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Oct 12 · Monday

All papers

Found 6 papers
Filter5

Attack type

Poisoning and backdoors3

Defense type

Detection and filtering4Instruction–data separation2Permissions and approval0

Impact

Unauthorized actions12Output manipulation6

System type

AI Agent5

Layer

Application layer6

System component

Memory6RAG3Guardrails and judges14Permissions and sandboxing4Vision4Video3Multimodal8
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 232papers have no tags yet and are not included in filtered results.
Defense type：Detection and filteringDefense type：Instruction–data separationDefense type：Permissions and approvalImpact：Output manipulationSystem component：MemoryClear all

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

  1. DefensesarXiv 2610.07403

    针对 LLM 谄媚的纵深防御：记忆门控对激活诱导与记忆诱导谄媚的评估

    评测记忆门控对激活诱导与记忆诱导谄媚的防御效果

    arXiv
    2610.07403
    Published
    Layer
    Application layer
    Setting
    LLM applications
    Evaluated models
    Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3 and others4
    Abstract作者称外部记忆过滤成立，数据未显示反向转向再有贡献。

    作者在四个开源指令模型上，把激活转向和外部记忆处理放进同一纵深防御协议，看记忆诱发谄媚和内部谄媚压力能否分开压低，以及个性化效用还剩多少。要处理的问题是：检索到的用户历史会推动模型顺从存储信念。只改外部上下文，并不直接动模型内部的谄媚表征。

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  2. DefensesarXiv 2610.05162

    MemAdapter：用反事实适配缓解记忆诱发的谄媚

    提出 MemAdapter，在检索后约束记忆对智能体推理的影响

    arXiv
    2610.05162
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    DeepSeek-V4-Flash, GPT-5.6-sol, Qwen3-8B
    Abstract检索后三阶段调节记忆角色。

    MemAdapter 是给长期记忆智能体用的检索后调节框架，用来处理记忆诱发谄媚。

    Paper analysisRead analysis
  5. AttacksarXiv 2607.06595

    When Agents Remember Too Much: Memory Poisoning Attacks against Large Language Model Agents

    Proposed GhostWriter memory poisoning, which writes into the agent's long-term memory via email and calendar and activates across sessions

    arXiv
    2607.06595
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    GPT-5.4-mini, DeepSeek-V4-Flash, Gemini-2.5-Flash and others4
    Paper analysisRead analysis
  6. Evaluation and benchmarksarXiv 2604.01350

    No Attacker Needed: Unintentional Cross-User Contamination in Shared-State LLM Agents

    形式化并评测共享状态 Agent 的无意跨用户污染

    arXiv
    2604.01350
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    GPT-4o
    Abstract作者报告良性交互即可造成跨用户污染，文本清洗挡不住代码记忆。

    UCC 是共享状态 LLM 智能体上的稳健性问题：源交互可以是良性的，局部有效的制品仍会在后来被误用到其他用户。

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