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Oct 12 · Monday

All papers

Found 3 papers
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Attack type

Poisoning and backdoors3

Attack technique

Gradient and representation optimization5Automated search5Conversation structure manipulation4Reasoning-chain manipulation13Cross-modal payloads4Defense-targeted3Dormant triggers3

System component

MCP and skills8Memory22RAG5Guardrails and judges5Reasoning chains2Permissions and sandboxing3Fine-tuning and open weights19Vision3Embeddings1Multimodal8
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Attack technique：Dormant triggersSystem component：Reasoning chainsSystem component：EmbeddingsClear all

Another 255papers do not have category tags yet and are not included in filtered results.

  1. AttacksarXiv 2610.12644

    Cancerous backdoor attack: progressive activation via invisible tokens and a stage controller.

    Proposes the Cancerous backdoor, which progressively corrupts reasoning using invisible stage tokens.

    arXiv
    2610.12644
    Published
    Layer
    Training and data layer
    Setting
    Models and APIs
    Evaluated models
    Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3
    AbstractHidden tokens plus a stage controller make the backdoor progressively corrupt reasoning upon triggering, and the authors believe that looking only at the answer will miss it.

    Cancerous Backdoor Attack changes LLM backdoors from one-time activation to multi-stage reasoning corruption that accumulates progressively with triggers.

    Paper analysisRead analysis
  2. AttacksarXiv 2604.05549

    Stop obsessing over prompts: reasoning hijacking and constraint tightening for red-teaming LLM agents

    提出 JailAgent，不改用户提示，经推理劫持与记忆投毒操纵 Agent 动作

    arXiv
    2604.05549
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    GPT-3.5-turbo, GPT-4o, GPT-5 and others7
    AbstractJailAgent 不改提示词，用三阶段操纵推理与检索。

    JailAgent 是不修改用户原始提示词的 LLM 智能体红队框架。

    Paper analysisRead analysis
  3. AttacksarXiv 2512.01335

    EmoRAG 研究揭示 RAG 检索对表情符号扰动的脆弱性

    提出 EmoRAG，向查询与知识库注入同一表情以劫持 RAG 检索

    arXiv
    2512.01335
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Evaluated models
    SPECTER, Contriever, Qwen2-7B and others11
    AbstractEmoRAG 用表情联动查询与库文本。

    EmoRAG 测量 RAG 对符号扰动的反应：同一表情同时出现在查询和库文本里时，检索会偏离语义匹配。作者要检查的是，检索是否真由语义相关性决定。

    Paper analysisRead analysis
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