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Oct 12 · Monday

All papers

Found 21 papers

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  1. AttacksarXiv 2610.12524

    An exploration of attacks on AI agent systems

    重测并扩展对 AI Agent 的提示注入,并用 Agent Card 欺骗操纵 A2A 选择

    Published
    Setting
    AI Agent
    Evaluated models
    Ollama Mistral 0.9.5, llama3-8b-8192, gemini-2.0-flash-lite and others5
    Abstract单智能体过滤与分类可被格式或措辞绕过,A2A 选卡还会偏向名称和描述。

    作者在自建的 LangChain、AutoGen 和 A2A 系统上重测提示注入,并改 Agent Card 看选择是否偏移。摘要称试验主要凸显单智能体弱点,同时把攻击扩到多智能体。

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  2. 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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  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. DefensesarXiv 2610.06636

    差分隐私混合公共数据集提升隐私学习效果

    提出 DP-MixMin,私有学习公开数据混合并做差分隐私微调

    Published
    Evaluated models
    ResNet-18, GPT-2 Medium
    AbstractDP-MixMin 私下学公开混合再 DP 微调,胸片 macro AUC 与邮件困惑度优于均匀混合。

    DP-MixMin 是一条端到端的差分隐私训练流程:先私下决定多个公开数据集的预训练混合,再预训练并在敏感数据上 DP 微调。作者称这是首个这样的流程。

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  5. DefensesarXiv 2610.04426

    UnAct:无需梯度的定向激活干预实现类别遗忘

    提出 UnAct,仅以前向激活干预从分类器中遗忘指定类别

    Published
    Evaluated models
    ResNet-18, ViT-B/16
    AbstractUnAct 用前向激活做类别遗忘。

    UnAct 是一种事后类别遗忘:删除请求可以只有少量该类图像,且没有标签和保留数据。它不用梯度,只对遗忘图像做前向。编辑对象是分类器所读的后期单元。

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  6. AttacksarXiv 2610.01365

    ReGap:无需真实隐私数据即可通过微调重新提取模型隐私关联

    提出 ReGap,用自生成伪监督微调恢复模型中的隐私关联

    Published
    Evaluated models
    GPT-2 Small, GPT-2 Medium, GPT-2 Large and others6

    Abstract提出无真实私有监督的恢复攻击 ReGap,实验显示微调可通过自生成数据唤醒模型潜在隐私关联,突显后适应隐私风险。

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  7. DefensesarXiv 2609.37858

    存储不等于策略:面向 LLM 遗忘的状态条件支持控制

    提出 Intervention Score 与 DIR-R,按干预效果选择并修订遗忘参数支持

    Published
    Evaluated models
    Llama 3 8B, Gemma 2 2B, Qwen2.5-7B and others4
    Abstract作者把局部化遗忘拆成初始干预选择和检查点修订,并在三个设置里分别给证据和边界。

    Storage Is Not Strategy 研究局部化 LLM 遗忘里两个常被混在一起的决定:一开始该更新哪些参数,以及优化改变模型之后这个选择要不要改。

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  8. AttacksarXiv 2609.33985

    研究:众包微调数据投毒可放大专有指令抽取

    提出主题锚点投毒,放大众包微调后的专有指令提取

    Published
    Attacker
    Black-box
    Evaluated models
    Qwen2.5-7B-Instruct, Qwen2.5-14B-Instruct, Llama-3.1-8B-Instruct and others4

    Abstract论文提出基于主题锚点的数据中毒攻击,证明低比例的中毒样本可在黑盒条件下隐蔽放大微调指令泄漏。

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  9. 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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  10. 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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  11. AttacksarXiv 2608.04741

    LoginTrap:针对 LLM Web Agent 的任务无关钓鱼式间接提示注入攻击

    提出 LoginTrap,以网页弹窗诱导 Web Agent 提交敏感信息

    Published
    Setting
    web agent
    Evaluated models
    GPT-4o, Gemini 3 Flash, Claude Sonnet 4 and others7

    Abstract揭示网络智能体在黑盒网页下的登录诱导风险,利用模糊测试生成话术并跨骨干和架构验证了隐私泄露威胁。

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  12. DefensesarXiv 2607.13336

    TC-UAP:针对图像转视频与微调定制的通用视频保护方法

    提出 TC-UAP,用身份级多帧扰动保护视频免被定制复现身份

    Published
    Evaluated models
    LTX-2.3, Wan2.2-5B
    AbstractTC-UAP 以可复用的时间一致扰动干扰两类定制共用的 VAE 隐空间。

    作者要解决的是:发布前如何给视频加不可见扰动,使片段既不能被拿去微调视频扩散模型,也不能被抽帧做 image-to-video。

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  13. AttacksarXiv 2605.05509

    研究者提出面向 Agent 浏览器的 Web 威胁模型,梳理 20 类攻击并在 4 个模型上复现

    提出面向智能体浏览器的 Web 威胁模型并复现混淆攻击

    Published
    Setting
    web agent
    Evaluated models
    GPT-5, Claude-Sonnet-4.6, Kimi-k2p5 and others8
    Abstract作者提出面向智能体浏览器的 Web 威胁模型与攻击分类,测试显示传统混淆攻击可复现且能绕过现有防御。

    这篇论文研究了智能体浏览器在面临传统 Web 攻击者时的安全性问题,指出当前针对提示词注入的安全假设无法抵御基于正常 Web 交互模式的混淆攻击。

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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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  15. 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

    Published
    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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