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Oct 12 · Monday

All papers

Found 8 papers

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  1. AttacksarXiv 2610.12524

    An exploration of attacks on AI agent systems

    重测并扩展对 AI Agent 的提示注入,并用 Agent Card 欺骗操纵 A2A 选择

    Published
    Setting
    AI Agent
    Evaluated models
    Ollama Mistral 0.9.5, llama3-8b-8192, gemini-2.0-flash-lite and others5
    Abstract单智能体过滤与分类可被格式或措辞绕过,A2A 选卡还会偏向名称和描述。

    作者在自建的 LangChain、AutoGen 和 A2A 系统上重测提示注入,并改 Agent Card 看选择是否偏移。摘要称试验主要凸显单智能体弱点,同时把攻击扩到多智能体。

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  2. AttacksarXiv 2610.04589

    StegoMemory red team testing: agent persistent memory can be used as a cross-session covert channel

    提出 StegoMemory,评测智能体持久记忆作为跨会话隐写信道

    Published
    Setting
    AI Agent
    Evaluated models
    Llama 3.1 405B, Claude 3.5 Sonnet, GPT-4o and others13

    Abstract研究揭示智能体持久记忆可作为隐蔽信道跨会话传输机密,写入持久化是主要防御瓶颈,呼吁建立记忆完整性防御。

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  3. AttacksarXiv 2603.17239

    LAAF: A Systematic Red-Teaming Method and Automated Attack Framework for LPCI Vulnerabilities in Agentic LLM Systems

    提出 LAAF,分阶段自动攻击智能体系统的 LPCI 漏洞

    Published
    Setting
    AI Agent
    Evaluated models
    gemini-2.0-flash-001, claude-3-haiku-20240307, meta-llama/llama-3.1-70b-instruct and others5
    AbstractLAAF does LPCI red teaming with taxonomy plus stage seeding.

    The authors state that LAAF is an automated red-teaming framework built specifically for LPCI in agentic LLMs. The issue to address is that encoded, delayed, and conditionally triggered payloads can persist in memory or RAG and re-enter across sessions, whereas Garak and PyRIT do not use the previous round's breakthrough results to seed the next stage across the six stages.

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  4. AttacksarXiv 2603.01414

    通过动作级操纵越狱具身 LLM

    提出 Blindfold,用动作级操纵越狱具身 LLM

    Published
    Evaluated models
    Llama-3.1-8B, DeepSeek-R1-14B, Gemma-3-27B and others8
    AbstractBlindfold 以动作级代理规划越狱具身 LLM,作者称可绕过语义检查并在仿真与真机生效。

    Blindfold 是面向具身 LLM 规划模块的 no-box 攻击,把恶意意图改写成看似良性的动作序列。

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  5. AttacksarXiv 2603.00131

    Thought Virus:多 Agent 系统中潜意识提示引发的病毒式失准

    提出 Thought Virus,经潜意识提示使偏好沿多 Agent 消息传播

    Published
    Evaluated models
    Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct
    AbstractThought Virus 把单点偏差传到下游。

    Thought Virus 把 subliminal prompting 放进多智能体通信,看单点偏差会不会在不点名目标概念时传到全网。

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