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Oct 12 · Monday

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  1. DefensesarXiv 2610.12432

    FAITH: A feasibility-aware safety filtering reinforcement learning framework for high-dimensional systems

    Proposes the FAITH safe filtering reinforcement learning framework, training high-dimensional systems to optimize task reward under constraints

    Published
    AbstractFAITH separates safety filtering from reward optimization.

    FAITH is a model-free safe filtering reinforcement learning framework for high-dimensional systems: safety correction and task reward reside in separate modules.

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  2. AttacksarXiv 2610.12292

    The paper proposes an option channel attack: changing one option label can make an agent guardrail allow a prohibited operation

    Proposing an option channel attack that renames option labels so that agent guardrails allow prohibited operations

    Published
    Attacker
    Black-box
    Evaluated models
    LAYA-TD, LAYA-EN, LAYA-ML and others5

    AbstractThe authors' evaluation indicates that typed models should not serve as final safety arbiters; they are susceptible to off-policy text and option name manipulation, and confidence filtering cannot resist attacks.

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  3. DefensesarXiv 2610.09793

    用时序逻辑监控工具 Agent

    提出 MFOTL 离线时序验证以监控工具 Agent 的危险动作链

    Published
    Setting
    AI Agent
    Attacker
    Black-box
    Abstract形式化验证可有效识别多步攻击,但其有效性高度依赖轨迹的可信历史记录。

    这篇论文对工具调用大语言模型智能体的安全监控进行了形式化时序验证研究。

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  4. AttacksarXiv 2610.10612

    PyCache Trap 利用 Python 缓存替换绕过七款 Agent Skill 扫描器

    提出 PyCache Trap,用字节码缓存替换绕过 Agent 技能扫描器

    Published
    Evaluated models
    CISCO, HSS, AUDITOR and others8
    Abstract论文剖析智能体技能审查与执行脱节盲区,提出 PyCache Trap 攻击并构建基于可信复现的 EAV 防御。

    这篇论文研究了智能体技能(Agent Skills)安全扫描中的“审查–执行脱节”问题,并提出了对应的攻击验证与防御方案。

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  5. DefensesarXiv 2610.09469

    Secure-CUA: Constraining the Impact of Untrusted Content in Computer-Use Agents

    提出 Secure-CUA,用动作事务约束计算机使用智能体的不可信影响

    Published
    Setting
    web agent
    Evaluated models
    Claude Opus 5, Gemini 3.8 Flash, GPT-5.6-Sol
    AbstractSecure-CUA 用每步事务约束不可信影响,良性 TSR 接近 Vanilla-CUA。

    Secure-CUA 要限制图形界面里不可信内容对 CUA 决策和 GUI 执行的影响,同时保留合法任务所需的不可信信息。

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  6. AttacksarXiv 2610.09240

    WebMirage hijacks visual web agents' browser operations with adversarial images

    提出 WebMirage,用局部对抗图像劫持视觉网页 Agent 的浏览器操作

    Published
    Setting
    web agent
    Evaluated models
    LLaVA-v1.5-13B, LLaVA-v1.6-34B, MiniCPM-o-8B and others6
    Abstract论文提出了针对网络智能体的端到端视觉红队框架 WebMirage,揭示了从视觉定位到浏览器执行的安全脆弱性。

    WebMirage 是一个面向视觉定位网络智能体的流水线感知端到端红队攻击框架。

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  7. AttacksarXiv 2610.08951

    ASPIRE:面向 LLM Agent 的开放式提示注入红队引擎

    提出 ASPIRE 行为图搜索,开放式发现 Agent 提示注入漏洞

    Published
    Setting
    AI Agent
    Evaluated models
    Gemini-3.7-Flash, DeepSeek-V4-Flash, Gemini-2.5-flash
    AbstractASPIRE 把提示注入红队从载荷优化改成行为图引导的开放式漏洞发现。

    ASPIRE 是给搭建方用的智能体提示注入红队引擎,目标是开放式画出可利用的行为路径,而不是在固定任务上打磨一条载荷。

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  8. DefensesarXiv 2610.08082

    POLAR:面向小型工具调用智能体的可逆性护栏框架

    提出 POLAR 本体门控,在执行前否决不可逆工具调用

    Published
    Setting
    AI Agent
    Evaluated models
    qwen/qwen3-8b, meta-llama/llama-3.1-8b-instruct, nvidia/nemotron-nano-9b-v2 and others6
    Abstract训练无关的事前可逆性门控,弱 airline 模型有收益,强模型与 retail 常回落。

    POLAR 是训练无关的事前门控,用类型化本体在工具调用执行前检查能否组成候选逆序列。

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  9. DefensesarXiv 2610.07935

    SIGMA:让模型依据 Model Spec 自我改进安全对齐

    提出 SIGMA,让模型依据 Model Spec 自造对齐数据并改进安全对齐

    Published
    Setting
    AI Agent
    Evaluated models
    Qwen3.6-27B
    AbstractSIGMA 用 Model Spec 自造监督,单轮训练可迁到多项智能体安全指标。

    SIGMA 让 Qwen3.6-27B 只凭 Model Spec 和高层领域,自己生产对齐任务、评分细则和奖励。

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  10. SafeActBench 发布:工具型 Agent 在证据到行动链上的失败模式

    构建 SAFEACTBENCH 评测工具型 Agent 的证据到行动失败

    Published
    Setting
    AI Agent
    Evaluated models
    Claude Opus 5, GPT-5.6 Sol, DeepSeek-V4-Flash and others5
    Abstract作者称失败常起于调查不足或过早行动,证据齐后的单步则少失败。

    SAFEACTBENCH 评测工具智能体的 consequential actions 是否由行动前已建立的证据支持,而不是只看终态对不对。

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  11. DefensesarXiv 2610.07657

    DEFER1 多智能体防御研究报告受控测试中的攻击成功率下降

    实现 DEFER,以确定性检查优先拦截多智能体流水线的内容攻击

    Published
    Evaluated models
    Qwen3-235B-A22B-Instruct-2507, gpt-oss-120b, Llama-4-Scout-17B-16E-Instruct and others5
    AbstractDEFER 把确定性检查放在 judge 之前。

    DEFER 用来测量多智能体授权流水线里,确定性规则在何处结束、LLM judge 从何处开始。

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  12. 论文实测 Agent 工具调用门控栈的失败相关性

    提出 nmult 并评测 Agent 动作门禁堆叠的错误相关性

    Published
    Setting
    AI Agent
    Evaluated models
    AgentTrust runtime (174 rules), GPT-5.5 (gpt55), claude-haiku-4-5-20251001 and others7
    Abstract论文实测发现大模型裁判间错误显著相关,作者主张按机制选型并在整栈层面度量防护增益。

    论文是一项针对智能体动作拦截防御堆叠故障关联性与实际组合价值的实证测量研究。

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