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 2610.08331
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 2610.06977
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 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 2608.10393
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。
- AttacksarXiv 2606.12716
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
- 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。
- AttacksarXiv 2605.21541
研究者提出 FRA-Attack 频域正则化方法，提升对闭源 MLLM 的迁移攻击效果
提出 FRA-Attack，用频域对齐与梯度正则做可迁移定向视觉攻击
- arXiv
- 2605.21541
- Published
- Layer
- Prompt layer
- Setting
- Models and APIs
- Attacker
- Black-box
- Evaluated models
- GPT-5.2, GPT-5.4, Claude-Opus-4.6 and others15
- AttacksDetection evasion
- TechniqueGradient and representation optimization
- ComponentVision
AbstractFRA-Attack 以频域对齐和梯度低通做可迁移定向攻击，闭源面板上作者称整体更强，Gemma 除外。
FRA-Attack 是面向闭源 MLLM 的迁移式定向图像攻击。作者要解决的是：空间域局部对齐重复全局低频，代理梯度的高频成分又把更新拉向代理自身。
- AttacksarXiv 2605.20519
CodecAttack：在音频编解码器潜空间构造对抗扰动，绕过有损压缩防御
提出 CodecAttack，在编解码器隐空间构造可穿透有损压缩的对抗音频
- arXiv
- 2605.20519
- Published
- Layer
- Prompt layer
- Setting
- Models and APIs
- Attacker
- White-box
- Evaluated models
- Qwen2-Audio-7B-Instruct, Audio Flamingo 3, Qwen2.5-Omni
- AttacksJailbreaks
- TechniqueGradient and representation optimization
- ComponentAudio
- AttacksarXiv 2605.16090
研究者提出 CrossMPI：仅用图像扰动实现跨模态提示注入
提出 CrossMPI，仅用图像扰动实现跨模态提示注入
- arXiv
- 2605.16090
- Published
- Layer
- Prompt layer
- Setting
- Models and APIs
- Evaluated models
- MiniGPT4-llama2, MiniGPT4-vicuna, InstructBLIP and others6
- AttacksPrompt injection
- TechniqueCross-modal payloads
- ComponentVision
- AttacksarXiv 2605.10253
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
- 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
- AttacksarXiv 2605.07399
研究者提出 GPO-V，用全局概率优化越狱扩散视觉语言模型
提出 GPO-V，用全局概率优化的视觉扰动越狱扩散视觉语言模型
- arXiv
- 2605.07399
- Published
- Layer
- Prompt layer
- Setting
- Models and APIs
- Evaluated models
- LLaDA-V, LaViDA
- AttacksJailbreaks
- TechniqueGradient and representation optimization
- ComponentVision
Abstract作者用视觉扰动改 dVLM 的全局生成概率，以绕过两类拒绝。
GPO-V 把越狱从“开头必须顺从”改成“整段去噪概率朝肯定方向走”。。
- AttacksarXiv 2604.14604
The AudioHijack framework achieves covert audio prompt injection against 13 speech large models
提出 AudioHijack，用不可感知对抗音频对语音模型做上下文无关提示注入
- arXiv
- 2604.14604
- Published
- Layer
- Prompt layer
- Setting
- Models and APIs
- Evaluated models
- SpeechGPT, GLM-4-Voice, VITA-Audio and others15
- AttacksPrompt injection
- TechniqueGradient and representation optimization
- ComponentAudio
Abstract提出了 AudioHijack 框架，揭示了 LALM 对听觉提示注入的脆弱性与传统防御的局限。
- AttacksarXiv 2604.03117
Unveiling Physical-World Semantic Vulnerabilities: Universal Adversarial Patches for Infrared Vision-Language Models
Proposes the universal curved mesh patch UCGP, causing cross-task semantic bias in infrared vision-language models.
- arXiv
- 2604.03117
- Published
- Layer
- Prompt layer
- Setting
- Models and APIs
- Attacker
- Gray-box
- Evaluated models
- OpenAI CLIP ViT-L/14, OpenCLIP ViT-B/16, Meta-CLIP ViT-L/14 and others10
- AttacksDetection evasion
- TechniqueGradient and representation optimization
- ComponentVision
AbstractUCGP uses one shared curved-grid patch to simultaneously lower the classification and open-ended outputs of IR-VLM.
UCGP is a universal physical adversarial patch for infrared vision-language models. The question is: can a patch that can be attached and reused across samples cause stable semantic deviations in the infrared visual evidence shared by classification, description, and VQA. The patch is constrained to a low-frequency curved mesh.
- AttacksarXiv 2603.29418
研究者提出针对商用多模态大模型的隐蔽视觉提示注入方法
提出 CoTTA 隐蔽视觉提示注入，使多模态模型输出指定指令
- arXiv
- 2603.29418
- Published
- Layer
- Prompt layer
- Setting
- Models and APIs
- Evaluated models
- GPT-4o, GPT-5, Claude-4.5 and others4
- AttacksPrompt injection
- TechniqueCross-modal payloads
- ComponentVision
AbstractCoTTA 以隐蔽触发和双目标对齐诱导指定句子。
- AttacksarXiv 2603.28568
XSPA: Constructing Imperceptible X-Shaped Sparse Adversarial Perturbations for Transferable Attacks Against VLMs
Proposes XSPA, which uses X-shaped sparse adversarial perturbations to transfer attacks on VLMs.
- arXiv
- 2603.28568
- Published
- Layer
- Prompt layer
- Setting
- Models and APIs
- Evaluated models
- OpenCLIP ViT-B/16, Meta-CLIP ViT-L/14, EVA-CLIP ViT-G/14 and others10
- TechniqueGradient and representation optimization
- ComponentVision
- AttacksarXiv 2603.16960
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
- 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。
- AttacksarXiv 2603.13847
SWhisper 用近超声隐蔽声学通道对语音驱动 LLM 实现黑盒越狱
提出 SWhisper，用近超声波向语音驱动 LLM 隐蔽注入越狱提示
- arXiv
- 2603.13847
- Published
- Layer
- Application layer
- Setting
- LLM applications
- Attacker
- Black-box
- Evaluated models
- Meta-Llama-3.1-8B-Instruct, Gemma-3-4B, Qwen2.5-7B-Instruct and others9
- AttacksJailbreaks
- TechniqueCross-modal payloads
- ComponentAudio
- AttacksarXiv 2603.04846
面向多模态大语言模型的多范式协同对抗攻击
提出 MPCAttack，协同多范式特征优化可迁移视觉对抗扰动
- arXiv
- 2603.04846
- Published
- Layer
- Prompt layer
- Setting
- Models and APIs
- Evaluated models
- Qwen2.5-VL-7B, InternVL3-8B, LLaVa-1.5-7B and others8
- TechniqueGradient and representation optimization
- ComponentVision