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Oct 12 · Monday

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  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

    Published
    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

    Published
    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 2610.06093

    研究评测训练数据提取风险的跨语言迁移

    用翻译前缀做跨语言训练数据提取,恢复英语训练文本中的 PII

    Published
    Evaluated models
    GPT-Neo 1.3B, GPT-Neo 2.7B, GPT-J 6B and others7
    Abstract跨语言 TDE 能恢复英语记忆的 PII,转移随多语训练和措辞保真变化。

    这篇工作检验训练数据抽取是否跨语言:英语段落里记住的 PII,能否被其他语言的前缀逐字抽出。

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  4. HXAI:分布式能源系统中的分层隐私保护可解释 AI 框架

    提出 HXAI 分层隐私解释框架,本地保留细解释并只发布加噪摘要

    Published
    Evaluated models
    gradient-boosted tree regression models
    AbstractHXAI 把无噪声局部 SHAP 留在家庭内,只聚合差分隐私摘要。

    HXAI 是面向分布式能源系统的分层隐私保护解释框架,服务规划、监测与诊断,而不是实时控制。

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  5. DefensesarXiv 2608.25431

    GIFT: Enforcing User Data Isolation in LLM Serving via GPU Information Flow Tracking

    Propose GIFT, isolating user data in shared LLM serving using encryption and GPU information flow tracking

    Published
    Evaluated models
    Qwen2.5-14B, Qwen2.5-32B, Qwen2.5-72B and others6
    AbstractGIFT uses cryptographic isolation plus GPU information flow rules to enforce user data isolation on vLLM and DistServe, and reports throughput overhead.

    GIFT is the authors' user data isolation system for shared LLM serving, with GIFT-CC enabling confidential computing. In the conclusion, the authors state it is the first IFT design for this scenario.

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  6. 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

    Published
    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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  7. AttacksarXiv 2608.09867

    Researchers steal proprietary LLM inference chains via cross-model compatibility of encrypted inference blocks

    利用加密思维块的跨模型重放,诱导弱模型逐字转写专有推理链

    Published
    Attacker
    Black-box
    Evaluated models
    Claude Opus 4.8, Claude Sonnet 4.6, Claude Haiku 4.5 and others13

    Abstract论文揭示了客户端加密思维块跨模型互换漏洞,提出了低成本提取方法并给出防御方案。

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  8. AttacksarXiv 2604.01039

    评估与加固 LLM 系统指令以抵御编码攻击提取

    用编码与结构化请求抽取系统指令,并用 CoT 改写加固

    Published
    Attacker
    Black-box
    Evaluated models
    GPT-4.1-mini, GPT-3.5-turbo, Gemini-2.5-flash and others7
    Abstract编码式请求可在直接拒答后引出系统指令。

    这项工作评测并加固 LLM 系统指令在编码式抽取下的保密性。作者关心的是:直接抽取已被拒绝时,把同一请求改写成格式化或编码任务,模型是否仍交出受保护内容。

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  9. DefensesarXiv 2603.10726

    PrefixWall:缓解共享 LLM 系统中的前缀缓存侧信道

    提出 PrefixWall 选择性隔离共享前缀以缓解计时侧信道

    Published
    Evaluated models
    gemma-3-4b-it, llava-onevision-qwen2-0.5b-ov-hf, llava-onevision-qwen2-7b-ov-chat-hf and others9
    AbstractPrefixWall 用缓存元数据按需切断跨用户前缀延续,以保留 APC 并限制 TTFT 侧信道。

    PrefixWall 是多租户 LLM 服务上的系统级防御,用来限制 Automatic Prefix Caching 的 TTFT 侧信道,同时保留前缀复用。

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  10. AttacksarXiv 2602.20595

    OptiLeak:在多租户 LLM 服务中用强化学习高效重建提示词

    提出 OPTILEAK,经 KV 缓存侧信道逐 token 重建他人提示

    Published
    Attacker
    Black-box
    Evaluated models
    Qwen-2.5-3B-Instruct, Qwen-2.5-7B-Instruct, Qwen-2.5-14B-Instruct and others4

    AbstractOPTILEAK 用 SFT 与 hard-token DPO 降低每 token 请求数,作者称缓存提示泄露的风险高于先前报告。

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  11. DefensesarXiv 2602.11528

    Stop Tracking Me! Proactive Defenses Against LLM Attribute Inference Attacks

    提出用户侧防御 TRACE-RPS,分享前匿名化文本并追加后缀以抑制属性推断

    Published
    Evaluated models
    Llama2-7B-Chat, Llama2-13B-Chat, Llama3.1-8B-Instruct and others10
    Abstract用户侧 TRACE 加 RPS/MPS,在分享前降低 LLM 对敏感属性的推断准确率。

    这是一项用户侧防御:在文本公开前降低 LLM 对敏感属性的推断,而不是改攻击提示或削弱模型推理。

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  12. AttacksarXiv 2512.16059

    ContextLeak:审计私有上下文学习方法的隐私泄露

    提出 ContextLeak,用金丝雀成员推断审计私有 ICL 泄漏

    Published
    Attacker
    Black-box
    Evaluated models
    Llama3.3-70b, Qwen2.5-72b, GPT-4.1 and others6
    AbstractContextLeak 审计私有 ICL 的 worst-case 泄漏。

    ContextLeak 是一套用成员推断审计私有 ICL 的框架,目标是给 worst-case 泄漏一个经验下界,而不是只引用理论 ε。

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