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Oct 12 · Monday

All papers

Found 11 papers
Filter5

Attack type

Prompt injection3Poisoning and backdoors8

Attack technique

Selection manipulation3

Impact

Harmful content5Unauthorized actions39Data and privacy leaks12Output manipulation11Availability and reliability3

System type

Models and APIs5AI Agent6

Attack surface / entry point

User input31Web pages and interfaces16Documents and messages27Tool responses16Tool and skill descriptions5Code repositories and dependencies5Retrieval corpus27Memory writes7Inter-agent messages10Images, audio and video15Training data6Weights and gradients6Distribution channels12Inference services and hardware5

Layer

Model layer5Application layer6Training and data layer24

System component

MCP and skills4
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.
Impact：Output manipulationAttack surface / entry point：Tool and skill descriptionsAttack surface / entry point：Training dataLayer：Model layerLayer：Application layerClear all

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  1. DefensesarXiv 2610.02432

    论文提出 LLM 输入扰动鲁棒性度量，并发布 ASA 攻击与 AttestMCP 防护

    提出 ASA 与 AttestMCP，度量并降低 LLM 对输入序列扰动的敏感

    arXiv
    2610.02432
    Published
    Layer
    Application layer
    Setting
    LLM applications, AI Agent
    Attacker
    Black-box
    Evaluated models
    Gemma-3-27B-Instruct, Gemma-3-4B-Instruct, Llama-3.2-3B-Instruct and others12
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  2. DefensesarXiv 2610.00348

    NEEDLE：通过权重正交化移除 LLM 后门

    提出 NEEDLE，用权重正交化在已知触发器时去除大模型后门

    arXiv
    2610.00348
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Gemma-3-4B-IT, Qwen3-4B-Instruct-2507, Gemma-3-12B-IT

    AbstractNEEDLE 用闭式权重编辑去掉已知触发器的后门，并保住拒绝子空间。

    Paper analysisRead analysis
  3. AttacksarXiv 2609.30383

    研究者提出技能级联攻击

    提出技能级联攻击，把有害目标拆散到多个技能里绕过单技能扫描

    arXiv
    2609.30383
    Published
    Layer
    Application layer
    Setting
    AI Agent
    Evaluated models
    GPT-5.4, GPT-5.4 Mini, Claude Opus 4.6 and others11

    Abstract论文探讨了多技能智能体系统面临的技能级联攻击风险，提出自动化红队框架与基准集，指出跨技能交互存在安全防护盲区。

    Paper analysisRead analysis
  5. AttacksarXiv 2609.17817

    论文：用投毒基准污染自改进编码 Agent，令其后续版本写出漏洞代码

    提出基准投毒攻击，使自修改编码 Agent 跨代写出含漏洞代码

    arXiv
    2609.17817
    Published
    Layer
    Application layer
    Setting
    coding agent
    Evaluated models
    Claude Sonnet 4.5, Qwen3.5-397B, gpt-oss-120b and others5

    Abstract论文报告了恶意基准能污染自修改代码智能体的进化过程并持续输出漏洞，警示系统设计需纳入安全约束。

    Paper analysisRead analysis
  7. DefensesarXiv 2609.00786

    MROP：针对 Deep JSCC 后门攻击的掩码区域优化净化方法

    提出 MROP，在发射端推理时纯化输入以抵御训练投毒后门

    arXiv
    2609.00786
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    DeepJSCC, SwinJSCC
    AbstractMROP 不重训练，在发射端纯化补丁后门。

    MROP 是 deep JSCC 无线图像传输上的推理时输入纯化，对象是矩形补丁后门。

    Paper analysisRead analysis
  8. AttacksarXiv 2608.29381

    Researchers propose five categories of safety failures in agent checkpoint rollback and implement three attacks on Hermes, Cline, and LangGraph

    提出利用检查点回滚破坏 Agent 执行连续性的攻击

    arXiv
    2608.29381
    Published
    Layer
    Application layer
    Setting
    AI Agent

    Abstract论文系统研究了智能体 C/R 的安全性，分析了合法回滚破坏执行连续性的机理，并实证评估了其普遍性与危害。

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  10. DefensesarXiv 2603.03206

    理解并缓解大语言模型引导中的数据集污染

    用 Lee-Valiant 稳健均值缓解 steering 数据集污染

    arXiv
    2603.03206
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    Llama-3.2-3B-Instruct, Mistral-7b-Instruct-v0.3, OLMo-2-1124-7B-Instruct
    Abstract数据污染会改 steering，协调注入最强。

    作者研究 contrastive steering 的训练数据被污染后，推理时的行为控制还剩多少，以及稳健均值能否接住。

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  11. DefensesarXiv 2602.03085

    Triggers in the Haystack: Extracting and Reconstructing LLM Backdoor Triggers

    提出推理扫描器，在仅有模型文件时检测后门并重构触发词

    arXiv
    2602.03085
    Published
    Layer
    Model layer
    Setting
    Models and APIs
    Evaluated models
    gemma-3-270m-it, DeepSeek-R1-Distill-Qwen-1.5B, Phi-4-mini-instruct and others8
    Abstract用记忆泄漏和内部信号重构触发词。

    这是一个只做推理的 sleeper agent 扫描器：在不知触发词和目标行为时，检测因果语言模型里的条件后门并重构触发词。。问题在于，后门只在触发词出现时偏离基线，而开放仓库和网页投毒使检测方通常没有触发词、目标或激活语料。

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