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这些AI攻击面,才是最难收敛的作用是什么？用户访问大模型，很多并非直连官方服务，而是依赖LiteLLM、One API、自研Proxy等中转站进行转发。攻击面在哪？被劫持或者使用攻击者提供的中转站。除了能看见交互内容，更危险的是还能修改返回命令。攻击面在哪？Agent可以执行任务，它处理的所有内容来源都是攻击面：IM消息、GitHub Issue、Jira工单、邮件……风险有哪些？用自身的软件、工具、接口等合法读写权限，执行恶意命令。不过，并非所有Agent都会成为攻击面，需要满足一套风险公式。有哪些基础设施？AI的落地需要大模型网关、MCP Server、中转站、Ollama等模型服务、Dify及n8n等AI工作流平台……这些都是基础设施。攻击面在哪？AI基础设施包含复杂的供应链，因此漏洞、投毒、不安全配置都会带来攻击面，例如最近的LiteLLM、Axios供应链投毒。面对这么多风险，AI安全的第一步不是如何拦截，而应该是资产化。看不见资产，根本谈不上安全治理。AI资产及攻击面梳理、收敛策略、攻击手法预测，直播解读。直播时间：5月13日下午14:00，欢迎微信扫描二维码报名。
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夜雨聆风
