Skip to content

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

Oct 12 · Monday

All papers

Found 24 papers

Another 232papers do not have category tags yet and are not included in filtered results.

  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。

    Paper analysisRead analysis
  2. MMSkillRisk:多模态技能图像中的隐藏指令可诱导 Agent 执行未授权操作

    提出 MMSkillRisk 基准,评测多模态技能图像中的隐藏指令诱导 Agent 越权操作

    Published
    Evaluated models
    GPT-5.6-sol, Kimi-K2.6, Kimi-K3 and others6

    Abstract论文提出 MMSkillRisk 基准与 NCVA 攻击,揭示多模态技能中教学图像潜藏指令可引发未授权操作并与良性任务共存。

    Paper analysisRead analysis
  3. AttacksarXiv 2609.30383

    研究者提出技能级联攻击

    提出技能级联攻击,把有害目标拆散到多个技能里绕过单技能扫描

    Published
    Setting
    AI Agent
    Evaluated models
    GPT-5.4, GPT-5.4 Mini, Claude Opus 4.6 and others11

    Abstract论文探讨了多技能智能体系统面临的技能级联攻击风险,提出自动化红队框架与基准集,指出跨技能交互存在安全防护盲区。

    Paper analysisRead analysis
  4. AttacksarXiv 2609.22510

    特拉维夫大学提出爆炸性提示词:条件触发的间接提示注入可绕过九款生产 Agent

    提出条件触发的爆炸提示词,并设计摄入期检测器 DeFuse

    Published
    Setting
    AI Agent
    Evaluated models
    Llama-3.1-8B-Instruct, Llama-3.1-70B-Instruct, Qwen2.5-32B-Instruct and others15
    Abstract论文揭示了条件触发使提示注入绕过防御并在触发时执行工具,提出轻量检测器并在摄入期有效防范。

    论文针对大语言模型智能体面临的间接提示注入威胁,研究了利用条件句式实现时间分离的爆炸提示词。

    Paper analysisRead analysis
  5. AttacksarXiv 2609.18217

    研究:跨通道碎片化攻击可绕过 MCP 单通道防御

    提出跨通道分段攻击,利用 MCP 隐式信任外发敏感文件

    Published
    Evaluated models
    GPT-4o, GPT-4o-mini, GPT-5.4 and others15

    Abstract论文揭示了 MCP 多通道隐式信任层级,通过跨通道分段与协议级利用突破现有防御,展示了严重的凭证外发风险。

    Paper analysisRead analysis
  6. AttacksarXiv 2608.16246

    CompoSkill 框架利用逐个通过扫描的 Agent 技能组合发起攻击

    提出 CompoSkill,将逐个通过扫描的技能编排为攻击链

    Published
    Setting
    AI Agent
    Evaluated models
    GPT-5.4, Gemini-3.1-flash, DeepSeek-V4 and others4

    Abstract论文分析了智能体技能组合风险,指出单包安全认证状态不具备可组合性,需转向路径级防护。

    Paper analysisRead analysis
  7. AttacksarXiv 2607.14493

    LogInject 框架披露 LLM 日志分析中的被动提示注入

    提出 LogInject 框架,评测日志分析中的被动提示注入及缓解

    Published
    Evaluated models
    GPT-4o, Claude 3.5 Sonnet, Llama-3-70B-Instruct

    Abstract论文评估了 LLM 日志分析中的上下文污染风险,提出分片拼接等攻击与三层防御,指出必须结合架构隔离与人工介入。

    Paper analysisRead analysis
  8. AttacksarXiv 2606.21732

    研究者提出 relinking 漏洞:LLM Agent 压缩边界可被拼出恶意指令

    提出 Relink 攻击,在 Agent 压缩边界将分散良性片段重组为恶意指令

    Published
    Setting
    AI Agent
    Evaluated models
    Gemma-4-31B-it, GPT-OSS-20B, Qwen3.6-27B and others15
    Abstract论文指出提示词压缩引入重链漏洞,提出 RELINK 攻击工具与 KBRA 边界审计防御,证明压缩边界应作为关键安全边界。

    本文研究了智能体基于摘要的提示词压缩所引入的新型安全漏洞——重链(Relinking),并提出了系统化的攻击构建方法与边界审计防御机制。

    Paper analysisRead analysis
  9. AttacksarXiv 2603.24203

    研究者提出 TIP:用树结构搜索为 MCP 生成隐蔽注入载荷

    提出 TIP,用树搜索为 MCP 响应生成隐蔽注入载荷

    Published
    Setting
    AI Agent
    Evaluated models
    Qwen2.5-7B-Instruct, Qwen2.5-72B-Instruct, Llama3.1-8B-Instruct and others8
    AbstractTIP 在 MCP 工具响应上做黑盒注入。

    TIP 是面向 MCP 工具响应的 black-box 间接提示注入,目标是在不改工具元数据的情况下生成语义连贯的 JSON 载荷。

    Paper analysisRead analysis
  10. AttacksarXiv 2603.17239

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

    Proposes LAAF, which automatically attacks LPCI vulnerabilities in agent systems in stages

    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.

    Paper analysisRead analysis
  11. AttacksarXiv 2603.00131

    Thought Virus: viral misalignment induced by subliminal prompts in multi-agent systems

    Propose Thought Virus, which propagates preferences along multi-agent messages via subliminal prompts.

    Published
    Evaluated models
    Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct
    AbstractThought Virus spreads a single-point bias downstream.

    Thought Virus puts subliminal prompting into multi-agent communication, to see whether a single-point deviation spreads across the network without naming the target concept.

    Paper analysisRead analysis
  12. AttacksarXiv 2602.18514

    招聘中的特洛伊木马:标准模型与推理模型间接提示注入的红队测试案例研究

    用简历中的不可见指令做间接提示注入,操纵招聘推荐

    Published
    Evaluated models
    Qwen 3 30B A3B Instruct 2507, Qwen 3 30B A3B Thinking 2507

    Abstract作者称推理在简单注入下会帮攻击圆谎,在不合逻辑的复杂注入下又会把欺骗漏出来。

    Paper analysisRead analysis
  13. 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 用双通道和闭环精炼投毒 skill。

    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.

    Paper analysisRead analysis
  14. AttacksarXiv 2601.21360

    Compliance Paradox: Semantic-Instruction Decoupling in Automated Academic Code Evaluation

    提出 AST-ASIP ,把对抗指令植入编译器忽略区域以劫持代码评分

    Published
    Evaluated models
    DeepSeek-V3.2, Qwen3-235B, GPT-OSS-120B and others9
    Abstract作者称 RLHF 的服从性使评分脱离代码。

    这篇工作把自动代码评分中的失败称为 Compliance Paradox:评分模型可以离开提交逻辑,转而执行嵌在可编译代码里的隐藏指令。

    Paper analysisRead analysis