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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 2610.06814Oct 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
Analysis and theoryarXiv 2610.07009Oct 4
研究受控拆解图像到文本越狱中图像属性的作用
固定有害指令，单轴归因图像到文本越狱的图像属性
arXiv
2610.07009
Published
Oct 4
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
GPT-4o, Claude Sonnet 4.5, Qwen3-VL-8B-Instruct and others6
- AttacksJailbreaks
- ComponentVision
Abstract固定文本的图像越狱拆解，只在 Qwen 上给出一条有界相关性归因。
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
Evaluation and benchmarksarXiv 2609.37863Sep 29
MIST 压力测试：无关图像会扰动 VLM 评判者标签
提出 MIST 压力测试，检验无关图像是否扰动 VLM 评判者标签
arXiv
2609.37863
Published
Sep 29
Layer
Prompt layer
Setting
LLM applications
Evaluated models
GPT-5.2, Gemini 3.1 Flash-Lite, Gemini 3.5 Flash and others13
- ComponentVision
Abstract有图就改标签，图的内容却不告知 judge，裁决绑定配置。
作者用MIST问：VLM 代替人类标注时，可替代性裁决会不会被指南要求忽略的图像改写。
Paper analysisRead analysis
DefensesarXiv 2609.36562Sep 29
ThinkingGuard：通过逐步风险归因解码多模态大语言模型中的隐式危害
构建 TriggerBench 并训练 ThinkingGuard，检测图文隐式风险
arXiv
2609.36562
Published
Sep 29
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
ThinkingGuard（Qwen3-VL-8B-Instruct）
- DefensesDetection and filtering
- AttacksJailbreaks
- ComponentGuardrails and judges
AbstractTriggerBench 隔离触发条件，ThinkingGuard 用分步归因在隐式风险检测上取得作者报告的较高平均分。
这篇工作研究多模态隐式风险检测：图文各自良性，风险只在关键元素与触发元素交互时出现。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.29349Sep 24
ArGuard 共享任务：阿拉伯语模因与 LLM 提示词中的有害内容检测
组织 ArGuard 共享任务评测阿拉伯语表情包与提示词有害检测
arXiv
2609.29349
Published
Sep 24
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Qwen3-VL, Qwen3-VL-8B-Instruct, MARBERTv2 and others9
- DefensesDetection and filtering
- ComponentGuardrails and judges
Paper analysisRead analysis
DefensesarXiv 2609.28239Sep 23
ODPure：通过集成破坏共识实现目标检测后门净化
提出 ODPure，在输入阶段用腐蚀重建与共识净化目标检测后门
arXiv
2609.28239
Published
Sep 23
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
YOLOv5, Faster R-CNN
- AttacksPoisoning and backdoors
- ComponentVision
Paper analysisRead analysis
DefensesarXiv 2609.21363Sep 18
用扩散模型隐空间扰动防御视觉语言模型的地理位置隐私泄露
提出扩散潜空间地理扰动防御，抑制视觉语言模型泄露精确地理位置
arXiv
2609.21363
Published
Sep 18
Layer
Prompt layer
Setting
Models and APIs
Attacker
Black-box
Evaluated models
GPT-4.1, GPT-5, Gemini 2.5 Pro and others5
- AttacksMalicious use
- ComponentVision
Paper analysisRead analysis
DefensesarXiv 2609.10613Sep 8
后验重加权框架解释并缓解多模态大模型上下文越狱
用后验重加权解释多模态上下文越狱，并提出风险门控的良性示例注入防御
arXiv
2609.10613
Published
Sep 8
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Qwen3-VL-2B, Qwen3-VL-4B, Qwen3-VL-8B and others8
- DefensesInstruction–data separation
- AttacksJailbreaks
- ComponentVision
Abstract后验重加权解释上下文越狱的缩放，并支持风险门控的良性示例防御。
Zhang 等把多模态 ICL 越狱写成对齐模型在安全与有害隐模式之间的推理时后验漂移，并据此做风险门控防御。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.05843Sep 5
SinoGlyphBench：中文字形混淆下语言模型审核的诊断基准
提出 SinoGlyphBench，用配对字形混淆诊断中文审核稳定性
arXiv
2609.05843
Published
Sep 5
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
GPT-5.4 mini, GPT-5.5, DeepSeek V4 Flash and others12
- AttacksDetection evasion
- TechniqueEncoding and obfuscation
- ComponentGuardrails and judges
AbstractSinoGlyphBench 显示中文字形混淆会同时增加有害漏报和误报，全文扰动最重。
SinoGlyphBench是一个配对诊断基准，用来看内容审核在可还原的中文字形混淆下是否还保持原决定。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2609.04859Sep 4
MM-IFEval-Pro：面向视觉语言模型的多语言抗攻击指令遵循基准
提出抗攻击多语言视觉指令遵循基准 MM-IFEval-Pro
arXiv
2609.04859
Published
Sep 4
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Qwen3-VL-8B-Instruct, InternVL3.5-8B, MiniCPM-V4.5 and others5
- AttacksPrompt injection
- ComponentVision
AbstractMM-IFEval-Pro 以规则验证评测中英指令遵循与视觉劫持，GRPO 提高两基座平均分。
MM-IFEval-Pro 是面向 VLM 的中英多模态指令遵循基准，并配有可用规则计算奖励的 GRPO 训练集。
Paper analysisRead analysis
DefensesarXiv 2609.03139Sep 3
TRIM：面向多种后门触发器的黑盒检测与净化方法
提出 TRIM，在黑盒推理时定位并净化图像后门区域
arXiv
2609.03139
Published
Sep 3
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
ResNet-50
- DefensesInference-time intervention
- AttacksPoisoning and backdoors
- ComponentVision
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 2608.27531Aug 27
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
Aug 27
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
Paper analysisRead analysis
AttacksarXiv 2608.10393Aug 11
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
Aug 11
Layer
Prompt layer
Setting
Embodied systems and robotics
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。
Paper analysisRead analysis
OtherarXiv 2608.04314Aug 5
面向良性用途的对抗攻击：视觉内容全生命周期主动防护综述
综述视觉内容全生命周期中的所有者侧对抗防护方法
arXiv
2608.04314
Published
Aug 5
Layer
Prompt layer
Setting
Models and APIs
- ComponentVision
Abstract综述把五类对抗式保护收成一个生命周期，并用三条轴比较其主张。
Adversarial attacks for good 是一篇视觉内容所有者侧保护的综述：把长期作为攻击来研究的扰动和结构化信号，反过来交给数据所有者、创作者、平台或审计方使用。
Paper analysisRead analysis
AttacksarXiv 2608.04034Aug 3
HACA：原子越狱策略解耦组合的多模态自动红队方法
提出 HACA，将图文越狱拆为原子策略并自动组合以诱导有害输出
arXiv
2608.04034
Published
Aug 3
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
LLaVA-1.6-Mistral-7B, InternVL-3.5-8B, VLGuard (LLaVA-v1.5-7B-Mixed) and others5
- AttacksJailbreaks
- TechniqueCross-modal payloads
- ComponentVision
Paper analysisRead analysis
AttacksarXiv 2607.02961Jul 3
研究者提出信息过载方法越狱大型视觉语言模型
提出 INFER+ 信息过载方法，越狱大型视觉语言模型
arXiv
2607.02961
Published
Jul 3
Layer
Prompt layer
Setting
Models and APIs
Attacker
Black-box, Gray-box
Evaluated models
Qwen3-VL-8B, Qwen2-VL-7B, InternVL3.5-8B and others8
- AttacksJailbreaks
- TechniqueCross-modal payloads
- ComponentVision
Abstract用图文过载越狱 LVLM。
这篇工作用信息过载越狱 LVLM，而不是靠 OOD 变换隐藏意图。
Paper analysisRead analysis
Evaluation and benchmarksarXiv 2606.07706Jun 5
MLingualFC: Evaluating Jailbreak Vulnerabilities in Multilingual Vision-Language Models
Propose MLingualFC, evaluating vision-language model jailbreak vulnerabilities with multilingual flowchart images
arXiv
2606.07706
Published
Jun 5
Layer
Prompt layer
Setting
Models and APIs
Evaluated models
Qwen2.5-VL-3B-Instruct, Gemma-4-E4B-it, Pangea-7B
- AttacksJailbreaks
- TechniqueCross-modal payloads
- ComponentVision
Paper analysisRead analysis

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