作者介绍了USAP，一个旨在通过强制执行“证据门”模式来提高AI安全判决可验证性的系统。该模式要求每个判决都必须得到至少一个可解析来源的支持，例如CVE、外部feed或操作员产物。这一严格要求带来了三个关键结果：连接器必须抽象化以适应不同的安全工具；数值分数必须能直接从引用的证据计算得出，而不是叙述性的；系统不能根据自身的输出来给自己打分。USAP是开源的，可以作为MCP服务器运行，或集成到现有的模型提示中。 AI
影响 通过确保判决有可验证的证据支持，增强了AI安全分析的信任度和可靠性。
排序理由 该条目描述了一个用于AI安全分析的新开源工具。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
<p>Every "AI security analyst" I tried had the same flaw: a correct verdict and a confident-but-wrong one are indistinguishable on screen. In security that's not a UX nit — it's the whole problem. So I built USAP around a single rule, and this post is about that rule and three th…
<p>Every "AI security analyst" I tried had the same flaw: a correct verdict and a confident-but-wrong one are indistinguishable on screen. In security that's not a UX nit — it's the whole problem. So I built USAP around a single rule, and this post is about that rule and three th…