How an inference provider can prove they're not serving a quantized model (tinfoil.sh)

Tinfoil has introduced Modelwrap, a groundbreaking tool designed to ensure that users know exactly which machine learning model weights are being served during inference API calls. This innovation addresses a crucial challenge within the AI/ML community: the inability to verify whether the models being provided are the exact weights as published and not altered or quantized versions. Modelwrap employs cryptographic commitments combined with secure hardware enclaves to guarantee that model weights are untampered and verifiable at runtime, thus enhancing trust in AI models provided by both open and closed-source vendors. Modelwrap operates through a robust system involving Merkle trees and dm-verity. It calculates a public commitment to model weights, binds this commitment to the inference server, and allows clients to verify that the correct weights are being used. This process incorporates runtime verification during every read operation from disk, ensuring that any alteration to the model data will result in an immediate error. By enabling high integrity guarantees without requiring significant changes to existing infrastructure, Tinfoil's Modelwrap not only improves transparency in AI model delivery but also marks a significant advancement for both developers and end-users in the AI/ML landscape.