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Oct 12 · Monday

All papers

Found 33 papers
Filter5

Attack type

Prompt injection7Poisoning and backdoors5Extraction and theft9Detection evasion10

Attack technique

Side channels3

Defense type

Watermarking and provenance7

Impact

Unauthorized actions3Data and privacy leaks10Output manipulation7

System type

Models and APIs26LLM applications20AI Agent87coding agent7web agent4Multi-agent systems28

Attacker access

Black-box3

Attack surface / entry point

User input304Web pages and interfaces3Documents and messages13Tool responses11Tool and skill descriptions25Code repositories and dependencies20Retrieval corpus3Memory writes4Inter-agent messages2Images, audio and video102Training data65Weights and gradients67Distribution channels36Inference services and hardware18

Layer

Model layer13Application layer7Infrastructure layer13

System component

MCP and skills3Monitors4
Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 240papers have no tags yet and are not included in filtered results.
System type：Models and APIsSystem type：coding agentAttack surface / entry point：Documents and messagesAttack surface / entry point：Inter-agent messagesAttack surface / entry point：Inference services and hardwareClear all

Another 240papers do not have category tags yet and are not included in filtered results.

  1. DefensesarXiv 2610.11848

    SemField: training-free semantic watermark

    Proposes the semantic watermark SemField, which still identifies the attribution of generated text after paraphrasing and structural changes.

    arXiv
    2610.11848
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama-3.2-1B, Qwen3-1.7B, OPT-1.3B
    AbstractTraining-independent document-level semantic watermark.

    SemField is a zero-bit semantic watermark embedded by the provider at generation time and detected later with a key, used to identify the provider's own model text after paraphrasing and sentence-level structural changes.

    Paper analysisRead analysis
  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

    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。

    Paper analysisRead analysis
  4. AttacksarXiv 2610.05894

    研究者提出闭环偏见注入攻击

    提出闭环激活引导，在扩散语言模型去噪过程中注入定向偏见

    arXiv
    2610.05894
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Attacker
    Gray-box
    Evaluated models
    LLaDA-8B-Instruct, Dream-v0-Instruct-7B, LLaDA-MoE-7B-A1B-Instruct

    Abstract通过去噪中间概率反馈动态调节残差流转向，证明扩散语言模型在推理服务栈中存在闭环偏见注入脆弱性。

    Paper analysisRead analysis
  5. DefensesarXiv 2610.05163

    Researchers construct segmented prompt injection on Claude Code and Codex and release a boundary action audit benchmark

    提出 PAA 方法，在边界动作前审计长流程智能体的分阶段提示注入

    arXiv
    2610.05163
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    Claude Code (v2.1.172, Claude Sonnet 5), Codex CLI (v0.144.0, GPT-5.5)
    Abstract论文针对长流程注入提出边界动作审计与 PAA，在保持低误阻的同时实现高拦截率。

    本研究针对长流程智能体在执行任务时面临的分阶段间接提示注入威胁，提出了在动作生效前进行拦截的边界动作审计框架与路径对齐归因方法。

    Paper analysisRead analysis
  6. DefensesarXiv 2610.04763

    UT Austin 将 SynthID 水印嵌入基因组语言模型，凭 DNA 序列和密钥追溯 AI 生成片段

    把 SynthID 嵌入基因组语言模型采样，并用位置无关检测追溯生成序列

    arXiv
    2610.04763
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Carbon-500M, GENERator-v2-eukaryote-1.2b-base
    Abstract两个六碱基基因组模型嵌入 SynthID 后，位置无关验证能找回水印，作者称这只是统计检测。

    作者在两个六碱基基因组模型的采样里嵌入 SynthID，并让验证者在不知道起点、链和 token phase 时做一次校正后的检出。

    Paper analysisRead analysis
  8. Analysis and theoryarXiv 2610.02504

    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

    arXiv
    2610.02504
    Published
    Layer
    Infrastructure layer
    Setting
    Models and APIs
    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。

    Paper analysisRead analysis
  9. DefensesarXiv 2609.37310

    AutoMark：让自动研究流程自主发现更优 LLM 水印方案

    提出 AutoMark，自动搜索并统计检验 LLM 文本水印

    arXiv
    2609.37310
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama-3.1-8B-Instruct, Qwen3.8-27B, GPT-6 Astra and others5
    AbstractAutoMark 的贡献同时包含搜索出的水印，以及用于约束自动研究误报与评测漏洞的验证框架。

    AutoMark 是一套自动水印研究框架：先检查生成分布和误报性质，再比较检测、编辑鲁棒性与质量，最后人工分析组件并做留出评测。

    Paper analysisRead analysis
  10. Evaluation and benchmarksarXiv 2609.36817

    pikit：面向间接提示注入研究与评测的可组合工具包

    构建可组合工具包 pikit，评测编码智能体的间接提示注入

    arXiv
    2609.36817
    Published
    Layer
    Application layer
    Setting
    coding agent
    Abstractpikit 提供组合式间接注入评测。

    pikit 是面向间接提示注入研究的组合式工具包，主要贡献是组织攻击、投递通道、防御和判定流程，而非提出新的攻击优化算法。

    Paper analysisRead analysis
  12. DefensesarXiv 2609.36372

    TTMark：超越单 token 熵的成对无失真水印

    提出成对无失真水印 TTMARK，增强低熵下的机器文本检测

    arXiv
    2609.36372
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama3.2-3B-Instruct, Mistral-v0.3-7B-Instruct, Qwen2.5-7B-Instruct
    AbstractTTMARK 用水印化相邻 token 对利用条件熵，在多种无失真方案上提高检测并保持生成质量。

    TTMARK 是一种成对、无失真的文本水印：对相邻 token 的联合分布重加权，而不是逐 token 独立重加权。

    Paper analysisRead analysis
  13. Evaluation and benchmarksarXiv 2609.32635

    TrustFork：显示身份如何劫持 LLM 智能体编排的权限

    提出 TRUSTFORK 基准，评测展示身份劫持多智能体编排权限

    arXiv
    2609.32635
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems, coding agent
    Evaluated models
    GPT-5.6-Sol, GPT-5.6-Luna, Kimi-K3 and others8

    Abstract论文提出了多智能体安全基准 TRUSTFORK，揭示展示身份如何劫持操作权威，并证明事后验证无法弥补已发生的执行破坏。

    Paper analysisRead analysis
  15. Evaluation and benchmarksarXiv 2609.28915

    研究提出 ACE 语料库，评估内核级证据对 Agent 安全检测的有效性

    构建 ACE 语料，评测内核级证据对 Agent 攻击检测的有效性

    arXiv
    2609.28915
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    AdaBoost, XGBoost, TabPFN and others10
    AbstractACE 用配对的内核与应用层证据表明，syscall 痕迹可判别智能体攻击，两层组合通常更好。

    ACE 是一份把智能体会话的内核 syscall 痕迹与应用层清单、转录配对起来的检测语料和基准。。

    Paper analysisRead analysis
  16. DefensesarXiv 2609.14257

    DenMark：面向扩散语言模型的鲁棒语义水印

    提出 DenMark，在扩散语言模型去噪时注入可抵抗保义编辑的语义水印

    arXiv
    2609.14257
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    LLaDA-8B, LLaDA1.5-8B, LLaDA2.0-mini and others4
    AbstractDenMark 用 rollout 在 DLM 去噪中累积语义水印，扫描检测以抵抗保义编辑。

    DenMark 是面向扩散语言模型的语义水印：在去噪的局部更新里注入密钥相关语义信号，并在编辑改变 token 边界后仍检测。现有句子级语义水印要先完成整个语义单元再打分取舍，而 DLM 以任意顺序更新掩码，中间单元不完整。DenMark 把续写划成固定 token 区域，对每个局部候选做不提交的 rollout 补全，按语义分 gb 的平均选择更新，从而沿轨迹累积证据。

    Paper analysisRead analysis
  17. AttacksarXiv 2609.13889

    PMPA：针对 OpenClaw 与 Claude Code 的持久记忆投毒攻击

    提出 PMPA，经外部源向 Agent 持久记忆投毒并跨会话泄露隐私

    arXiv
    2609.13889
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    DeepSeek-V4-Flash, DeepSeek-V4-Pro, Qwen3-Max

    Abstract论文提出了持久记忆投毒攻击 PMPA，报告了 harness 智能体在处理外部数据时易受跨会话记忆投毒与隐私泄露的风险。

    Paper analysisRead analysis
  19. DefensesarXiv 2609.01677

    Skill-as-API：面向智能体软件工程的机密多智能体协作协议

    提出 Skill-as-API，隔离多智能体协作中的 skill 正文与系统提示词

    arXiv
    2609.01677
    Published
    Layer
    Application layer
    Setting
    Multi-agent systems, coding agent
    Abstract协议把 skill 正文留在所有者侧，四层限制发现内容、权限期限和注入拼接。

    Skill-as-API 是面向智能体软件工程的协调协议，用来在跨组织调用时把 skill 的代码和系统提示词留在所有者进程。作者针对三条威胁：经通道或命名空间抽取专有方法，用注入让被调用模型输出系统提示词，以及协作结束后权限继续保留。

    Paper analysisRead analysis
  20. 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

    arXiv
    2608.25431
    Published
    Layer
    Infrastructure layer
    Setting
    Models and APIs
    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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