双盲评测：用安全飞地同时保护评测提示词与模型权重

论文团队进行了首次试点：在Google Cloud Platform的H100安全飞地中，用AILuminate基准的私有题库评测Google DeepMind的专有模型Gemini 2.5 Flash Lite。作者称这是首次有技术方案同时实现一方前沿专有模型权重与另一方封闭基准的互相保密。

对第三方评测机构和监管审计而言，这类方案可能降低接触闭源模型时提示词泄露的风险，也为模型厂商接受独立安全审计提供新的技术路径。

Double Blind Evals: Resolving the Dual Confidentiality Dilemma in AI Safety Auditing

AI safety evaluations often involve sensitive prompts and proprietary models. Evaluators want to keep their evaluation prompts secret from model owners to prevent purposeful or incidental data leakage, ensuring longer usefulness of their benchmark. Model owners in turn wish to keep their proprietary model weights and inference code within their own secure infrastructure to protect their IP. We present a way forward for "double-blind evals", which provide cryptographic guarantees that an evaluator's prompts are not shared with the model owner, while simultaneously maintaining the confidentiality of the model weights and inference code. To accomplish this, we use secure enclaves that encrypt data in RAM at the hardware level. In a world-first pilot, we test this approach by evaluating a proprietary Google DeepMind model (Gemini 2.5 Flash Lite) against a selection of the private AILuminate benchmark corpus, using an NVIDIA H100 secure enclave on Google Cloud Platform, and using OpenMined's PySyft to maintain data and model privacy. We believe this effort marks the first use of this technology to provide mutual secrecy for one organization's frontier-class proprietary model weights and another organization's closed benchmark.

本条情报归属 AITOP「论文」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」「中美大模型技术代差与商业化对照」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：AILuminate、Gemini 2.5 Flash Lite、PySyft、AI安全，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。

信源核验说明：本文原始信息发布自一手权威信源「arXiv: Google DeepMind」（Tier-1 一手发布），具备高确定性参考价值。