把事故翻译成保单：真正昂贵的是理赔灰区AI风险不是一个新险种，而是把多个传统险种的触发边界搅在一起。这也是为什么企业会同时看到两个看似矛盾的现象：一方面觉得我买了网络险或责任险就够了；另一方面承保人却越来越倾向于用更硬的条款把AI相关风险切出去，或要求加买独立附加险。问题不在AI是否危险，而在它让很多损失同时落在多张保单的边界线上，而保险最怕边界不清。
17:44, 27 февраля 2026Бывший СССР
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This does not mean confusables.txt is wrong. It means confusables.txt is a visual-similarity claim that has never been empirically validated at scale. Many entries map characters to the same abstract target under NFKC decomposition (mathematical bold A to A, for instance), and the mapping is semantically correct even if the glyphs look nothing alike. But if you treat every confusables.txt entry as equally dangerous for UI security, you are generating massive false positive rates for 96.5% of the dataset.
斯坦福和耶鲁的研究者发现，Claude 3.7 Sonnet 在特定条件下会以 95.8% 的准确率「近乎逐字逐句」地输出《哈利波特》等受版权保护的作品——这不仅与 Anthropic 长期以来关于「模型只是学习了语言规律」的说法背道而驰，更让该公司对任何人的「蒸馏」指控显得缺乏底气。
Defense in depth on top of gVisorgVisor gives you the user-space kernel boundary. What it does not give you automatically is multi-job isolation within a single gVisor sandbox. If you are running multiple untrusted executions inside one runsc container, you still need to layer additional controls. Here is one pattern for doing that: