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Oct 13 · Tuesday

All papers

Found 67 papers

Filter5

System type

Models and APIs36 LLM applications8 AI Agent23 coding agent3 web agent4 Embodied systems and robotics4

Attacker access

Black-box11 White-box15

Attack surface / entry point

User input23 Web pages and interfaces5 Tool responses3 Tool and skill descriptions10 Retrieval corpus6 Images, audio and video31 Distribution channels9

Layer

Prompt layer37 Model layer12 Application layer28 Infrastructure layer2

System component

MCP and skills10 RAG6 Guardrails and judges4 Reasoning chains3 Vision28 Audio4 Embeddings3 Multimodal33

Select any value within a category; different categories must all match. Counts include the other filters you have selected.Another 213papers have no tags yet and are not included in filtered results.

Attack technique：Gradient and representation optimization Attack technique：Selection manipulation Layer：Prompt layer Layer：Application layer Layer：Infrastructure layer Clear all

Another 213papers do not have category tags yet and are not included in filtered results.

AttacksarXiv 2610.12524Oct 8
An exploration of attacks on AI agent systems
Retest and extend prompt injection against AI agents, and use Agent Card deception to manipulate A2A selection
arXiv
2610.12524
Published
Oct 8
Layer
Application layer
Setting
AI Agent
Evaluated models
Ollama Mistral 0.9.5, llama3-8b-8192, gemini-2.0-flash-lite and others5
- AttacksPrompt injection
- TechniqueEncoding and obfuscation
- ComponentMCP and skills
AbstractSingle-agent filtering and classification can be bypassed by format or wording, and A2A card selection is also biased toward names and descriptions.
The authors retested prompt injection on their own LangChain, AutoGen, and A2A systems, and modified the Agent Card to see if choices shifted. The abstract states the experiments mainly highlight single-agent weaknesses while extending attacks to multi-agent.
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 2610.09708Oct 7
研究者提出利用祖先 VLM 的黑盒对抗补丁攻击，可降低 VLA 任务成功率
提出利用祖先 VLM 优化对抗补丁，使已部署 VLA 任务失败
arXiv
2610.09708
Published
Oct 7
Layer
Prompt layer
Setting
Embodied systems and robotics
Evaluated models
OpenVLA, UniVLA, π0.5
- TechniqueGradient and representation optimization
- ComponentVision
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 2610.09027Oct 7
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
arXiv
2610.09027
Published
Oct 7
Layer
Infrastructure layer
Setting
Models and APIs
Evaluated models
Qwen3-8B, Qwen3-VL-8B-Instruct, Gemma-4-12B
- AttacksPrompt injection
- TechniqueDormant triggers
- ComponentVision
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。
Paper analysisRead analysis
AttacksarXiv 2610.08331Oct 6
STCA：研究者提出针对自动驾驶视频 VLM 的时空一致性对抗攻击
提出 STCA，对驾驶视频 VLM 构造可迁移的时空对抗扰动
arXiv
2610.08331
Published
Oct 6
Layer
Prompt layer
Setting
Models and APIs
Attacker
No-box (offline transfer)
Evaluated models
Video-LLaVA-7B, Qwen2.5-VL-7B, Dolphins
- AttacksDetection evasion
- TechniqueGradient and representation optimization
- ComponentVideo
Paper analysisRead analysis
AttacksarXiv 2610.08098Oct 6
CORSA：面向技能路由器的技能注入攻击可跨路由器与 LLM 迁移
提出 CORSA，按任务簇优化技能注入的检索与执行
arXiv
2610.08098
Published
Oct 6
Layer
Application layer
Setting
coding agent
Evaluated models
GPT-5.4, DeepSeek-V4-Pro, Qwen3.8-27B and others5
- AttacksPoisoning and backdoors
- TechniqueSelection manipulation
- ComponentMCP and skills
Paper analysisRead analysis
AttacksarXiv 2610.06814Oct 6
TAPDreamer：可跨任务迁移的世界动作模型对抗补丁
TAPDreamer 用公开编码器做固定补丁。
arXiv
2610.06814
Published
Oct 6
Layer
Prompt layer
Setting
Embodied systems and robotics
Evaluated models
FastWAM, DreamWAM-uncond, DreamWAM-joint and others4
- TechniqueGradient and representation optimization
- ComponentVision
Paper analysisRead analysis
AttacksarXiv 2610.06977Oct 4
IAU-FOA：面向闭源多模态大模型的可迁移对抗攻击方法
提出 IAU-FOA，构造可迁移图像扰动以诱导多模态模型输出目标语义
arXiv
2610.06977
Published
Oct 4
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Qwen2.5-VL-7B, Qwen2.5-VL-72B, InternVL3-14B and others17
- TechniqueGradient and representation optimization
- ComponentVision
Paper analysisRead analysis
AttacksarXiv 2610.03430Oct 2
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
arXiv
2610.03430
Published
Oct 2
Layer
Infrastructure layer
Setting
Models and APIs
Evaluated models
Llama-3-8B-Instruct, Qwen2.5-7B-Instruct, Mistral-7B-Instruct-v0.3 and others4
- TechniqueGradient and representation optimization
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。
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
AttacksarXiv 2609.30383Sep 25
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
Sep 25
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-5.4, GPT-5.4 Mini, Claude Opus 4.6 and others11
- AttacksPoisoning and backdoors
- TechniqueDefense-targeted
- ComponentMCP and skills
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
AttacksarXiv 2609.26761Sep 23
A2M: A two-stage agent hijacking framework targeting the MCP ecosystem
提出两阶段框架 A2M，优化 MCP 工具元数据与返回载荷以劫持 Agent
arXiv
2609.26761
Published
Sep 23
Layer
Application layer
Setting
AI Agent
Evaluated models
GLM-4.6, Qwen3-Max, DeepSeek-V3.1 and others5
- AttacksPrompt injection
- TechniqueAutomated search
- ComponentMCP and skills
Paper analysisRead analysis
InterpretabilityarXiv 2609.24801Sep 22
研究用 XAI 归因分析 Prompt Guard 2，同义词替换可翻转其提示注入判定
用词级归因指导同义词替换，翻转提示注入检测并验证越狱
arXiv
2609.24801
Published
Sep 22
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Prompt Guard 2 (86M), Llama 3.1 8B
- DefensesDetection and filtering
- AttacksDetection evasion
- TechniqueGradient and representation optimization
- ComponentGuardrails and judges
Abstract作者称检测靠分散的词法线索。
Paper analysisRead analysis
AttacksarXiv 2609.03884Sep 3
HookPry：利用插件生命周期钩子更新劫持 AI Agent 框架
提出 HOOKPRY，用插件钩子版本更新供应链劫持 Agent 框架
arXiv
2609.03884
Published
Sep 3
Layer
Application layer
Setting
AI Agent
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentMCP and skills
Paper analysisRead analysis
AttacksarXiv 2609.02172Sep 2
BOSS：以广度优先后缀搜索改进 GCG 越狱优化
提出广度优先后缀搜索 BOSS，改进 GCG 越狱优化
arXiv
2609.02172
Published
Sep 2
Layer
Prompt layer
Setting
Models and APIs
Attacker
White-box
Evaluated models
Llama-2-7B-Chat, Yi-1.5-9B-Chat, Qwen2-7B-Instruct and others8
- AttacksJailbreaks
- TechniqueGradient and representation optimization
AbstractBOSS 把 GCG 类优化从单条深搜索改为多条短轨迹加源侧诊断。
BOSS 是插在 GCG 类离散 suffix 优化上的搜索框架：局部梯度更新不变，变的是固定预算花在深度还是广度。
Paper analysisRead analysis
AttacksarXiv 2609.02035Sep 2
ISM：通过语义匹配隐式操纵 LLM Agent 的技能选择
提出 ISM，用语义匹配隐式操纵 Agent 技能选择
arXiv
2609.02035
Published
Sep 2
Layer
Application layer
Setting
AI Agent
Evaluated models
Llama-2-7B, Llama-3-8B, Llama-3.1-8B and others8
- AttacksPoisoning and backdoors
- TechniqueSelection manipulation
- ComponentMCP and skills
Paper analysisRead analysis
AttacksarXiv 2609.05525Sep 2
DIVA：针对离散扩散视觉语言模型的跨步条件传播视觉越狱
提出 DIVA，利用跨步条件传播对离散扩散视觉语言模型实施视觉越狱
arXiv
2609.05525
Published
Sep 2
Layer
Prompt layer
Setting
Models and APIs
Attacker
White-box
Evaluated models
LLaDA-V, MMaDA, LLaDA2.0-Uni
- AttacksJailbreaks
- TechniqueGradient and representation optimization
- ComponentVision
Paper analysisRead analysis
AttacksarXiv 2608.27800Aug 28
ContextLeak：通过恶意工具窃取 LLM Agent 运行时上下文
提出 ContextLeak，用强化学习优化恶意工具描述，诱导 Agent 外泄运行时上下文
arXiv
2608.27800
Published
Aug 28
Layer
Application layer
Setting
AI Agent
Evaluated models
Qwen-3-8B, GPT-OSS-20B, Gemma-4-E4B-it and others11
- AttacksPrompt injection
- TechniqueSelection manipulation
- ComponentMCP and skills
Paper analysisRead analysis
AttacksarXiv 2608.25776Aug 26
EvoMal attack uses self-poisoning of self-evolving coding agents to achieve worm-like propagation
提出 EVOMAL，诱导自演化编码 Agent 在创建技能时自我投毒并蠕虫传播
arXiv
2608.25776
Published
Aug 26
Layer
Application layer
Setting
coding agent
Evaluated models
Devstral-Small-2, Gemma-4-31B-IT, Qwen3-Coder-Next and others7
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentMCP and skills
Abstract论文指出了自演化智能体模仿检索代码的自我毒化漏洞，提出 EVOMAL 攻击与防御，阐明了供应链新风险。
Paper analysisRead analysis

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All papers

Attack type

Attack technique

Impact

System type

Attacker access

Attack surface / entry point

Layer

System component

An exploration of attacks on AI agent systems

从期望危害到似然：LLM 智能体越狱的概率重构

研究者提出利用祖先 VLM 的黑盒对抗补丁攻击，可降低 VLA 任务成功率

WebMirage hijacks visual web agents' browser operations with adversarial images

P-VMI：对抗图像经 KV cache 持续影响多轮对话

STCA：研究者提出针对自动驾驶视频 VLM 的时空一致性对抗攻击

CORSA：面向技能路由器的技能注入攻击可跨路由器与 LLM 迁移

TAPDreamer：可跨任务迁移的世界动作模型对抗补丁

IAU-FOA：面向闭源多模态大模型的可迁移对抗攻击方法

JIL：通过对抗后缀操纵 LLM 请求调度优先级

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

研究者提出技能级联攻击

A2M: A two-stage agent hijacking framework targeting the MCP ecosystem

研究用 XAI 归因分析 Prompt Guard 2，同义词替换可翻转其提示注入判定

HookPry：利用插件生命周期钩子更新劫持 AI Agent 框架

BOSS：以广度优先后缀搜索改进 GCG 越狱优化

ISM：通过语义匹配隐式操纵 LLM Agent 的技能选择

DIVA：针对离散扩散视觉语言模型的跨步条件传播视觉越狱

ContextLeak：通过恶意工具窃取 LLM Agent 运行时上下文

EvoMal attack uses self-poisoning of self-evolving coding agents to achieve worm-like propagation