How to Verify LLM Model Identity Using Zero-Knowledge Proofs

As Large Language Model (LLM) services are increasingly delivered to users through proxy layers, aggregation gateways, and third-party routing, a highly practical issue arises: when a user initiates a request specifying a high-capability model, is the model actually participating in the computation still the one promised by the service provider?

Traditional approaches typically rely on API names, version strings, response headers, or platform declarations. These pieces of information can indicate “what the other party claims to use,” but they can hardly prove “what was actually computed.” Relay services might switch models due to cost, concurrency, quotas, or policy adjustments; service providers might also replace weights, reduce inference capabilities, or use pruned, distilled, or quantized variants without changing the API name.

A more sophisticated approach is to combine model identity verification with Zero-Knowledge Proofs (ZKP). The service provider does not disclose model weights, nor does it leak user inputs and the full inference process, but it can prove to the user that this computation was indeed completed based on a pre-committed model version. The core of this approach is not to “make the model itself trustworthy,” but to add a layer of verifiable cryptographic evidence to model invocations.

1. What Problems Does Model Identity Verification Solve?

Before discussing technical implementation, we need to clarify the verification goals. Model authenticity is not a single concept and can be broken down into at least the following categories:

1.1 Verifying Model Version Consistency

Service providers can generate a unique cryptographic commitment for a specific version of model weights. This commitment can be understood as a fingerprint calculated jointly from model weights, configurations, and necessary metadata. If any parameter in the weight file changes—even a very small numerical value—the final commitment will typically change. Users can thus confirm whether the current service corresponds to a publicly committed model version.

1.2 Verifying if the Model is Replaced Midway

Users might not be calling the model vendor’s native API, but rather a service forwarded through multiple layers. Each layer has the potential to alter the request target. Cryptographic proofs can bind “request-model-output” together, making it impossible for a relay service to merely forward a seemingly credible model identifier while secretly using another model for backend computation.

1.3 Verifying Inference is Completed by the Committed Model

Merely verifying the weight fingerprint is not enough. A platform could publish a model commitment but use another model to actually respond. Therefore, it must be proven that there indeed exists an execution process conforming to the model’s computational rules between the given input, the specified model commitment, and the output result. This step usually requires converting model inference into verifiable computational circuits, constraint systems, or other representations suitable for proof systems.

1.4 Verifying if Capabilities are Secretly Reduced

“Downgrading” does not necessarily mean directly replacing the model. It can also manifest as using low-precision weights, reducing computation steps, adjusting sampling strategies, enabling different system prompts, or employing a pruned model copy. Therefore, comprehensive verification requires a clear scope. It can prove the weight version and computation process, and it can further bind inference configurations, but it may not automatically prove whether the answer quality, knowledge timeliness, or safety policies meet user expectations.

2. Model Commitment Mechanism: Defining “Which Model Exactly”

Model commitment is the foundation of the entire system.

2.1 Basic Form of Commitment

The service provider can normalize the model weights and related configurations, then compute a cryptographic hash to obtain a commitment value:

commitment = Hash(
    normalized_weights
    || architecture_config
    || tokenizer_config
    || inference_policy
)

Here, || denotes concatenation. In actual implementation, the standardization of file ordering, floating-point encoding, parameter naming, version info, and configuration fields must be specified; otherwise, the same model might yield different results due to file arrangement or encoding differences.

Characteristics of the commitment value include:

• It can be publicly disclosed;
• It is difficult to reverse-engineer model weights from the commitment value;
• The commitment value will change if weights or configurations change;
• Under secure assumptions, the provider cannot easily construct a different set of weights for the same commitment.

2.2 What Should the Commitment Cover?

If the commitment only covers weights and ignores the tokenizer, architecture config, and inference rules, the verification may be incomplete. For example, the same weights with different tokenization or different precision/positional encoding/sampling settings can lead to behavioral changes.

Therefore, a more comprehensive model identity commitment usually needs to consider:

• Model weights;
• Network architecture and layer configurations;
• Tokenizer and vocabulary;
• Numerical precision and quantization methods;
• System prompts or policy templates;
• Sampling configurations used during inference;
• Necessary software execution environment information.

However, a larger commitment scope means more complex engineering management. Providers must establish clear version management rules to avoid confusing frequently changing runtime parameters with stable model identities.

2.3 Commitment Publication and Version Governance

To prevent post-hoc modifications, commitments should not be stored only internally by the provider. A more reliable approach is to publish them in advance through public channels with chronological and tamper-proof features. Users retrieve the commitment value before making a call and verify after the call whether the commitment referenced in the proof matches. If the provider temporarily switches models, a new commitment must be generated, with a clear explanation for the version change.

3. How Zero-Knowledge Proofs Participate in Inference Verification

The key capability of ZKP is that the prover can prove a computation was completed correctly without disclosing all inputs, internal states, and secret data.

In LLM scenarios, the proof target can be abstracted as:

Prove the existence of model weights W, input X, and intermediate state S,
such that:
Commit(W) = C
Inference(W, X, Config) = Y
OutputHash(Y) = H

Where:

• W represents model weights;
• X represents user input;
• C is the public model commitment;
• Y is the model output;
• H is the output digest;
• S represents intermediate states during inference;
• Config represents the constrained inference configuration.

The proof content allows the verifier to confirm that there exists a set of weights consistent with commitment C, and that these weights indeed computed result Y under the specified input and configuration, without needing direct access to the full weights or all intermediate tensors.

4. Why Can’t We Simply Stuff LLMs into Proof Systems?

Theoretically, any computable process can be converted into a proof circuit, but the scale of LLM inference is massive, and directly proving the entire computation brings significant costs.

4.1 Massive Computation Scale

LLMs contain extensive matrix multiplications, attention calculations, normalization, activation functions, and sampling operations. ZKP systems are better suited for computations with clear structures and manageable constraint counts, whereas large-scale tensor operations generate massive constraints.

4.2 Floating-Point Operations are Not Suitable for Direct Proof

Standard model inference heavily uses floating-point numbers, while proof systems typically prefer integers or fixed-point arithmetic over finite fields. Therefore, floating-point operations need conversion or the use of provable fixed-point approximation schemes. During conversion, overflow, rounding, truncation, and precision loss must be handled; otherwise, the proven computation might not match the actual inference.

4.3 Non-linear Operations Require Extra Design

Some activation functions, normalization operations, and probability sampling are not easily mapped directly to efficient constraints. Engineering implementations might use lookups, piecewise approximations, custom gate circuits, or other optimization methods, but these introduce extra complexity and potential errors.

4.4 Full Proof May Impact Service Latency

Generating a proof usually consumes more computational resources than standard inference. If a full proof is generated for every request, the server might require additional proving nodes, caching mechanisms, and batch scheduling. In high-concurrency scenarios, proof costs could become the primary bottleneck.

Therefore, practical systems often do not prove the entire LLM inference from the start, but adopt a phased strategy:

1. First verify the model commitment;
2. Then verify critical layers or key computation fragments;
3. Perform batch proofs for multiple requests;
4. Use recursive proofs to compress multiple execution processes;
5. Enable full proofs for high-value, disputed, or audit scenarios.

5. A Practical Invocation Workflow

A relatively complete verification workflow can be divided into the following steps:

Step 1: Provider Generates Model Commitment

The provider normalizes the fixed-version model and related configurations, generates a commitment value, and publishes the model version, commitment scope, and effective time.

Step 2: User Initiates Request with Verification Requirements

The user’s request includes the target model commitment, input content digest, inference config digest, and a random challenge nonce. The random challenge reduces the provider’s chance of pre-forging a proof.

Step 3: Server Executes Inference

The server completes inference using internal weights while recording the intermediate information needed for the proof. The user input itself can remain private, appearing only as a commitment or digest in the proof.

Step 4: Server Generates Proof

The prover generates a zero-knowledge proof verifying the following facts:

• The weights used are consistent with the public commitment;
• The input matches this request;
• The inference process satisfies predefined computational constraints;
• The output digest matches the returned content;
• The specified inference configuration has not been replaced.

Step 5: Local Verification by User

The user verifies using the public verification key, model commitment, request digest, output digest, and server proof. Upon successful verification, the user can confirm the response meets the agreed cryptographic conditions.

Schematic pseudocode is as follows:

request_digest = Hash(user_input || inference_config || nonce)
response = service.infer(
    model_commitment = C,
    request_digest = request_digest
)
valid = verify(
    proof = response.proof,
    public_inputs = {
        commitment: C,
        request_digest: request_digest,
        output_digest: Hash(response.output)
    }
)
if valid:
    accept(response.output)
else:
    reject("Cannot verify model identity or inference process")

This code is for illustrative purposes only and does not represent any specific platform’s API. Real systems also need to handle key rotation, proof versions, exception retries, request replay, and audit logs.

6. What This Technology Can and Cannot Solve

What It Can Solve

First, it improves the verifiability of model versions, reducing the trust risks associated with one-sided platform declarations.

Second, it constrains relay services from arbitrarily forwarding high-value model requests to lower-capability models, while helping enterprises audit whether model invocations comply with procurement or compliance requirements.

Third, it provides a degree of computational correctness proof without disclosing weights, protecting the service provider’s intellectual property.

Finally, it provides evidence for dispute resolution. When abnormal responses occur, users can inspect the commitment, input digest, output digest, and proof, rather than relying solely on the provider’s logs.

What It Cannot Automatically Solve

Zero-knowledge proofs cannot prove that a model’s answer is definitely correct. A model can faithfully execute while producing factually incorrect results.

It also cannot automatically determine whether a model has good safety, fairness, or values. What is proven is that the computation process meets constraints, not that the output content satisfies all business standards.

Moreover, if the model commitment itself corresponds to a polluted version, one with opaque training data, or a weaker variant, the proof system will merely faithfully prove that this version was used.

For external tool calls, retrieved content, dynamic system prompts, and human review processes, additional verifiable records are needed; otherwise, only the model’s main computation can be proven, without covering the entire answer generation chain.

7. Key Engineering Issues for Enterprise Deployment

7.1 Define Verification Levels

Verification capabilities can be divided into multiple levels:

• Identity Level: Only verify model commitment;
• Routing Level: Verify the request target was not replaced;
• Computation Level: Verify the core inference process;
• Full-Chain Level: Simultaneously verify retrieval, tool calls, policy configs, and output.

Different businesses do not need to pursue the highest level. Standard applications might only need identity and routing level verification, while financial, government, or research scenarios might require higher-level audit capabilities.

7.2 Distinguish Model Identity from Runtime Strategy

Model weights, system prompts, sampling parameters, and safety policies should be committed or recorded separately. Otherwise, when output changes occur, it is difficult to determine whether the weights changed or the runtime configuration was altered.

7.3 Design an Auditable Evidence Chain

Evidence should at least include the model commitment, request digest, time info, output digest, proof version, and verification result. For enterprise environments, verification logs can also be written to logging services or immutable storage for subsequent audits.

7.4 Evaluate Proof Costs

Before deployment, test proof generation time, verification time, memory usage, batch processing capability, and failure retry costs. One cannot focus solely on cryptographic security while ignoring actual service latency and resource overhead.

7.5 Protect User Privacy

Although proof systems can hide inputs, the request digest itself might expose length, timing, and invocation patterns. In highly sensitive scenarios, metadata protection, request obfuscation, and access log permission controls must also be considered.

8. Future Development Directions

With the advancement of dedicated hardware, proof system optimization, and model architecture modifications, verifiable model computation may gradually transition from experimental capability to production application. More realistic future directions include:

• Designing computation structures more suitable for proofs;
• Lowering per-request costs through batch proofs;
• Using recursive proofs to compress long-chain inference;
• Combining model commitments with data source commitments;
• Unifying verification for retrieval-augmented generation, tool calls, and agent execution;
• Establishing cross-provider standards for model identity and proof formats.

Overall, zero-knowledge proofs do not replace provider reputation, third-party audits, or traditional security mechanisms; instead, they add a layer of independently verifiable technical evidence for LLM services. Their true value lies in transforming “what the provider claims to use” into “what the user can self-verify as meeting the agreed computation.”

For enterprises invoking LLMs through relay, aggregation, or multi-tier proxies, this capability is particularly crucial. In the future, a model name might no longer be just a string, but will correspond to a set of verifiable commitments, configurations, and execution proofs. Only when identity, computation, and output form a complete binding will the trustworthiness of LLM services evolve from verbal promises to auditable, verifiable engineering facts.