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Oct 13 · Tuesday

All papers

Found 14 papers

Filter5

Attack type

Attack technique

Gradient and representation optimization4 Automated search7 Encoding and obfuscation3 Cross-modal payloads4 Dormant triggers5

Impact

Unauthorized actions6 Output manipulation5

System type

AI Agent12 web agent6

Attack surface / entry point

User input29 Web pages and interfaces14 Documents and messages17 Tool responses25 Tool and skill descriptions21 Code repositories and dependencies7 Retrieval corpus14 Memory writes7 Inter-agent messages5 Images, audio and video9 Distribution channels17

Layer

Application layer14

System component

Vision4 Multimodal4

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：Automated search Attack technique：Encoding and obfuscation Attack surface / entry point：Web pages and interfaces Layer：Application 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 2610.09240Oct 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 2610.08951Oct 7
ASPIRE：面向 LLM Agent 的开放式提示注入红队引擎
提出 ASPIRE 行为图搜索，开放式发现 Agent 提示注入漏洞
arXiv
2610.08951
Published
Oct 7
Layer
Application layer
Setting
AI Agent
Evaluated models
Gemini-3.7-Flash, DeepSeek-V4-Flash, Gemini-2.5-flash
- AttacksPrompt injection
- TechniqueAutomated search
AbstractASPIRE 把提示注入红队从载荷优化改成行为图引导的开放式漏洞发现。
ASPIRE 是给搭建方用的智能体提示注入红队引擎，目标是开放式画出可利用的行为路径，而不是在固定任务上打磨一条载荷。
Paper analysisRead analysis
AttacksarXiv 2609.33628Sep 27
用课程强化学习对前沿模型做提示注入红队测试
用跨目标课程强化学习训练提示注入红队，攻破前沿智能体
arXiv
2609.33628
Published
Sep 27
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-4o-mini, GPT-5-nano, GPT-5.6-Luna and others12
- AttacksPrompt injection
- TechniqueAutomated search
Abstract提出跨目标模型课程强化学习，攻破前沿模型冷启动瓶颈并实现跨模型迁移。
该研究针对强化学习自动化提示注入红队在攻击前沿大模型时的冷启动问题，提出了一种基于目标模型鲁棒性递进的课程强化学习方法。
Paper analysisRead analysis
AttacksarXiv 2608.05108Aug 6
PIMiner：无需再训练即可跨模型迁移的提示注入红队智能体系统
提出 PIMiner，用可复用策略库自动搜索并迁移针对智能体的提示注入
arXiv
2608.05108
Published
Aug 6
Layer
Application layer
Setting
AI Agent
Evaluated models
Gemini-2.5-Pro, DeepSeek-V4-Pro, GPT-4o-mini and others10
- AttacksPrompt injection
- TechniqueAutomated search
Paper analysisRead analysis
AttacksarXiv 2608.04741Aug 5
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
arXiv
2608.04741
Published
Aug 5
Layer
Application layer
Setting
web agent
Evaluated models
GPT-4o, Gemini 3 Flash, Claude Sonnet 4 and others7
- AttacksPrompt injection
- TechniqueAutomated search
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。
Paper analysisRead analysis
AttacksarXiv 2608.04565Aug 5
Researchers propose Breadcrumbing long-range attack that can hijack the evidence chain of DeepResearch-style search agents
提出权威链劫持与轨迹引导策略演化，劫持搜索 Agent 的证据链
arXiv
2608.04565
Published
Aug 5
Layer
Application layer
Setting
web agent
Evaluated models
Qwen3.5-9B, Gemma4-31B, DeepResearch and others7
- AttacksPrompt injection
- TechniqueAutomated search
- ComponentRAG
Abstract论文揭示了多步搜索智能体在受限中间人操纵下的脆弱性，提出 ACH 策略与 TGSE 演化方法并完成系统验证。
Paper analysisRead analysis
AttacksarXiv 2606.14517Jun 12
Researchers disclose a reasoning-amplified denial-of-service attack against LLM agent guardrails
提出针对 Agent 护栏的推理扩展拒绝服务攻击
arXiv
2606.14517
Published
Jun 12
Layer
Application layer
Setting
AI Agent
Attacker
Black-box
Evaluated models
TS-Guard-8B, Qwen3.5-9B, DeepSeek-V3.2 and others11
- AttacksDenial of service
- TechniqueReasoning-chain manipulation
- ComponentGuardrails and judges
Paper analysisRead analysis
AttacksarXiv 2605.15338May 15
Hidden in Memory: Latent Memory Poisoning of LLM Agents
提出潜伏记忆投毒，诱导 Agent 把外部文档写成持久伪造记忆
arXiv
2605.15338
Published
May 15
Layer
Application layer
Setting
AI Agent
Evaluated models
GPT-5.4, GPT-5.5, Claude Sonnet 4.6 and others12
- AttacksPoisoning and backdoors
- TechniqueDormant triggers
- ComponentMemory
Abstract持久记忆成为跨会话攻击面：通用载荷可写入伪造记忆，并在相关后续任务中影响行为。
sleeper memory poisoning 研究带持久记忆的 LLM 助手：外部文档能否让助手存下一则伪造的用户记忆，并在原文档消失后的新会话里被取回、用来改变回复或动作。
Paper analysisRead analysis
AttacksarXiv 2603.16960Mar 17
Adversarial attacks against modern vision-language models
Conduct adversarial attacks on product images for open-source visual shopping agents and induce wrong purchases
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
AbstractThe authors state that LLaVA is easily induced by three attacks to buy the wrong item, while Qwen's correct purchase rate remains close to its clean condition.
The authors compare the robustness of two open-source VLM shopping agents against image adversarial perturbations in a self-built e-commerce environment.
Paper analysisRead analysis
AttacksarXiv 2602.05484Feb 5
Masked Mirror: Covert Prompt Injection Attacks Against LLM-Based Phishing Detection
提出隐蔽提示注入攻击 LLM 钓鱼检测器，并给出防御 InjectDefuser
arXiv
2602.05484
Published
Feb 5
Layer
Application layer
Setting
LLM applications
Evaluated models
OpenAI GPT-5, xAI Grok 4 Fast (Non-Reasoning), Meta Llama 4 Maverick and others4
- DefensesInstruction–data separation
- AttacksPrompt injection
- TechniqueEncoding and obfuscation
- ComponentGuardrails and judges
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2601.10923Jan 16
Hidden-in-Plain-Text: A benchmark for indirect prompt injection in social network RAG
用 OpenRAG-Soc 评测社交网页 RAG 的间接提示注入与检索投毒
arXiv
2601.10923
Published
Jan 16
Layer
Application layer
Setting
LLM applications
Evaluated models
Llama-3-8B-Instruct, Mistral-7B-Instruct, Qwen2.5-14B and others8
- AttacksPrompt injection
- TechniqueEncoding and obfuscation
- ComponentRAG
Paper analysisRead analysis
AttacksarXiv 2505.11717May 17, 2025
WebInject：通过网页像素扰动对多模态网页 Agent 实施提示注入
提出 WebInject，用网页像素扰动诱导网页 Agent 执行指定动作
arXiv
2505.11717
Published
May 17, 2025
Layer
Application layer
Setting
web agent
Evaluated models
UI-TARS-7B-SFT, Phi-4-multimodal-instruct, Llama-3.2-11B-Vision-Instruct and others5
- AttacksPrompt injection
- TechniqueGradient and representation optimization
- ComponentVision
AbstractWebInject 经源码扰动原始像素。
WebInject 是对 MLLM 网页智能体的环境提示注入：修改网页源码，使原始像素上的扰动经过显示器映射后，诱导智能体执行攻击者指定动作。
Paper analysisRead analysis
AttacksarXiv 2406.12814Jun 19, 2024
论文提出 ARE 框架，在 VisualWebArena 上评估多模态 Agent 的对抗鲁棒性
提出 ARE 框架与图像文本攻击，评测多模态网页 Agent 的对抗鲁棒性
arXiv
2406.12814
Published
Jun 19, 2024
Layer
Application layer
Setting
web agent
Evaluated models
GPT-4o, GPT-4V, Claude-3-Opus and others4
- AttacksPrompt injection
- TechniqueGradient and representation optimization
- ComponentVision
Abstract系统评估多模态网页智能体的脆弱性，指出推理时计算组件被攻破会加剧系统风险。
Paper analysisRead analysis

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