Anthropic introduces FlashProve to verify whether a response came from a given AI model
Overview
Anthropic introduces FlashProve, an open-source cryptographic tool that lets developers verify whether a given response came from a given AI model, and posts the code on GitHub.
Anthropic says FlashProve is substantially more efficient than prior open-source methods, and that proofs could make chat hijacking harder and let safety evaluators confirm they are testing the model served to customers.
Rohan Paul, in a separate X post, reports benchmark figures: a single Nvidia H100 produced proofs for Meta's Llama 4 Maverick at 110 tokens per second, with each proof about 19MB, and a regular CPU checked a proof in about 17 seconds. Paul says the proofs could catch resellers serving a cheaper model than the one advertised. These efficiency and reseller claims come from Paul's post, not from Anthropic's announcement.
Written by AI from the articles below · updated Nov 3, 3:35 PM ET
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The articles in this story. Times are ET.
Rohan Paul@rohanpaul_aiXPickAnthropic open-sources FlashProve to verify which AI model wrote a replyAIAnthropic has open-sourced FlashProve, a cryptographic system that lets a server prove which AI model produced a given response. The post says it is far more efficient than prior open-source methods, and that a single Nvidia H100 produced proofs for Meta's Llama 4 Maverick at 110 tokens per second, each about 19MB, which a regular CPU checks in about 17 seconds. The author says the proofs could catch resellers serving a cheaper model and let safety evaluators confirm they are testing the model customers receive.

Anthropic@AnthropicAIOfficialPickAnthropic introduces FlashProve for verifying whether AI responses came from a given modelAIAnthropic has introduced FlashProve, a tool that lets developers verify whether a given response came from a given AI model, building on established cryptographic techniques. The company says it is substantially more efficient than prior open-source methods, and that proofs could make chat hijacking harder and let safety evaluators confirm they are testing the model served to customers. The code is open-sourced on GitHub.
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