AttacksarXiv 2610.09708
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
- Layer
- Prompt layer
- Evaluated models
- OpenVLA, UniVLA, π0.5
- TechniqueGradient and representation optimization
- ComponentVision
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- AttacksarXiv 2610.09240
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
- 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。
- AttacksarXiv 2610.09027
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
- 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。
- AttacksarXiv 2610.08331
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
- 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
- AttacksarXiv 2610.06814
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
- Layer
- Prompt layer
- Evaluated models
- FastWAM, DreamWAM-uncond, DreamWAM-joint and others4
- TechniqueGradient and representation optimization
- ComponentVision
- AttacksarXiv 2610.06083
研究者提出面向深度强化学习的轨迹级可迁移黑盒对抗攻击
提出轨迹级可迁移对抗攻击，降低深度强化学习策略的期望回报
- arXiv
- 2610.06083
- Published
- Layer
- Model layer
- Evaluated models
- DQN, Double DQN (DDQN)
Abstract轨迹级后退视界攻击在 CartPole 上比逐步 FGSM 类和 Sign 噪声更压低回报。
这项工作研究 DRL 上的迁移式黑盒观测攻击：受害参数不可访问时，在白盒代理上造扰动再迁移。
- AttacksarXiv 2610.06977
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
- 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
- AttacksarXiv 2610.03166
LiBRA：通过双向隐空间优化实现检测感知的图像水印去除
提出 LiBRA，在潜空间双向优化以规避图像水印双侧检测
- arXiv
- 2610.03166
- Published
- Layer
- Model layer
- Setting
- Models and APIs
- Attacker
- White-box
- Evaluated models
- Stable Signature, YU1, HiDDeN and others4
- AttacksDetection evasion
- TechniqueDefense-targeted
AbstractLiBRA 用双向潜空间优化规避双侧水印检测，并提高相对反转基线的保真。
- AttacksarXiv 2610.01871
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
- 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
- AttacksarXiv 2610.01367
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
- 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。
- AttacksarXiv 2609.39788
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
- 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。
- AttacksarXiv 2609.38899
SceneJail: Jailbreaking multimodal large models using video scene context
提出 SceneJail，利用视频情境上下文越狱视频多模态模型
- arXiv
- 2609.38899
- Published
- Layer
- Prompt layer
- Setting
- Models and APIs
- Attacker
- Black-box
- Evaluated models
- Qwen2.5-VL-7B-Instruct, Qwen2.5-VL-32B-Instruct, Qwen3-VL-8B-Instruct and others8
- AttacksJailbreaks
- TechniqueCross-modal payloads
- ComponentVideo
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
- 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。
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
- 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。
- AttacksarXiv 2609.07216
DIVA：利用视觉锚点对视频生成模型实施多模态越狱
提出 DIVA，将有害意图拆成视觉锚点与运动文本以越狱视频生成模型
- arXiv
- 2609.07216
- Published
- Layer
- Prompt layer
- Setting
- Models and APIs
- Attacker
- Black-box
- Evaluated models
- Wan, CogVideoX, LTX-Video and others9
- AttacksJailbreaks
- TechniqueCross-modal payloads
- ComponentImage generation
AbstractDIVA 用视觉锚点抑制语义漂移做视频越狱，并给出多条件安全基准 TI2VSafetyBench。
DIVA 是针对带参考图的视频生成模型的免训练 black-box 越狱：用一致性机制本身堵住语义漂移这条安全逃逸路径。。
- AttacksarXiv 2609.04533
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
- 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。
- AttacksarXiv 2609.05525
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
- Layer
- Prompt layer
- Setting
- Models and APIs
- Attacker
- White-box
- Evaluated models
- LLaDA-V, MMaDA, LLaDA2.0-Uni
- AttacksJailbreaks
- TechniqueGradient and representation optimization
- ComponentVision
- AttacksarXiv 2608.27531
MAMJ：面向视觉语言模型的元自适应多模态越狱攻击
提出 MAMJ，在元层面交替优化多模态越狱的策略提示与攻击者权重
- arXiv
- 2608.27531
- Published
- Layer
- Prompt layer
- Setting
- Models and APIs
- Attacker
- Black-box
- Evaluated models
- GPT-4o, Gemini-3-Pro-Preview, Seed 2.0
- AttacksJailbreaks
- TechniqueAutomated search
- ComponentVision
- AttacksarXiv 2608.15578
ARENA：面向大型音频语言模型的自动化红队框架
提出 ARENA 音频红队框架，自动构造文本安全且音频接地的越狱输入
- arXiv
- 2608.15578
- Published
- Layer
- Prompt layer
- Setting
- Models and APIs
- Attacker
- Black-box
- Evaluated models
- Audio Flamingo 3, Qwen2-Audio-7B-Instruct, MiMo-Audio-7B-Instruct and others4
- AttacksJailbreaks
- TechniqueAutomated search
- ComponentAudio
- AttacksarXiv 2608.10393
DURA：用扩散模型生成自然对抗补丁攻击 VLA 机器人模型
提出 DURA，用扩散模型生成自然对抗补丁操控 VLA 机器人动作
- arXiv
- 2608.10393
- Published
- Layer
- Prompt layer
- Evaluated models
- OpenVLA-7B, π0-FAST, openvla-7B
- TechniqueCross-modal payloads
- ComponentVision
AbstractDURA 用扩散潜空间轨迹生成自然补丁，白盒与黑盒都能把 VLA 导向指定动作。
DURA 是针对 VLA 的扩散式、补丁内容不受限的机器人攻击，同时支持白盒梯度和只看动作输出的黑盒。