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Oct 12 · Monday

All papers

Found 29 papers

Filter5

System type

LLM applications11 AI Agent16 web agent4

Attacker access

Black-box5

Attack surface / entry point

User input8 Web pages and interfaces5 Documents and messages5 Tool responses3 Retrieval corpus5 Images, audio and video12 Training data4

Layer

Prompt layer73 Model layer13 Application layer26 Training and data layer3

System component

RAG5 Reasoning chains3 Vision12 Embeddings3 Multimodal14

Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 254papers have no tags yet and are not included in filtered results.

Attack technique：Gradient and representation optimization Attack technique：Conversation structure manipulation Attack technique：Cross-modal payloads Layer：Application layer Layer：Training and data layer Clear all

Another 254papers do not have category tags yet and are not included in filtered results.

AttacksarXiv 2610.12644Oct 9
Cancerous backdoor attack: progressive activation via invisible tokens and a stage controller.
Proposes the Cancerous backdoor, which progressively corrupts reasoning using invisible stage tokens.
arXiv
2610.12644
Published
Oct 9
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Llama-3.1-8B-Instruct, Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentReasoning chains
AbstractHidden tokens plus a stage controller make the backdoor progressively corrupt reasoning upon triggering, and the authors believe that looking only at the answer will miss it.
Cancerous Backdoor Attack changes LLM backdoors from one-time activation to multi-stage reasoning corruption that accumulates progressively with triggers.
Paper analysisRead analysis
AttacksarXiv 2610.09973Oct 7
从期望危害到似然：LLM 智能体越狱的概率重构
提出 OPUR 攻击，用似然梯度优化对抗后缀越狱 LLM 智能体
arXiv
2610.09973
Published
Oct 7
Layer
Application layer
Setting
AI Agent
Attacker
White-box
Evaluated models
Llama-3.1-8B-Instruct, Ministral-8B-Instruct-2410
- AttacksJailbreaks
- TechniqueGradient and representation optimization
Paper analysisRead analysis
AttacksarXiv 2610.09240Oct 7
WebMirage hijacks visual web agents' browser operations with adversarial images
提出 WebMirage，用局部对抗图像劫持视觉网页 Agent 的浏览器操作
arXiv
2610.09240
Published
Oct 7
Layer
Application layer
Setting
web agent
Evaluated models
LLaVA-v1.5-13B, LLaVA-v1.6-34B, MiniCPM-o-8B and others6
- AttacksPrompt injection
- TechniqueGradient and representation optimization
- ComponentVision
Abstract论文提出了针对网络智能体的端到端视觉红队框架 WebMirage，揭示了从视觉定位到浏览器执行的安全脆弱性。
WebMirage 是一个面向视觉定位网络智能体的流水线感知端到端红队攻击框架。
Paper analysisRead analysis
AttacksarXiv 2610.04985Oct 4
研究揭示 Jev 作为应用决策层的安全与隐私风险
评估应用决策层的输入注入与隐私推断风险
arXiv
2610.04985
Published
Oct 4
Layer
Application layer
Setting
LLM applications
Attacker
Black-box, Gray-box
Evaluated models
Jev, NanoJev
- AttacksPrompt injection
- TechniqueRole-play and persuasion
AbstractJev 的类型化决策可被输入与训练操纵，也会泄露信息，并同时支持检测与滥用。
Jev 是 TypeSafe 的 System One 模型，把应用 state 和固定问题变成 Choice、Noul 或 Score 的类型化决策与概率。作者用官方 API 和可改训练的 NanoJev，考察它作为决策层时的操纵、泄露与双重用途。
Paper analysisRead analysis
AttacksarXiv 2610.01871Oct 1
研究者提出 IMMRAG 攻击，可从多模态 RAG 数据存储中提取医学与文档图像
提出 imMRAG 攻击，从多模态 RAG 私有图像库提取内容
arXiv
2610.01871
Published
Oct 1
Layer
Application layer
Setting
LLM applications
Attacker
Black-box
Evaluated models
Lumina-mGPT, Gemini 2.5 Flash Image Preview, CLIP ViT-B/16 and others5
- AttacksExtraction and theft
- TechniqueCross-modal payloads
- ComponentRAG
Paper analysisRead analysis
AttacksarXiv 2610.01367Oct 1
研究：高质量数据筛选挡不住投毒
提出 Bi-QSTO，优化能穿过质量筛选并在微调中削弱安全对齐的投毒样本
arXiv
2610.01367
Published
Oct 1
Layer
Training and data layer
Setting
Models and APIs
Evaluated models
Llama2-7B-Chat, Llama3-8B-Instruct, Qwen2-7B-Instruct and others5
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentFine-tuning and open weights
Abstract质量筛选挡不住部分高分样本的安全削弱。
Bi-QSTO 研究一个实际训练缺口：基于质量的数据选择会先于微调决定样本去留，而以往微调投毒多假设毒样本直接进入训练集。已对齐 LLM 的拒答会被少量有害或看似无害的微调样本削弱。作者用三个问题串起实验：选择能否排除毒样本。
Paper analysisRead analysis
AttacksarXiv 2609.39788Sep 30
研究：多智能体系统潜在通信链路可被训练用于提升有害顺从
提出只训练或篡改多智能体潜在通信链路来提高有害遵从
arXiv
2609.39788
Published
Sep 30
Layer
Application layer
Setting
Multi-agent systems
Evaluated models
Llama-3.2-3B-Instruct, Qwen2.5-3B-Instruct, Gemma-3-1B-IT and others7
- AttacksModel tampering
- TechniqueGradient and representation optimization
Abstract潜在链路本身即可改变冻结智能体系统的安全，奖励既能攻击也能修复。
学习得到的潜在通信链路会改变由冻结、对齐智能体组成的系统是否遵从有害请求。。作者研究三种拓扑：双智能体、三级顺序协作，以及双专家加汇总。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.35912Sep 28
MMSkillRisk：多模态技能图像中的隐藏指令可诱导 Agent 执行未授权操作
提出 MMSkillRisk 基准，评测多模态技能图像中的隐藏指令诱导 Agent 越权操作
arXiv
2609.35912
Published
Sep 28
Layer
Application layer
Setting
coding agent
Evaluated models
GPT-5.6-sol, Kimi-K2.6, Kimi-K3 and others6
- AttacksPrompt injection
- TechniqueCross-modal payloads
- ComponentMCP and skills
Abstract论文提出 MMSkillRisk 基准与 NCVA 攻击，揭示多模态技能中教学图像潜藏指令可引发未授权操作并与良性任务共存。
Paper analysisRead analysis
AttacksarXiv 2609.27542Sep 23
Researchers use control token injection to delete gpt-oss-20b's reasoning chain and bypass chain-of-thought monitoring
提出控制标记注入，清空工具 Agent 的思维链以绕过推理监控
arXiv
2609.27542
Published
Sep 23
Layer
Application layer
Setting
AI Agent
Evaluated models
openai/gpt-oss-20b, google/gemma-4-26B-A4B-it, deepseek-ai/DeepSeek-R1-Distill-Qwen-7B and others4
- AttacksPrompt injection
- TechniqueConversation structure manipulation
- ComponentReasoning chains
Abstract论文阐明控制标记注入可抑制 CoT 使监控失效并促成拒绝绕过，且工具执行受解析器宽容度影响。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.09404Sep 9
MMPIBench：多模态提示注入对六种 Agent 框架的跨框架评测
用 MMPIBench 评测多模态提示注入对 Agent 框架的影响
arXiv
2609.09404
Published
Sep 9
Layer
Application layer
Setting
AI Agent
Evaluated models
Claude Opus 4.8, GPT-5.4, Gemini 3.1 Pro and others6
- AttacksPrompt injection
- TechniqueCross-modal payloads
- ComponentVision
Abstract论文提出 MMPIBench 评测多模态提示注入对智能体的影响，发现模型主导行为、尝试远超完成，音频通道缺乏防护。
Paper analysisRead analysis
AttacksarXiv 2609.04533Sep 4
Repeat-After-Me：针对黑盒 VLM 的自适应视觉提示注入攻击
提出 Repeat-After-Me 自适应视觉提示注入，诱导 VLM 智能体执行指定任务
arXiv
2609.04533
Published
Sep 4
Layer
Application layer
Setting
AI Agent
Evaluated models
Claude-Opus-4.7, GPT-5.5, Gemini-3.1-Pro and others6
- AttacksPrompt injection
- TechniqueAutomated search
- ComponentVision
Abstract论文提出了针对黑盒 VLM 的前缀诱导自适应视觉提示注入，在多款模型上诱发了工具调用与隐私泄露。
Paper analysisRead analysis
AttacksarXiv 2608.23471Aug 25
InjecMEM: Memory Injection Attacks against LLM Agent Memory Systems
提出 InjecMEM，一次注入 Agent 记忆使后续查询输出预设内容
arXiv
2608.23471
Published
Aug 25
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, Mistral-7B-Instruct-v0.3 and others6
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentMemory
Paper analysisRead analysis
AttacksarXiv 2607.14493Jul 16
LogInject 框架披露 LLM 日志分析中的被动提示注入
提出 LogInject 框架，评测日志分析中的被动提示注入及缓解
arXiv
2607.14493
Published
Jul 16
Layer
Application layer
Setting
LLM applications
Evaluated models
GPT-4o, Claude 3.5 Sonnet, Llama-3-70B-Instruct
- AttacksPrompt injection
- TechniqueEncoding and obfuscation
- ComponentGuardrails and judges
Abstract论文评估了 LLM 日志分析中的上下文污染风险，提出分片拼接等攻击与三层防御，指出必须结合架构隔离与人工介入。
Paper analysisRead analysis
AttacksarXiv 2606.12716Jun 11
PaperGuard：针对多模态 AI 同行评审的攻击与防御基准
提出 PaperGuard，用图文注入与梯度扰动抬高自动审稿分数并检测恶意指令
arXiv
2606.12716
Published
Jun 11
Layer
Application layer
Setting
LLM applications
Evaluated models
Claude-sonnet-4.5, GPT-4o, GPT-4.1 and others11
- AttacksPrompt injection
- TechniqueCross-modal payloads
- ComponentVision
AbstractPaperGuard 测到提示注入与白盒视觉抬分，并比较分块检索防御。
PaperGuard同时评测并检测多模态 AI 审稿中的评分操纵。
Paper analysisRead analysis
AttacksarXiv 2606.11265Jun 9
论文提出 CRCP 投毒框架，揭示 RAG 分块与重排序下的投毒失效问题
提出 CRCP 框架，优化分块与重排下的 RAG 语料投毒
arXiv
2606.11265
Published
Jun 9
Layer
Application layer
Setting
LLM applications
Evaluated models
GPT-4o, DeepSeek-R1, Qwen3-Max and others11
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentRAG
AbstractCRCP 把投毒做成多阶段一致性问题。
CRCP 研究的是带分块和重排的 RAG 语料投毒，而不是只提高文档级检索分数。
Paper analysisRead analysis
AttacksarXiv 2606.09499Jun 8
研究者提出针对世界模型的机器人学习管线投毒攻击
提出 VPH 与 VTH，只改视频帧劫持世界模型并投毒下游策略
arXiv
2606.09499
Published
Jun 8
Layer
Training and data layer
Setting
Embodied systems and robotics
Evaluated models
Cosmos-Predict 2.5, Cosmos-Predict 2.5 AC, Dino WM and others4
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentVideo
Abstract只改视频帧经世界模型投毒。
只改看似安全的视频帧，经世界模型之后才改变下游机器人策略。 问题在于世界模型进入数据生成后，数据集检查看到的仍是安全演示，合成轨迹却可以变危险。
Paper analysisRead analysis
AttacksarXiv 2605.28074May 27
SilentRetrieval: Hijacking retrieval-augmented generation with semantically preserved adversarial data poisoning
提出 SilentRetrieval，以语义保持对抗文档投毒劫持 RAG 检索与生成
arXiv
2605.28074
Published
May 27
Layer
Application layer
Setting
LLM applications
Evaluated models
Llama-2-7B-Chat, Mistral-7B-Instruct-v0.2, Qwen-7B-Chat and others10
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentRAG
Abstract两阶段投毒在单文档协议下兼顾检索劫持、生成操纵与近良性困惑度。
SilentRetrieval 是针对 RAG 的两阶段语料投毒，在每查询一篇、合成目标答案的统一协议下，同时处理检索劫持、生成操纵和流畅度。
Paper analysisRead analysis
AttacksarXiv 2605.24421May 23
研究者提出日志型提示注入攻击，可抑制 SOC 中 LLM 分析助手的恶意日志识别
提出日志基底提示注入，操纵 SOC 分析员的分类、摘要与处置建议
arXiv
2605.24421
Published
May 23
Layer
Application layer
Setting
LLM applications
Evaluated models
gpt-4o-mini
- AttacksPrompt injection
- TechniqueConversation structure manipulation
Abstract在 gpt-4o-mini 上，人格劫持与上下文操纵可改写 SOC 分析输出，直接覆盖在分类上无效。
Paper analysisRead analysis
AttacksarXiv 2605.10253May 11
Knowledge poisoning attacks against multimodal retrieval-augmented generation in healthcare
提出 M3 Att，在不知查询时投毒医学多模态 RAG 并误导临床决策
arXiv
2605.10253
Published
May 11
Layer
Application layer
Setting
LLM applications
Evaluated models
GPT-4o, GPT-5 Chat, Gemini-2.5-Flash and others8
- AttacksPoisoning and backdoors
- TechniqueGradient and representation optimization
- ComponentRAG
Paper analysisRead analysis
AttacksarXiv 2603.16960Mar 17
针对现代视觉语言模型的对抗攻击
对开源视觉购物智能体实施商品图对抗攻击并诱导买错
arXiv
2603.16960
Published
Mar 17
Layer
Application layer
Setting
web agent
Evaluated models
LLaVA-v1.5-7B, Qwen2.5-VL-7B
- AttacksPrompt injection
- TechniqueGradient and representation optimization
- ComponentVision
Abstract作者称 LLaVA 易被三种攻击诱导买错，Qwen 正确购买率仍接近其干净条件。
作者在自建电商环境里比较两个开源 VLM 购物智能体对图像对抗扰动的鲁棒性。
Paper analysisRead analysis

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Attack type

Attack technique

Impact

System type

Attacker access

Attack surface / entry point

Layer

System component

Cancerous backdoor attack: progressive activation via invisible tokens and a stage controller.

从期望危害到似然：LLM 智能体越狱的概率重构

WebMirage hijacks visual web agents' browser operations with adversarial images

研究揭示 Jev 作为应用决策层的安全与隐私风险

研究者提出 IMMRAG 攻击，可从多模态 RAG 数据存储中提取医学与文档图像

研究：高质量数据筛选挡不住投毒

研究：多智能体系统潜在通信链路可被训练用于提升有害顺从

MMSkillRisk：多模态技能图像中的隐藏指令可诱导 Agent 执行未授权操作

Researchers use control token injection to delete gpt-oss-20b's reasoning chain and bypass chain-of-thought monitoring

MMPIBench：多模态提示注入对六种 Agent 框架的跨框架评测

Repeat-After-Me：针对黑盒 VLM 的自适应视觉提示注入攻击

InjecMEM: Memory Injection Attacks against LLM Agent Memory Systems

LogInject 框架披露 LLM 日志分析中的被动提示注入

PaperGuard：针对多模态 AI 同行评审的攻击与防御基准

论文提出 CRCP 投毒框架，揭示 RAG 分块与重排序下的投毒失效问题

研究者提出针对世界模型的机器人学习管线投毒攻击

SilentRetrieval: Hijacking retrieval-augmented generation with semantically preserved adversarial data poisoning

研究者提出日志型提示注入攻击，可抑制 SOC 中 LLM 分析助手的恶意日志识别

Knowledge poisoning attacks against multimodal retrieval-augmented generation in healthcare

针对现代视觉语言模型的对抗攻击