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Oct 12 · Monday
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About this collection Collection in progress We are building a large AI safety paper collection to help researchers find work related to their interests. Papers are collected automatically andfiltered only for relevance to AI safety , without judging quality, novelty or reading value. The default “Annotated first” shows papers with tags or published paper analyses first. Each group is ordered by original publication date, newest first, with undated papers last. “Newest first” uses publication dates only. This order is not a quality ranking.
One-line summaries, tags and paper analyses are prepared by AI from the papers. Language models may have systematic biases and produce hallucinations.We plan to carry out human annotation and review to reduce errors in this content.
构建 EvoRiskBench 评测工作区 Agent 的运行时安全风险
Published Oct 2
Evaluated models GPT-5.6 Sol , DeepSeek-V4-Pro-0813 , Claude Opus 5 Abstract 论文构建了工作空间智能体运行时风险基准 EvoRiskBench，发现高执行力智能体在 IPI 下面临较高安全风险。
提出 PACE，在工具调用前做路径隔离与效果授权
Published Oct 1
Evaluated models gpt-5.6-luna , DeepSeek-V4-Flash , Qwen-3.8-27B Abstract PACE 在工具边界做路径割与效果核验。PACE 是面向工具调用型 LLM 智能体的运行时调解：当准入证据无法区分会改变行为的规格时，不在出口站点上撤销调解，而在最后一次工具调用前同时检查表示出的影响路径和请求权限。
构建 ACE 语料，评测内核级证据对 Agent 攻击检测的有效性
Published Sep 24
Evaluated models AdaBoost , XGBoost , TabPFN and others10 Abstract ACE 用配对的内核与应用层证据表明，syscall 痕迹可判别智能体攻击，两层组合通常更好。ACE 是一份把智能体会话的内核 syscall 痕迹与应用层清单、转录配对起来的检测语料和基准。 。
提出企业 MCP 零信任授权与发现扩展，在架构上拒绝提示注入下的越权调用
Published Sep 19
Evaluated models gpt-5 , claude-sonnet-4-6 , claude-opus-4-7 and others6 Abstract 同一份按工具声明同时管 MCP 的发现、列表和调用，使注入不能扩大已有凭证的权限。作者把零信任放在 MCP 工具调用边界：智能体即使被提示注入，也不能调用其凭证范围之外的工具。问题来自三点部署性质。调用意图会随不可信上下文改变。
提出 IntentCap，按来源字段所有权合成并裁决 Agent 能力租约
Published Sep 14 Abstract 四源字段租约约束副作用。IntentCap 是把智能体能力绑到当前任务意图、并按上下文来源划分字段所有权的权限机制。
提出企业级检测系统 ADR，识别编程 Agent 的提示注入与恶意 MCP
Published May 17 Abstract 提出了端点因果遥测、两级在线分流与离线红队闭环的 ADR 系统，并在生产环境中验证了有效性。
提出 ClawSafety，评测个人智能体读到对抗工件后是否执行有害动作
Published Apr 2
Evaluated models Claude Sonnet 4.6 , GPT-5.1 , Gemini 2.5 Pro and others5 Reviews threats to autonomous agents and proposes a full-lifecycle defense blueprint, FASA
Published Mar 13 Abstract Using OpenClaw as an example, it divides risks into three layers and uses FASA to describe defense from input to system execution.This is a threat review and defense architecture report using OpenClaw as a case study, not a scored attack or defense evaluation.
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