Anthropic has unveiled FlashProve, an open-source cryptographic system designed to verify whether an AI model actually generated a response without revealing its proprietary weights. The technology could help users, independent auditors and regulators confirm that providers are running the models they claim to serve.

The system uses zero-knowledge cryptography to generate mathematical proofs that a specific model produced given output tokens in response to a particular prompt. This could address a growing trust problem as companies increasingly rely on third-party model providers and routing services to access advanced AI systems.

Today, users have no reliable way to establish whether a response came from the model they selected or whether a provider secretly substituted a cheaper alternative. FlashProve aims to provide that assurance without requiring users to access or independently run the model.

FlashProve uses a cryptographic technique called a zero-knowledge succinct non-interactive argument of knowledge (zkSNARK). It allows a provider to demonstrate that a computation was performed correctly without revealing sensitive information about the underlying model.

The system works in two stages. First, the provider publishes a cryptographic commitment to its model weights, effectively creating a fingerprint of the exact model being used while keeping the weights private.

When a user submits a request, the provider can subsequently generate a proof that the committed model produced the corresponding output tokens. Anyone with the commitment, prompt and response can verify the proof without accessing the model weights.

Anthropic demonstrated the approach using Llama 4 Maverick, a 400-billion-parameter mixture-of-experts model. Running on a single NVIDIA H100 GPU, FlashProve generated proofs at approximately 110 tokens per second, with each proof measuring around 19 MB for a 1,024-token request.

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