Survey of Adversarial Robustness in Multimodal Large Language Models
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910880472498176 |
|---|---|
| author | Jiang, Chengze Wang, Zhuangzhuang Dong, Minjing Gui, Jie |
| author_facet | Jiang, Chengze Wang, Zhuangzhuang Dong, Minjing Gui, Jie |
| contents | Multimodal Large Language Models (MLLMs) have demonstrated exceptional performance in artificial intelligence by facilitating integrated understanding across diverse modalities, including text, images, video, audio, and speech. However, their deployment in real-world applications raises significant concerns about adversarial vulnerabilities that could compromise their safety and reliability. Unlike unimodal models, MLLMs face unique challenges due to the interdependencies among modalities, making them susceptible to modality-specific threats and cross-modal adversarial manipulations. This paper reviews the adversarial robustness of MLLMs, covering different modalities. We begin with an overview of MLLMs and a taxonomy of adversarial attacks tailored to each modality. Next, we review key datasets and evaluation metrics used to assess the robustness of MLLMs. After that, we provide an in-depth review of attacks targeting MLLMs across different modalities. Our survey also identifies critical challenges and suggests promising future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_13962 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Survey of Adversarial Robustness in Multimodal Large Language Models Jiang, Chengze Wang, Zhuangzhuang Dong, Minjing Gui, Jie Computer Vision and Pattern Recognition Multimodal Large Language Models (MLLMs) have demonstrated exceptional performance in artificial intelligence by facilitating integrated understanding across diverse modalities, including text, images, video, audio, and speech. However, their deployment in real-world applications raises significant concerns about adversarial vulnerabilities that could compromise their safety and reliability. Unlike unimodal models, MLLMs face unique challenges due to the interdependencies among modalities, making them susceptible to modality-specific threats and cross-modal adversarial manipulations. This paper reviews the adversarial robustness of MLLMs, covering different modalities. We begin with an overview of MLLMs and a taxonomy of adversarial attacks tailored to each modality. Next, we review key datasets and evaluation metrics used to assess the robustness of MLLMs. After that, we provide an in-depth review of attacks targeting MLLMs across different modalities. Our survey also identifies critical challenges and suggests promising future research directions. |
| title | Survey of Adversarial Robustness in Multimodal Large Language Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.13962 |