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Oct 13 · Tuesday

All papers

Found 2 papers
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Attack technique

Gradient and representation optimization0Dormant triggers2

Attack surface / entry point

Memory writes1Training data1

System component

MCP and skills3Memory19RAG3Guardrails and judges4Reasoning chains2Fine-tuning and open weights13Multimodal4
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Attack technique：Gradient and representation optimizationAttack technique：Dormant triggersAttack surface / entry point：Memory writesAttack surface / entry point：Training dataSystem component：Reasoning chainsClear all

Another 201papers 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
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