突破传统二分类安全护栏，从输出分布中校准LLM风险概率，为AI安全提供量化新思路。
arXiv:2608.10621v1 Announce Type: new Abstract: Recent research on Large Language Model (LLM) safety has widely adopted guardrails to identify unsafe …
系统评估大模型多步工具调用中的安全护栏，为Agent可靠性提供关键基准。
arXiv:2604.07223v2 Announce Type: replace-cross Abstract: As large language models (LLMs) evolve from static chatbots into autonomous agents, the prim…
重磅论文揭露：主流LLM Agent评估框架忽略静默故障，提出轻量黑盒审计方案，精准检测恶意拒绝漏洞
arXiv:2607.19449v1 Announce Type: new Abstract: Evaluation frameworks for tool-augmented LLM agents focus overwhelmingly on capability metrics or expl…
为LLM应用量身定制的轻量级安全护栏，用小模型实现高效防护。
arXiv:2607.18268v1 Announce Type: new Abstract: Real-world applications that use closed-source large language models (LLMs) need advanced safety measu…
被ICML 2026接收，研究如何通过高效学习构建AI合规护栏，兼顾安全与性能。
arXiv:2510.03485v2 Announce Type: replace Abstract: Autonomous web agents are increasingly deployed for long-horizon tasks, yet their ability to adher…