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Oct 13 · Tuesday

All papers

Found 16 papers

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  1. 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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  2. AttacksarXiv 2610.09264

    研究者提出 PackHallu:经规则文件提示注入对编码 Agent 发起包幻觉攻击

    提出 PackHallu,经规则文件提示注入诱导编码 Agent 换用攻击者指定包

    Published
    Evaluated models
    Qwen2.5-Coder-7B, Qwen3-Coder-30B, Devstral-Small-2-24B and others19
    AbstractPackHallu 诱导规则文件换包。

    PackHallu 研究一种针对编程智能体规则文件的提示注入:让智能体在本应使用合法依赖时,改导入攻击者指定并控制的包。

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  3. AttacksarXiv 2609.39788

    研究:多智能体系统潜在通信链路可被训练用于提升有害顺从

    提出只训练或篡改多智能体潜在通信链路来提高有害遵从

    Published
    Evaluated models
    Llama-3.2-3B-Instruct, Qwen2.5-3B-Instruct, Gemma-3-1B-IT and others7
    Abstract潜在链路本身即可改变冻结智能体系统的安全,奖励既能攻击也能修复。

    学习得到的潜在通信链路会改变由冻结、对齐智能体组成的系统是否遵从有害请求。。作者研究三种拓扑:双智能体、三级顺序协作,以及双专家加汇总。

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  4. AttacksarXiv 2609.39352

    研究者提出解耦铺垫与执行的 LLM Agent 技能投毒攻击

    提出 CoordPoison,解耦技能投毒的借口与动作

    Published
    Evaluated models
    DeepSeek-V4-Flash, GLM-5.3-Flash, Claude-Sonnet-4.6 and others5
    AbstractCoordPoison 外置借口。

    CoordPoison 是一种 Skill 供应链投毒方法,把“为何执行”和“执行什么”拆到两个 Skill。。

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  5. AttacksarXiv 2608.27439

    RedEvoAgent:以经验驱动技能演化的自动红队智能体

    提出 RedEvoAgent,演化攻击技能对黑盒智能体做自动红队

    Published
    Attacker
    Black-box
    Evaluated models
    MiniMax-M2.5, DeepSeek-V4-Flash, Qwen3.5-35B
    AbstractRedEvoAgent 用验证棘轮演化可读攻击技能,在两种产品级 harness 上多数设置高于固定工具与红队基线。

    RedEvoAgent 是面向产品级 black-box 智能体的自动红队方法,用一份可演化、人类可读的攻击技能编排多个越狱工具。。

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  6. AttacksarXiv 2608.09732

    ColluSkill 用跨技能组合绕过 Agent 技能扫描器

    提出 ColluSkill,用跨技能组合绕过 Agent 技能扫描器

    Published
    Evaluated models
    GPT-5.5, DeepSeek-V4-Pro, GLM-5.2

    Abstract论文揭示了单技能孤立扫描的盲区,提出多技能链攻击 ColluSkill 与上下文防御 ChainGuard 并验证了有效性。

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  7. AttacksarXiv 2608.09577

    ElasticBack:通过触发词与规则耦合在 LLM Agent 技能中植入条件后门

    提出 ElasticBack,在单个 Agent 技能中植入条件后门

    Published
    Evaluated models
    GLM-5.2, MiniMax-M3, GPT-5.4 and others4
    AbstractElasticBack 用冻结规则加遗传搜索演化 trigger,在单 skill 上做条件后门。

    ElasticBack 是放在 LLM agent 单个 skill 里的条件后门攻击。。

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  8. AttacksarXiv 2607.11698

    AHA 用自动研究循环发现生产 Agent 漏洞概念

    提出 AHA 自动科研循环,发现生产 Agent 的可复用漏洞概念

    Published
    Attacker
    Black-box
    Evaluated models
    Minimax-M2.7, Kimi-K2.6, Deepseek-V4-Pro and others5
    Abstract论文提出基于可证伪科研循环的 AHA 框架,从生产级智能体中挖掘并沉淀漏洞概念图,提升攻击复用率并指导防御加固。

    AHA 是一项针对生产级 LLM 智能体自动化红队测试的研究,旨在解决传统方法仅保留具体攻击载荷而缺乏成因解释、导致跨版本复用困难且无法指导修复的问题。

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  9. AttacksarXiv 2603.20925

    Profit is the Red Team: Stress-Testing Agents in Strategic Economic Interactions

    提出利润驱动红队,用标量盈余搜索对手以压测经济博弈 Agent

    Published
    Evaluated models
    GPT-OSS-120B, Qwen3-32B, MiniMax-M2.5 and others6
    Abstract利润驱动红队以标量盈余训练对手,六个目标在四博弈中被利用,提示规则可抬高目标盈余。

    利润驱动红队把可审计终局里的智能体安全写成对手搜索问题:固定目标会不会被一个只追逐自身盈余的自适应玩家利用。

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  10. AttacksarXiv 2603.11132

    WebWeaver: Breaking the topology confidentiality of LLM multi-agent systems via covert context inference

    Propose WebWeaver, which compromises only a single agent and recovers the multi-agent communication topology from conversation context.

    Published
    Evaluated models
    Llama 3.1 8B, Qwen 2.5 7B, Mistral NeMo 12B and others6
    AbstractWebWeaver infers topology from context.

    WebWeaver is an inference attack against the confidentiality of LLM-MAS communication topology: it controls only one arbitrary agent and recovers the full graph from dialogue context, rather than querying agent IDs.

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  11. AttacksarXiv 2602.14211

    SkillJect: Automatically generating skill-based prompt injection for skill-based agents

    Proposes SkillJect, which automatically generates poisoned skills to induce code agents to execute hidden scripts

    Published
    Evaluated models
    Claude-4.6-Sonnet, GPT-5-mini, GLM-4.7 and others10
    AbstractSkillJect poisons skills using dual channels and closed-loop refinement.

    SkillJect is an automated prompt injection framework targeting installable agent skills. The authors address the problem that malicious instructions placed in publicly available or manually installed skills are often rejected or ignored, yet once a skill is installed, it is repeatedly loaded as trusted guidance.

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