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Oct 13 · Tuesday

All papers

Found 4 papers
Filter4

Attack type

Extraction and theft4

Attack technique

Multi-turn escalation0Conversation structure manipulation0Side channels4

Impact

Data and privacy leaks4

System type

Models and APIs3

Attack surface / entry point

Inference services and hardware4

Layer

Prompt layer30Application layer12Infrastructure layer4
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Attack technique：Multi-turn escalationAttack technique：Conversation structure manipulationAttack technique：Side channelsLayer：Infrastructure layerClear all

Another 201papers do not have category tags yet and are not included in filtered results.

  1. AttacksarXiv 2610.09981

    研究：LLM 路由决策日志可推断用户医疗、自伤等敏感话题

    提出利用路由元数据推断敏感话题的侧信道攻击

    arXiv
    2610.09981
    Published
    Layer
    Infrastructure layer
    Setting
    LLM applications
    Evaluated models
    RouteLLM (BERT router), notdiamond-0001, Vela (vLLM Semantic Router)

    Abstract论文揭示并测量了 LLM 路由决策在无文本日志中的敏感话题泄露，验证了按类别公平性防御的有效性与边界。

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  2. AttacksarXiv 2610.06848

    TranScope：利用 CPU 性能计数器对 Transformer 做成员推断攻击

    提出 TRANSCOPE，用 CPU 硬件计数器推断训练集成员

    arXiv
    2610.06848
    Published
    Layer
    Infrastructure layer
    Setting
    Models and APIs
    Attacker
    Black-box
    Evaluated models
    BERT (4.4M-340M), ViT (22M-307M), Pythia (70M-2.8B) and others5

    AbstractTRANSCOPE 揭示了分词器在嵌入表引发的硬件访问局部性差异，证实微架构计数器可突破恒定时间与置信度遮蔽防御。

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  3. AttacksarXiv 2608.17485

    KeyPooling: Measuring where LLM API relay paths break prompt cache isolation

    Proposing KeyPooling to measure where API relays break prompt cache isolation

    arXiv
    2608.17485
    Published
    Layer
    Infrastructure layer
    Setting
    Models and APIs
    Evaluated models
    grok-4-fast-non-reasoning, grok-4-1-fast-non-reasoning, grok-3 and others11
    AbstractShared upstream credentials place relay customers in the same cache domain; preserving identity or separate principals can separate them.

    KeyPooling measures where LLM API relays drop customer identity, causing prompt caches to no longer be isolated per downstream customer.

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  4. AttacksarXiv 2602.20595

    OptiLeak: Efficiently Reconstructing Prompts with Reinforcement Learning in Multi-Tenant LLM Services

    Proposing OPTILEAK, reconstructing others' prompts token-by-token via a KV cache side channel

    arXiv
    2602.20595
    Published
    Layer
    Infrastructure layer
    Setting
    Models and APIs
    Attacker
    Black-box
    Evaluated models
    Qwen-2.5-3B-Instruct, Qwen-2.5-7B-Instruct, Qwen-2.5-14B-Instruct and others4

    AbstractOPTILEAK uses SFT and hard-token DPO to reduce the number of requests per token; the authors claim the risk of cache prompt leakage is higher than previously reported.

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