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Oct 13 · Tuesday
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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 SAFESHIELD，组织小语言模型部署时的安全决策
Published Oct 6
Evaluated models Qwen3-4B-Instruct Abstract SAFESHIELD 用可审计决策轨迹组织 SLM 部署时的四项安全决策，并报告协调依赖影响端到端结果。SAFESHIELD 是面向小语言模型的部署时安全框架，把运行时安全从单项审核改写成可检查的决策过程。
提出免训练框架 SED，将有害智能体轨迹蒸馏为可检索安全策略
Published Sep 29
Evaluated models DeepSeek-V4-Flash , GLM-5.2 , Kimi K3 系统梳理智能体执行链路中的越狱攻击与防御并比较推理时防御
Published Sep 11
Evaluated models Qwen3.5-4B , Llama-3.1-8B-Instruct Abstract SoK 将越狱重放到智能体流水线，并用两种模型检验回复层防御的边际与代价。Mia 等人把越狱安全从单轮回复扩展到智能体的规划、记忆、工具和智能体间通信，并问既有结论在更强原生对齐下是否还成立。
提出 CFD 跨会话分解攻击，利用工件来源缺口绕过工具型 Agent 的本地检查
Published Jun 8
Evaluated models GPT-4.1 , GPT-5-nano , Claude-3.5-Sonnet and others6 Abstract CFD 用无指令 artifact 把越狱拆到跨会话。CFD 针对工具型 LLM 智能体：利用 artifact 来源不被跟踪这一部署失败模式，把有害目标拆开后延迟组合。
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