原标题：Anthropic is working on a system to prove AI models’ identity
Anthropic 发布开源系统 FlashProve，通过可证明推理为模型输出提供密码学证明，验证其确实来自指定模型。系统要求 AI 实验室发布不泄露模型权重的指纹，可在本地设备完成轻量验证，用于防止模型被偷换或冒充。研究仍在进行中，技术规范和代码已在 GitHub 公开，Anthropic 暂无部署计划。
原文说明了 FlashProve 的验证机制与开源发布状态，读者可以据此判断可证明推理对模型可信度的实际意义。
本页的摘要、推荐理由与解读出自 AIsa Studio 的 AI 风向智能体，原文版权归原作者所有。
Anthropic is working on an open source system to verify which AI model produced a given output, according to a blog post published Tuesday. The system, called FlashProve, is built to provide what researchers call “provable inference” — a cryptographic proof, essentially a mathematical guarantee, that a given output came from a specific model. It’s meant to let users confirm that the answer they received really came from the model they think they’re using.
Similar to cryptographic email signatures that let recipients confirm a message really came from its claimed sender, FlashProve would be robust against impostors attempting to disguise one model as another, even when the impostor is the lab that built and runs the model. In particular, the researchers note that the system could be used to prevent an AI company from secretly swapping in a cheaper model to cut costs, or a more expensive model to inflate its performance results.
FlashProve could also be used to verify claims by third parties that a specific task was done by an advanced model. “A developer might claim that their software library was scanned for vulnerabilities by a frontier cybersecurity model, or a company might claim that its business plan was given the go-ahead by an appropriate model,” the researchers write. “How can a user verify these claims?”
To work, the system requires AI labs to publish a “fingerprint” of each model they release that can later be matched to specific outputs and verified cryptographically. Crucially, the fingerprints don’t reveal a model’s weights, the internal numbers that define how it behaves and which closed-model makers like Anthropic and OpenAI keep private. That means labs could publish the fingerprints without giving up their most valuable asset, enabling the system to work with commercial models, too. The system is also designed to be lightweight, so verification could be performed on a local device.
As part of the announcement, researchers released a detailed technical specification and working code for various elements of the system, which is publicly available under an open source license on GitHub. Research in the system is ongoing, and there are no plans to deploy it at Anthropic or any other lab.
“This research post is meant to share intermediate progress, and not a completed research agenda,” researchers explain in the announcement post. “We are sharing this explicitly with the goal that others look at what we have done, take it, and improve it.”
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Russell Brandom has been covering the tech industry since 2012, with a focus on platform policy and emerging technologies. He previously worked at The Verge and Rest of World, and has written for Wired, The Awl and MIT’s Technology Review. He can be reached at [email protected] or on Signal at 412-401-5489.
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2026 年 11 月 3 日，Anthropic 宣布推出可证明推理研究成果 FlashProve，并同步开源代码。该技术基于既有密码学方法，可为模型输出生成密码学证明，使开发者能够验证某条回复确实来自指定的 AI 模型，效率显著高于此前的开源方法。据 Anthropic 官方介绍，FlashProve 可帮助防御聊天劫持攻击，或让安全评估者确认被测模型与线上服务模型一致；作者认为可证明推理的进一步进展将有助于提升 AI 部署的可信度。同日稍晚，TechCrunch 对此进行了跟进报道，称该系统要求 AI 实验室发布不泄露模型权重的指纹，验证可在本地设备上轻量完成，用于防止模型被偷换或冒充。TechCrunch 报道称，该研究仍在进行中，技术规范和代码已在 GitHub 公开，Anthropic 暂无部署计划。两篇报道在核心技术事实（基于密码学、验证模型输出来源、效率优于此前开源方法、代码开源）上一致，未发现矛盾之处。目前事件停留在研究发布与代码开源阶段，尚无部署进展的后续报道。
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