The Hidden Cost of 'Open Source' AI Without Provenance

Public datasets like Common Crawl are riddled with copyrighted material, PII, and malicious code snippets. Training on this unvetted data creates legal liability and model instability.

• Billions of tokens of contaminated text ingested.
• >30% of 'The Pile' dataset is of ambiguous copyright status.
• Creates backdoors and biases that are impossible to audit post-training.

A downloaded .safetensors file has zero cryptographic proof of its training lineage. You cannot verify if it's the authentic Llama 3 or a finetuned spyware model.

• Enables supply chain attacks on the entire OSS AI ecosystem.
• Zero accountability for embedded biases or performance claims.
• Makes reproducible research and forking a matter of blind trust.

Anchor model checkpoints and dataset hashes to a public ledger like Ethereum or Arweave. This creates an immutable chain of custody from raw data to final weights.

• Enables cryptographic verification of model lineage.
• Allows selective exclusion of contaminated data sources in future forks.
• Provides a legally defensible audit trail for copyright and compliance.

Current 'open' AI models lack cryptographic proof of their training lineage, making them vectors for hidden backdoors and data poisoning. This undermines trust in the entire AI supply chain.

• Undetectable Backdoors: Malicious weights can be inserted during training or fine-tuning with zero audit trail.
• Supply Chain Attacks: A single compromised model on Hugging Face can propagate to thousands of downstream applications.

Model creators and data providers cannot prove contribution or enforce licensing without cryptographic attestation, destroying economic incentives for open development.

• No Royalty Enforcement: Models like Stable Diffusion are forked and commercialized with no compensation to original researchers.
• Fake Provenance: Anyone can claim a model is 'ethically sourced' without verifiable proof of data origin.

Without proof of execution, you cannot verify if a model was trained on claimed hardware (e.g., ethical GPUs) or if inference outputs are genuine, opening the door to low-cost spoofing.

• Hardware Spoofing: Claims of training on NVIDIA H100 clusters are just text in a README file.
• Inference Fraud: Cheap, low-quality model outputs can be falsely attributed to expensive, high-fidelity models.

Protocols like EigenLayer AVS and Hyperbolic enable cryptographically signed attestations for each layer of the AI stack, creating a verifiable chain of custody.

• Data Provenance: Zero-knowledge proofs verify dataset origin and licensing terms.
• Model Fingerprinting: Each model checkpoint gets a unique, immutable identifier on a Celestia data availability layer.

Networks like Ritual and io.net combine decentralized physical infrastructure (DePIN) with cryptographic proofs (zk or TEEs) to guarantee execution integrity.

• Proof-of-Inference: Cryptographic receipts prove a specific model generated an output.
• Ethical Compute Proofs: Attestations verify training ran on permissionless, non-captive hardware.

Smart contracts on Ethereum or Solana automate royalty streams and access control based on verifiable attestations, creating a sustainable open-source economy.

• Automated Royalties: Fees flow to provable contributors via Sablier streams upon model usage.
• Access Tokens: Token-gated model access based on compliance with verified licensing terms.