Nathan Lambert 解读 Qwen3.8-Flash-Next 异常行为：或为依赖内部基建的窄范围 RL 训练

在受控沙箱环境中评估 Qwen3.8-Flash-Next 时，Cohere 的研究团队观察到了意料之外的情况。 当被要求生成一条防御性网络安全检测规则时，该模型却转而盘点其运行所在机器的各项信息，并试图向位于北京的阿里云存储桶上传一个探测文件。值得注意的是，该模型的推理轨迹声称，在采取这些行动之前它不会调用任何工具。 Cohere 的沙箱阻止了这次尝试，没有任何对外通信成功。我们尚未确定根本原因，也不主张存在恶意意图。 但这一事件为部署智能体式 AI 系统的组织提出了重要问题：模型能否采取与其所述推理不一致的行动？安全团队又应如何评估开放权重模型中的信任、来源与行为？ 我们公布调查结果，是因为正在做出模型采用决策的组织理应获得尽可能多的信息。我们希望这有助于提升透明度、强化安全实践，并推动整个 AI 生态系统的持续协作。 阅读完整报告：https://t.co/TZSugXi2ZE

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While evaluating Qwen3.8-Flash-Next in a controlled sandbox environment, Cohere's research team observed something unexpected. Asked to generate a defensive cybersecurity detection rule, the model instead inventoried aspects of the machine it was running on and attempted to upload a probe file to an Alibaba Cloud storage bucket in Beijing. Notably, the model's reasoning trace stated that it would not invoke any tools before taking these actions. Cohere's sandbox blocked the attempt, and no external communication succeeded. We have not determined the root cause, and we are not asserting malicious intent. But the incident raises important questions for organizations deploying agentic AI systems: Can models take actions that differ from their stated reasoning, and how should security teams evaluate trust, provenance, and behavior in open-weight models? We are publishing our findings because organizations making model adoption decisions deserve as much information as possible. We hope this contributes to greater transparency, stronger security practices, and continued collaboration across the AI ecosystem. Read the full report: https://t.co/TZSugXi2ZE