Anthropic’s FlashProve uses zero-knowledge proofs to verify large AI model outputs

The open-source research prototype proves which model produced a response without exposing its weights, though the proving overhead remains steep Every time you send a prompt to an AI model, you are trusting that the model on the other end is the one you were promised. Until now, you had essentially no way to check. Anthropic wants to change that. The company released FlashProve, a system built on a hash-based zkSNARK that can verify outputs from large language models without revealing the model’s weights. Unveiled on November 2, 2026, the open-source research prototype targets a problem most users never think about. Nobody can currently confirm whether a response came from the model a developer claims, or from a cheaper one a routing service swapped in to save money. An attacker tampering with the token stream is another possibility. The person typing the prompt would never know. How FlashProve works A zkSNARK, short for zero-knowledge succinct non-interactive argument of knowledge, is a type of cryptographic proof. It lets one party demonstrate that a computation was performed correctly without revealing the private inputs behind it. FlashProve runs in two phases. First, during a...