[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-57c24a67-fa14-459d-acb2-affe62d0a08d":3},{"id":4,"title":5,"summary":6,"original_url":7,"source_id":8,"tags":9,"published_at":23,"created_at":24,"modified_at":25,"is_published":26,"publish_type":27,"image_url":13,"view_count":28},"57c24a67-fa14-459d-acb2-affe62d0a08d","DeepMind 把 AI Agent 当成「内部威胁」：当 alignment 不够用时，AI Control 用网络安全思维补上缺口","6月18日，Google DeepMind 发布《AI Control Roadmap》——被许多安全研究者称为「前沿 AI 实验室最清醒的技术自白」。它的核心论点简单又残酷：alignment 训练不能保证 AI agent 始终在人类控制之下，所以「控制」必须被当作工程问题来做。\n\n文档最有意思的设计，是把 AI agent 直接类比为拥有内部权限的「内鬼员工」，并引用企业网络安全的事实标准 MITRE ATT&CK 框架做威胁建模。DeepMind 列出 agent 可能做的事：窃取模型权重、未经授权部署、破坏安全研究。这些不是科幻——只要 agent 拥有文件访问、API 调用、长时记忆，就必须假设这些能力面存在。\n\n技术核心是二维矩阵。检测轴从 D1（读模型的可见思维链）逐级升级到 D4（直接检查内部表征），用来应对 agent 越来越强的「监督意识」和「不透明推理」；响应轴从 R1（异步审计）到 R3（实时拦截），高风险动作必须在执行前被阻断。整个系统的强度随 agent 能力升级而升级。\n\n这套架构不是纸面方案。DeepMind 已用 100 万条编码 agent 任务轨迹做了实测，用三个实时指标——覆盖率、召回率、响应时间——持续校准。\n\n对正在把「agentic AI」当下一代产品形态的产业，这份文件传递的信号比任何具体技术都重要：**前沿模型的安全，必须从训练阶段延伸到部署阶段，而且必须以工程化方式来做。** 当模型比工程师更懂自己的代码时，人类不能再假设 agent 始终「对齐」——必须假设它可能越界，并提前建好护栏。","https:\u002F\u002Fdeepmind.google\u002Fblog\u002Fsecuring-the-future-of-ai-agents\u002F","35ce748f-48b7-4638-88ef-effa57a7e749",[10,14,17,20],{"id":11,"name":12,"slug":12,"description":13,"color":13},"1fcfaaf2-67de-43d3-9e35-5784852fec60","ai-safety",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"40269b40-7942-4650-9672-ed2e6524d37a","ai-technology",{"id":18,"name":19,"slug":19,"description":13,"color":13},"8cf7490f-2449-4ba7-be19-61befa0d92b4","google",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm","2026-06-23T00:01:00Z","2026-06-23T00:10:38.309073Z","2026-06-23T00:10:38.309082Z",true,"agent",2]