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Oct 13 · Tuesday

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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 2610.09027

    P-VMI:对抗图像经 KV cache 持续影响多轮对话

    提出持久视觉记忆注入,使对抗图像经缓存在触发时操控输出

    Published
    Evaluated models
    Qwen3-8B, Qwen3-VL-8B-Instruct, Gemma-4-12B

    Abstract论文提出 P-VMI,证实对抗输入被掩蔽后仍可经 KV 缓存持久控制模型,强调了保留状态的安全风险。

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  3. 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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  4. AttacksarXiv 2610.03430

    JIL:通过对抗后缀操纵 LLM 请求调度优先级

    提出 JIL 对抗后缀,压低长度预测以抬高请求调度优先级

    Published
    Evaluated models
    Llama-3-8B-Instruct, Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3 and others4
    AbstractJIL 用对抗后缀压低长度预测,在预测式 LLM 调度中提高自身优先级。

    JIL 是对预测式 LLM 调度的攻击:用对抗后缀压低长度预测,使请求在 SRPT 式队列里更早得到服务。

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  5. 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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  6. OtherarXiv 2610.01539

    研究者提出 AI 评估沙盒配置器,支持欧盟 AI 法案下的技术评估

    提出 AI 评估沙盒配置器,支撑监管沙盒多方技术评估与报告

    Published
    Evaluated models
    LLM-backed conversational assistant
    Abstract框架把每次技术测试配成沙盒。

    AI Assessment Sandbox Configurator是面向欧盟 AIRS 中结构化技术测试的开源配置框架,用来按一次参与组装评估环境。

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  7. 面向智能体的高效 HPC 系统:挑战与机遇

    刻画 HPC 上编码智能体的使用差异并提出设施协同设计议程

    Published
    AbstractFASRC 测量说明编码智能体改变 HPC 使用方式,作者据此提出四层协同设计议程。

    这是一篇对生产 HPC 上编码智能体的测量与系统议程论文,不是攻击实现或防御评测。。

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  8. OtherarXiv 2609.16915

    ROSETTA:基于混合 CKKS/TFHE 的高效准确隐私保护 LLM 解码框架

    提出混合 CKKS/TFHE 的隐私保护 LLM 解码框架 ROSETTA

    Published
    Evaluated models
    GPT-2 Base, TinyLlama-1.1B, LLaMA-3-8B
    AbstractROSETTA 用分段 LUT 和 DAG 最短路,把 CKKS 与 TFHE 按算子拼进 LLM 解码。

    ROSETTA 是面向 LLM decode 的混合 CKKS/TFHE 隐私推理框架,目标是在 honest-but-curious 两方设定下,让服务器在密文上完成解码,同时保住权重和用户输入。

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  9. DefensesarXiv 2609.12378

    Odin:面向隐私保护 Llama 3 8B 推理的开源端到端 FHE 实现

    提出端到端全同态加密方案 ODIN,在云端密文上完成推理以保护提示词

    Published
    Evaluated models
    Meta-Llama-3-8B
    AbstractODIN 用可复用的特征主序打包和联合误差预算,在单张 H100 上完成 Llama-3-8B 的 CKKS 推理。

    ODIN 是面向 Llama 的服务器侧 CKKS 推理系统:客户加密提示词,诚实但好奇的服务器用明文权重在密文上算完 Llama-3,只返回结果密文。

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  10. 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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  11. 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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  12. AttacksarXiv 2605.30040

    研究揭示 LLM 按 token 计费可被服务商系统性虚报

    构造可通过计费审计的 token 虚报,使服务商系统性超收

    Published
    Evaluated models
    PALACE domain-adapted auditor
    Abstract不诚实提供方可在三种 token 审计上虚报并通过检查,作者认为需改用提供方不控制的证据。

    不透明 LLM 的按 token 计费里,现有审计核验的是提供方可控的证据,不诚实提供方可以虚报并通过检查。。

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  13. AttacksarXiv 2605.08313

    研究者提出 SeedHijack 攻击,劫持 LLM 采样随机数种子

    提出 SeedHijack,劫持采样 PRNG 以强制选定 token

    Published
    Evaluated models
    GPT-2 (124M), Qwen2-1.5B-Instruct, DeepSeek-R1-1.5B and others5
    Abstract采样层 PRNG 被替换以强制选 token。

    SeedHijack 把 LLM 采样用的 PRNG 输出换成攻击者选定的数,从而指定下一个 token。权重、输入和 logits 都不改。防御是用硬件 QRNG 替换该随机源。

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  14. DefensesarXiv 2603.10726

    PrefixWall: Mitigating Prefix Cache Side Channels in Shared LLM Systems

    Proposes PrefixWall, which selectively isolates shared prefixes to mitigate timing side channels

    Published
    Evaluated models
    gemma-3-4b-it, llava-onevision-qwen2-0.5b-ov-hf, llava-onevision-qwen2-7b-ov-chat-hf and others9
    AbstractPrefixWall uses cache metadata to selectively cut off cross-user prefix continuation, preserving APC while limiting the TTFT side channel.

    PrefixWall is a system-level defense for multi-tenant LLM services that limits the TTFT side channel of Automatic Prefix Caching while preserving prefix reuse.

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