none is claimed to close a flaw. Links from flaws are the editors' reading of the two records.
Start from a goal
A goal is something a rule or agreement about AI sets out to achieve. Choosing one loads the claims it needs verified. Your mechanisms and filters stay as they are.
- Cap frontier training2 direct, 3 supportingKeep every AI training run below an agreed amount of compute.
- Pause frontier AI development2 direct, 3 supportingStop new AI training runs and experiments for an agreed period, while existing models stay in service.
- Deploy only evaluated models2 direct, 3 supportingDeploy powerful AI models widely only after their risks have been evaluated and judged manageable.
- Prevent catastrophic misuse2 direct, 2 supportingKeep capable AI models from helping anyone carry out catastrophic attacks, such as biological or chemical ones.
- Prevent weight theft1 direct, 1 supportingKeep the weights of capable AI models from being copied out of the facilities that hold them.
- Enforce chip export controls1 directKeep export-controlled AI chips at the destinations they were authorised for.
Start from a published design
Choosing a design loads the mechanisms its record realises or depends on. If the proposal has no claims yet, it also loads the claims that record says the design addresses.
- AI 2040 inference-only verification stack7 mechanismsProposed architecture, AI Futures Project
- Low-trust AI compute verification system overview7 mechanismsProposed architecture, Machine Intelligence Research Institute
- RAND secure inference data center (SIDC) design3 mechanismsProposed architecture, RAND