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Oct 12 · Monday
All papers Found 11 papers Another 247 papers do not have category tags yet and are not included in filtered results.
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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.
Proposes LGWM, using an action-conditioned world model to verify GUI agent screen transitions.
Published Oct 8
Evaluated models LGWM 评测编码智能体 harness 安全机制在对抗任务中的防护效果
Published Oct 6 构建可组合工具包 pikit，评测编码智能体的间接提示注入
Published Sep 29 Abstract pikit 提供组合式间接注入评测。pikit 是面向间接提示注入研究的组合式工具包 ，主要贡献是组织攻击、投递通道、防御和判定流程，而非提出新的攻击优化算法。
用 DIBench 评测移动 GUI 智能体被 UI 注入后的决策完整性
Published Sep 29
Evaluated models Qwen2.5-VL(32B) , Qwen3-VL(32B) , GUI-Owl(32B) and others7 Abstract 论文提出移动智能体决策完整性基准 DIBench，发现欺诈 UI 注入诱导目标选择并推高完成率，通用防御收益不稳定。
提出 CAVE-BENCH，评测智能体在虚假指责下破坏已完成的正确工作
Published Sep 26
Evaluated models Claude-Sonnet-5 , Claude-Opus-5 , GPT-5.6-Sol and others14 Abstract 论文提出了评估智能体在虚假指责下破坏已有正确成果的 CAVE-BENCH，发现强模型易推翻已知证据，提出了门控缓解方案。
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。 。
Proposes IssueTrojanBench to evaluate coding agents' execution of malicious issues
Published Jul 23
Evaluated models GPT-5.3 Codex , GPT-5.4 , Sonnet 4.6 评测编码 Agent 记忆文件中提示注入的跨会话影响
Published Jul 16
Evaluated models Claude Haiku 4.5 , Claude Opus 4.7 , GPT-5.2 and others4 Abstract 已植入记忆文件的载荷可以跨会话影响 Claude Code 与 Codex，攻击成功与载荷是否留下是两种不同的失败。Bad Memory 在沙箱合成工作区里评测文件型持久记忆上的提示注入，比较载荷能否改变当前会话，以及能否在后续会话中留下或叠加。
提出 AgentHazard 基准，评测计算机使用智能体的执行层有害行为
Published Apr 3
Evaluated models Qwen2.5-72B-Instruct , Qwen2.5-Coder-32B-Instruct , Qwen3-32B and others12 Abstract AgentHazard 评估了计算机使用智能体的执行安全，作者认为模型层对齐无法可靠保障智能体层面的安全性。AgentHazard 是针对计算机使用智能体执行层安全构建的评估基准，重点探究多步工具调用下局部合理动作累积引发的危害。
用 MobiRed 评测移动 LLM Agent 面对第三方渠道的提示注入
Published Oct 31, 2025
Evaluated models Mobile-Agent-E , AppAgent , AutoGLM and others8 Abstract 论文评估了移动 LLM 智能体作为 confused deputy 的易受攻击性，作者称非应用控制通道可诱导智能体执行多步越权危害。
提出 JAWS-Bench，评测代码 Agent 在三级工作区中的越狱与恶意代码可执行性
Published Oct 2, 2025
Evaluated models GPT-4.1 , GPT-o1 , DeepSeek-R1 and others8 Abstract 论文提出了评估代码智能体端到端恶意代码越狱的 JAWS-Bench，发现多文件与智能体推理会明显放大可部署危害。
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