Skip to content

Translation in progress Some entries still include text in the original language.

Oct 12 · Monday

All papers

Found 21 papers

Another 240papers do not have category tags yet and are not included in filtered results.

  1. DefensesarXiv 2610.03089

    COBRA: A Dual-LLM Architecture for Defending Computer-Using Agents Against Branch-Jumping Attacks

    提出 COBRA 双 LLM 架构,防御计算机使用 Agent 的分支转向攻击

    Published
    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。

    Paper analysisRead analysis
  2. DefensesarXiv 2610.01349

    PACE:面向工具调用 LLM Agent 的来源感知能力强制执行

    提出 PACE,在工具调用前做路径隔离与效果授权

    Published
    Setting
    AI Agent
    Evaluated models
    gpt-5.6-luna, DeepSeek-V4-Flash, Qwen-3.8-27B
    AbstractPACE 在工具边界做路径割与效果核验。

    PACE 是面向工具调用型 LLM 智能体的运行时调解:当准入证据无法区分会改变行为的规格时,不在出口站点上撤销调解,而在最后一次工具调用前同时检查表示出的影响路径和请求权限。

    Paper analysisRead analysis
  3. DefensesarXiv 2610.02569

    Pincer:用数字分身学习用户偏好并执行动态最小权限

    为 Agent 提出资源层授权系统 Pincer,用数字分身执行动态最小权限

    Published
    Setting
    AI Agent
    Evaluated models
    Claude Haiku 4.5, Muse Spark 1.2 Contributor
    AbstractPincer 在资源层以三票合取做授权。

    Pincer 是加在现成 coding agent 上的资源层授权防御:用只受用户侧信息影响的 digital twin 做动态最小权限,并与 tool-call 层防御并存。要处理的是用户维护策略带来的权限蠕变和授权疲劳,以及不学习用户策略的 tool-call 分类器。作者引述工作称 auto mode 会放行多数不安全操作,并认为推理代码或复杂 shell 的效果在这一层不实际。

    Paper analysisRead analysis
  4. DefensesarXiv 2609.35659

    Tracekit:面向自主编码 Agent 的防篡改意图-推理-动作审计系统

    提出 Tracekit,审计编码 Agent 的意图、推理与动作并检测账本篡改

    Published
    Evaluated models
    Claude Code (claude -p, version 2.1), 独立评审(同一 CLI,无工具)
    AbstractTracekit 把意图、自述和动作写入可锚定的哈希链。

    Tracekit 是无第三方依赖的编码智能体审计层:把人类请求、模型自述和已执行动作并排写入可外部锚定的哈希链,执行前用正则门,事后用规则和独立模型互查。

    Paper analysisRead analysis
  5. DefensesarXiv 2609.22573

    研究提出面向企业 MCP 的零信任授权与工具发现方案

    提出企业 MCP 零信任授权与发现扩展,在架构上拒绝提示注入下的越权调用

    Published
    Setting
    AI Agent
    Evaluated models
    gpt-5, claude-sonnet-4-6, claude-opus-4-7 and others6
    Abstract同一份按工具声明同时管 MCP 的发现、列表和调用,使注入不能扩大已有凭证的权限。

    作者把零信任放在 MCP 工具调用边界:智能体即使被提示注入,也不能调用其凭证范围之外的工具。问题来自三点部署性质。调用意图会随不可信上下文改变。

    Paper analysisRead analysis
  6. DefensesarXiv 2609.14631

    IntentCap 提出按任务意图限定 LLM Agent 权限的机制

    提出 IntentCap,按来源字段所有权合成并裁决 Agent 能力租约

    Published
    Setting
    AI Agent
    Abstract四源字段租约约束副作用。

    IntentCap 是把智能体能力绑到当前任务意图、并按上下文来源划分字段所有权的权限机制。

    Paper analysisRead analysis
  7. DefensesarXiv 2609.05901

    KITA:为 LLM Agent 设计密钥隔离的阈值签名授权架构

    提出 KITA,把 Agent 批准绑定到规范动作的密钥隔离阈值签名

    Published
    Setting
    AI Agent
    AbstractKITA 把隔离的阈值签名当作精确动作的执行条件,并测量门控、相关性与签名开销。

    KITA 是一套密钥隔离的阈值授权架构,用来把模型评审变成执行外部动作的条件。

    Paper analysisRead analysis
  8. ActBench: A Self-Evolving Benchmark for Behavioral Safety of Collaborative Agents

    构建自演化基准 ActBench,评测协作智能体的行为安全

    Published
    Setting
    AI Agent
    Evaluated models
    Claude-Opus-4.8, Claude-Sonnet-4.6, GPT-5.5 and others15

    AbstractActBench 通过自演化搜索与双证据重构评测轨迹行为安全,揭示基础模型决策差异主导了协同智能体的操作风险。

    Paper analysisRead analysis
  9. DefensesarXiv 2607.13716

    CAVA:面向智能体 AI 运行时治理的规范动作验证与证明

    提出 CAVA,将异构智能体运行时事件规范为可哈希动作并绑定审批与回执

    Published
    Setting
    AI Agent
    AbstractCAVA 用规范动作指纹承接异构运行时治理。

    CAVA 是给异构智能体运行时用的动作语义层,用来回答“被批准和被审计的到底是哪一个动作”。

    Paper analysisRead analysis
  10. DefensesarXiv 2603.12644

    Revealing security threats and defense architectures for autonomous agents: an OpenClaw case study

    Reviews threats to autonomous agents and proposes a full-lifecycle defense blueprint, FASA

    Published
    Setting
    AI Agent
    AbstractUsing OpenClaw as an example, it divides risks into three layers and uses FASA to describe defense from input to system execution.

    This is a threat review and defense architecture report using OpenClaw as a case study, not a scored attack or defense evaluation.

    Paper analysisRead analysis
  11. DefensesarXiv 2603.11619

    Taming OpenClaw: Security Analysis and Mitigation of Threats in Autonomous LLM Agents

    Proposes a defense-in-depth aligned with the five stages to mitigate cross-stage threats to autonomous agents

    Published
    Setting
    AI Agent
    AbstractThis is a lifecycle threat analysis and layered mitigation design for OpenClaw, with the cases serving as qualitative evidence.

    This is a lifecycle threat analysis of OpenClaw, accompanied by a set of layered mitigation designs. The problem addressed is: when autonomous agents connect instant messaging, persistent memory, third-party plugins, and high-privilege execution, attacks are no longer limited to single-turn jailbreaks but can compound across multiple stages from initialization to execution. The authors argue that defenses hardening only individual interfaces cannot stop such cross-temporal attacks.

    Paper analysisRead analysis