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Oct 12 · Monday
Agent safety · Papers Found 12 papers “
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 PASAC and a five-layer boundary guarantee stack, turning agent evaluation overstepping into verifiable whole-system guarantees.
Published Oct 9 提出两阶段门控审计框架，在执行前阻断有害智能体动作
Published Oct 6
Evaluated models Claude Sonnet 5.5 , Claude Haiku 4.5 提出 VoxParity，固定转写只改音频，评测语音智能体是否按行业规则改工具调用
Published Sep 28
Evaluated models MiMo-V2.6-Pro , gemini-3.7-flash , Qwen3.8-Omni and others15 Abstract VoxParity 用固定转写的音频对比和 words-only null，测量语音智能体是否按所听内容改变工具调用。VoxParity 测量语音智能体在词语不变、只有声音改变时，是否按行业规则改它所执行的工具调用。
用五阶段蒙特卡洛分析奖励作弊如何导致 Agent 围堵失效
Published Sep 26 Abstract 分层控制的模拟事件率低于弱控制以及单独隔离或单独监控。这项研究用蒙特卡洛把奖励黑客接到外部安全事件上，比较的是控制组合，不是 Hugging Face 事件的再现概率。
提出移动 Agent 轨迹审计器 MATE，判断轨迹是否违反可编辑安全策略
Published Sep 19
Evaluated models MATE-0.5B , MATE-1.5B , MATE-3B Abstract MATE 用合成轨迹微调轻量审计器，在可编辑策略下判断移动智能体轨迹是否违规并给出解释。MATE 是面向移动 GUI 智能体的轻量策略条件轨迹审计器，在给定自然语言安全策略时判断轨迹是否违规并解释原因。
提出 k-robust 联盟对齐，刻画误对齐评审下的安全委托授权
Published Sep 15 Abstract 联盟覆盖使无个体对齐的评审面板仍可相对基线安全授权，数值分数改善完备性。委托授权的安全不要求每位评审者对齐，而要求面板在效用空间覆盖委托人。
提出 HazardAuditor，用可执行轨迹训练计算机使用 Agent 的运行时守卫
Published Sep 14
Evaluated models HazardAuditor , HazardAuditor-SFT 提出结合 Scientist AI 监督与系统级控制的智能体保障架构
Published Sep 9 Abstract 作者梳理事件中的多层失效，主张监督模型、系统控制与证据治理并用。Lu 与 Bengio 给出的是一套可逐层上移的保障架构和一组事件归类，不是已测量的防御系统。
提出 ClawSentry 多层监控网关，在技能准入和运行时拦截恶意技能与隐藏指令
Published Aug 21
Evaluated models Codex/GPT-5.4 , Codex/GPT-5.5 , Claude Code/GLM-5.1 and others6 Abstract ClawSentry 用包准入、三级运行时门控和反绕过，压低 skill 攻击并保住干净任务。ClawSentry 是挂在智能体运行时之外的安全监督网关，用来同时盖住 skill 执行的多个生命周期边界，并把已被拒绝的效果记到会话里。
提出 Janus 前瞻守卫，在长程智能体执行有害动作前拦截潜在风险
Published Jul 22
Evaluated models Vanguard Proposes out-of-process dual-channel comparison classification to intercept erroneous clicks by computer-use agents
Published Mar 16
Evaluated models multi-modal-embed-small Abstract Perception errors can be exploited by swapping screen pixels.The authors frame CUA's perception failure as a security vulnerability, visual confused deputy : the agent authorizes click(x,y) based on a misread screen, and the actual landing point differs from the element it believes it is clicking.
提出 Aura，用可信内核中介移动 Agent 的身份、感知与动作控制
Published Feb 11
Evaluated models Gemini-3-Flash Abstract Aura mediates structured interactions via the Agent Kernel.Aura uses kernel-mediated structured agent interaction, replacing screen-interface mobile assistants. .
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