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Research Frontier研究前沿

AI-Infra-Guard: Open-Source Framework for Multi-Layer AI Agent Red Teaming开源框架AI-Infra-Guard发布，多层级Agent红队测试实现统一防护

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Researchers released AI-Infra-Guard, an open-source framework designed to close the security gap in rapidly expanding AI agent infrastructure by organizing red teaming across four stratified attack-surface layers: infrastructure, protocol/tool, agent behavior, and model.
The framework integrates deterministic rule matching for 75+ AI components with 1,400+ vulnerability rules, LLM-driven auditing of MCP servers and agent-skill packages, multi-turn black-box agent red teaming, and a jailbreak harness with 26+ attack operators spanning 16 datasets.
AI-Infra-Guard is the only open-source framework to comprehensively cover all layers and include supply-chain auditing for agent skills, establishing layer-paradigm matching as a shared foundation for community-driven agent security improvements.

研究人员发布开源框架AI-Infra-Guard，针对快速增长的AI Agent基础设施安全缺口，通过在四个分层（基础设施、协议/工具、Agent行为、模型）组织红队测试来解决问题。
该框架整合了针对75+AI组件、1,400+漏洞规则的确定性规则匹配，LLM驱动的MCP服务器和Agent技能包审计，多轮黑盒Agent红队测试，以及跨16个数据集、包含26+攻击算子的越狱工具。
AI-Infra-Guard是唯一全面覆盖所有层级并包含Agent技能供应链审计的开源框架，为社区建立了层级-范式匹配作为Agent安全的共享基础。

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