Cryptographic Attestation & Verification Model (CAVM)

MindCP introduces a novel cryptographic attestation framework designed to prove the provenance, integrity, and authenticity of outputs generated by decentralized agents. This model leverages digital signatures, zero-knowledge proofs, and deterministic state representations to achieve verifiability without relying on centralized authorities.

1. Formal Representation of Model State

Let a MindCP Agent be defined by a deterministic function

$$f θ ​ :X→Y$$

where X denotes the input space, Y the output space, and θ∈Rⁿ represents the fixed model parameters.

To cryptographically bind an output to a specific model instance, we derive a state commitment using a collision-resistant hash function H:

$$C=H(f θ ​ ∥θ∥m)$$

where m is the message or input prompt, and ∥ denotes byte-level concatenation.

2. Attestation Signature Scheme

Each agent instance is initialized with a public-private key pair (pk,sk). Upon generating an output y=f _θ(m), the agent signs the tuple (m,y,C) using a digital signature algorithm such as EdDSA:

$$σ=Signsk ​ (m,y,C)$$

The tuple (m,y,C,σ,pk) forms the attestation package, which can be independently verified:

$$Verify pk ​ (m,y,C,σ)=true$$

3. Deterministic Output Verification

To avoid nondeterminism in generative AI outputs, MindCP constrains the agent behavior using seed-locked generation:

$$y=f θ ​ (m;s)$$

where sss is a shared PRNG seed. This ensures that the same input and seed always produce the same output, satisfying the deterministic constraint:

$$f θ ​ (m 1 ​ ;s)=f θ ​ (m 2 ​ ;s)⇒m 1 ​ =m 2 ​$$

The seed is included in the attestation hash:

$$C=H(f θ ​ ∥θ∥m∥s)$$

4. Zero-Knowledge Proof of Execution

For sensitive models, MindCP optionally supports zk-SNARK-compatible attestation, where a prover generates a succinct proof π such that:

$$π=Prove(R f ​ ,m,y)$$

where R_f​ encodes the execution circuit of f_θ​. A verifier can then check:

$$VerifyZK(π,m,y)=true$$

This enables third parties to trust the correctness of model outputs without revealing the model parameters or internal architecture.

5. Blockchain Anchoring

To ensure immutability, MindCP periodically commits attestation hashes to a smart contract on Ethereum:

$$SubmitAttestation(C,t)→on-chain$$

where ttt is a timestamp or block height. This creates a tamper-proof log of model responses that can be queried and audited by any party.

6. Security Assumptions

The security of MindCP’s attestation model is based on the following cryptographic hardness assumptions:

• Collision resistance of hash function H

• Unforgeability of the digital signature scheme under chosen message attacks (UF-CMA)

• Soundness and completeness of the zk-SNARK protocol

• Determinism of the model f_θ​ under fixed seeds

Together, these ensure that any claimed output can be cryptographically linked to a specific model, input, and execution instance, forming the backbone of trustless AI verification.