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Oct 13 · Tuesday
Another 227 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.
评测 coding agent 在非公开漏洞上的利用生成、修复与后续攻击
Published Oct 6
Evaluated models Opus 5 , GPT-5.6 Sol , GPT-6 Sol and others10 Abstract 攻防孰强随环境、系统和弱点改变。作者比较编程智能体在利用生成与漏洞修复上的自主表现，并检查修复之后还能不能被攻破。文中的 help 指自主完成任务，不是提升人类操作者。
提出 MobileCybench，用可执行探针评测 Agent 的漏洞发现能力
Published Sep 21
Evaluated models OpenCode/GPT-5.5 , OpenCode/GPT-5.6-Sol , OpenCode/GLM-5.2 and others5 提出 SRE-Bench 评测智能体的真实规模二进制逆向能力
Published Aug 12
Evaluated models GPT-6-Astra , GPT-5.6-Sol , GPT-5.6-Cyber and others11 Abstract SRE-Bench 用从零编写的大规模保护二进制评测智能体 RE，公开设置下多数模型完全解开比例不高。作者构建并评测 SRE-Bench ，一个从零编写、用于测量智能体逆向工程而非利用开发的基准，并称它同时满足污染控制与真实软件规模。
用 CryptanalysisBench 评测大模型的端到端密码分析能力
Published Jul 21
Evaluated models Claude Opus 4.8 , Claude Sonnet 5 , Claude Mythos 5 and others10 Abstract 提出真实密码原语分析基准，证实前沿模型具备显著分析能力并能发现未公开设计缺陷。
提出工作流级越狱，让编程 Agent 在开发任务中写入有害示例
Published Jul 5
Evaluated models Claude Sonnet 4.6 , Claude Haiku 4.5 , Gemini 3.1 Pro and others4 Abstract 作者报告 Copilot 后端会在编码工作流中生成其在单轮条件下通常拒绝的有害答案。这是一项生产 IDE 编码智能体的工作流越狱研究 ，受测对象是 Copilot 的四个模型后端，而非评测流水线中的名义目标。
构建真实网络靶场基准，评测 AI 的端到端自主攻击能力
Published Jun 12
Evaluated models GPT-5.5 , Claude-Opus-4.7 , GLM-5.1 and others7 Abstract 构建开放多靶场评测基础设施 AGENTCYBERRANGE，测定前沿 AI 网络攻击能力并揭示其端到端规划短板。
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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