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Oct 12 · Monday
Another 254 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.
提出 COBRA 双 LLM 架构，防御计算机使用 Agent 的分支转向攻击
Published Oct 2
Evaluated models gpt-5 , UI-TARS-1.5-7B , claude-sonnet-5 and others6 Abstract 提出分支转向防御架构 COBRA，通过事前能力约束与边界代理消除分支转向攻击并兼顾效用。
提出 ZoneClaw，用记忆分区防御计算机使用 Agent 的持久记忆攻击
Published Oct 1
Evaluated models Claude Sonnet 4.6 , GPT-5.5 , GLM-5.2 and others4 提出进程外双通道对比分类，拦截计算机使用智能体的错误点击
Published Mar 16
Evaluated models multi-modal-embed-small Abstract 感知错误可被屏幕像素对调利用。作者把 CUA 的感知失败写成安全漏洞visual confused deputy ：智能体按看错的屏幕授权 click(x,y)，真实落点与它以为的元素不一致。
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。
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