Model Attestation

An attestation is a permanent, tamper-proof record stored on a blockchain (e.g., Ethereum, Solana) or a decentralized network (e.g., Ethereum Attestation Service, EAS). It cryptographically links a model to its creator, training data hash, and creation timestamp, creating an immutable chain of custody. This prevents model spoofing and ensures the model's origin can be independently verified by anyone.

Attestations can encode key performance metrics directly into the signed data structure. This allows developers to verify claims about a model's accuracy, latency, or fairness without trusting the publisher. Common attested metrics include:

• Benchmark scores (e.g., accuracy on ImageNet, HELM scores)
• Inference cost and latency specifications
• Bias audit results or fairness metrics This creates a trust-minimized marketplace for model performance.

The core technical guarantee is the binding of a model to its exact parameters. The attestation contains a cryptographic hash (e.g., SHA-256) of the model's serialized weights or a Merkle root of its architecture. Any alteration to the deployed model—whether from tampering, quantization, or fine-tuning—will produce a different hash, causing the attestation verification to fail. This ensures the model executed is identical to the one attested.

Trust is distributed across a network of attesters or validators, not a single central authority. These can be:

• Committee-based: A known set of entities (e.g., research labs) must co-sign.
• Stake-based: Attesters bond stake which can be slashed for false claims.
• Reputation-based: Systems like Ethereum Attestation Service (EAS) allow any schema, with trust derived from the attester's reputation. This design removes single points of failure and censorship.

Attestations are composable data primitives. A downstream application (e.g., a prediction market) can trustlessly query and verify an attestation as a precondition for execution. Attestations can also support revocation, where the original attester can invalidate a previously issued statement (e.g., if a vulnerability is found). The revocation status is stored on-chain, making the trust model dynamic and accountable.

Model attestations enable several key applications in decentralized AI:

• On-chain Inference: Smart contracts verify an attestation before paying for a model's prediction.
• Model Marketplaces: Buyers can cryptographically verify a model's provenance and performance claims.
• AI Governance: DAOs can attest to model compliance with specific ethical or technical standards.
• Reproducible Research: Academic papers can link to an on-chain attestation of their published models.

Model attestation provides a cryptographic fingerprint (typically a hash) of an AI model's parameters, architecture, and training data. This fingerprint is stored on-chain, creating an immutable record. This allows anyone to verify that a deployed model is exactly the one claimed by its creator, preventing tampering and ensuring reproducibility. It's the foundational layer for trust in decentralized AI inference and agentic systems.

In decentralized inference networks (e.g., those using Bittensor), model attestation is critical. Before a node is permitted to serve inference requests, it must attest to the specific model it is running. This ensures clients receive predictions from a verified, unaltered model, enabling a trustless marketplace where compute is commoditized but model quality is guaranteed. Attestation acts as the gatekeeper for the inference workload.

Autonomous agents and AI-powered smart contracts rely on model attestation for security. When an agent takes an action based on an AI's decision, the underlying model's hash can be checked on-chain. This creates verifiable causality: the action is a direct, auditable result of a specific, known model's output. This mitigates risks from malicious or unpredictable model updates in agentic ecosystems.

Attestation extends beyond models to include training and input data. Oracles and specialized protocols can attest to the source and integrity of datasets used in training or provided as real-time inputs for inference. This creates a verifiable data pipeline, ensuring that models are trained on authenticated data and operate on trustworthy inputs, closing another major trust gap in on-chain AI systems.

Enables peer-to-peer trading of ML models by providing a cryptographic certificate of authenticity. Buyers can verify they are receiving the exact model advertised, not a tampered version or inferior copy. This facilitates:

• Royalty enforcement via smart contracts triggered by attestation proofs.
• Confidential model inference where the model is attested but its weights remain private.
• Federated learning coordination, where contributions from different parties are verifiably integrated.

Coordinates distributed training (e.g., federated learning) by attesting to the contributions of each participant. This prevents poisoning attacks and ensures the integrity of the aggregated model. The process involves:

• Attesting each participant's local model update.
• Using smart contracts to verify attestations before aggregation.
• Creating a final attestation for the globally trained model, crediting all contributors.

Provides a foundation for third-party auditors to verify a model's behavior against ethical and legal standards. The immutable attestation record allows auditors to:

• Reproduce inference results exactly using the attested model and data.
• Check for algorithmic bias by running standardized fairness tests on the proven model.
• Certify models for use in regulated industries like finance, healthcare, and hiring.

Model attestation relies on public-key cryptography and a Public Key Infrastructure (PKI). The hardware manufacturer (e.g., Intel) acts as a root of trust, issuing certificates for legitimate TEEs. The attestation quote is signed by a key derived from the hardware, allowing any verifier to check its authenticity against the manufacturer's root certificates.

• Core Mechanism: Asymmetric cryptography ensures the attestation report is unforgeable.
• Chain of Trust: Hardware Root Key → Platform Manufacturer Key → Enclave Quote.
• Essential for: Proving the attestation evidence originates from genuine, unmodified silicon.

Zero-Knowledge Proofs represent an advanced, cryptographic alternative to TEE-based attestation. Instead of trusting hardware, ZKPs allow a prover to generate a cryptographic proof that a model produced a given output from a given input, without revealing the model's internal weights or architecture. This enables verifiable inference with potentially stronger privacy and portability guarantees, though at higher computational cost.

• Contrast with TEEs: Trust moves from hardware to cryptography.
• Emerging Use: Projects like zkML (Zero-Knowledge Machine Learning) are exploring this frontier for on-chain, verifiable AI.