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Attracted by the impressive power of Multimodal Large Language Models (MLLMs), the public is increasingly utilizing them to improve the efficiency of daily work. Nonetheless, the vulnerabilities of MLLMs to unsafe instructions bring huge…

Computer Vision and Pattern Recognition · Computer Science 2024-06-21 Xin Liu , Yichen Zhu , Yunshi Lan , Chao Yang , Yu Qiao

Multi-Modal Language Models (MLLMs) have transformed artificial intelligence by combining visual and text data, making applications like image captioning, visual question answering, and multi-modal content creation possible. This ability to…

Cryptography and Security · Computer Science 2024-11-11 Pete Janowczyk , Linda Laurier , Ave Giulietta , Arlo Octavia , Meade Cleti

Large Language Models (LLMs) have evolved into Multimodal Large Language Models (MLLMs), significantly enhancing their capabilities by integrating visual information and other types, thus aligning more closely with the nature of human…

Cryptography and Security · Computer Science 2025-06-03 Youze Wang , Wenbo Hu , Yinpeng Dong , Jing Liu , Hanwang Zhang , Richang Hong

The deployment of multimodal large language models (MLLMs) has brought forth a unique vulnerability: susceptibility to malicious attacks through visual inputs. This paper investigates the novel challenge of defending MLLMs against such…

Cryptography and Security · Computer Science 2024-06-18 Renjie Pi , Tianyang Han , Jianshu Zhang , Yueqi Xie , Rui Pan , Qing Lian , Hanze Dong , Jipeng Zhang , Tong Zhang

The security concerns surrounding Large Language Models (LLMs) have been extensively explored, yet the safety of Multimodal Large Language Models (MLLMs) remains understudied. In this paper, we observe that Multimodal Large Language Models…

Computer Vision and Pattern Recognition · Computer Science 2024-06-21 Xin Liu , Yichen Zhu , Jindong Gu , Yunshi Lan , Chao Yang , Yu Qiao

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,…

Computer Vision and Pattern Recognition · Computer Science 2025-03-19 Chengze Jiang , Zhuangzhuang Wang , Minjing Dong , Jie Gui

Multimodal large language models (MLLMs) integrate information from multiple modalities such as text, images, audio, and video, enabling complex capabilities such as visual question answering and audio translation. While powerful, this…

Cryptography and Security · Computer Science 2026-03-31 Bhavuk Jain , Sercan Ö. Arık , Hardeo K. Thakur

Multimodal large language models (MLLMs), which bridge the gap between audio-visual and natural language processing, achieve state-of-the-art performance on several audio-visual tasks. Despite the superior performance of MLLMs, the scarcity…

Cryptography and Security · Computer Science 2025-06-16 Jinming Wen , Xinyi Wu , Shuai Zhao , Yanhao Jia , Yuwen Li

Multimodal Large Language Models (MLLMs) have enabled transformative advancements across diverse applications but remain susceptible to safety threats, especially jailbreak attacks that induce harmful outputs. To systematically evaluate and…

The integration of Large Language Models (LLMs) and Multi-modal Large Language Models (MLLMs) into mobile GUI agents has significantly enhanced user efficiency and experience. However, this advancement also introduces potential security…

Cryptography and Security · Computer Science 2025-03-18 Yulong Yang , Xinshan Yang , Shuaidong Li , Chenhao Lin , Zhengyu Zhao , Chao Shen , Tianwei Zhang

Recently, multimodal large language models (MLLMs) have emerged as a unified paradigm for language and image generation. Compared with diffusion models, MLLMs possess a much stronger capability for semantic understanding, enabling them to…

Computer Vision and Pattern Recognition · Computer Science 2026-03-26 Ye Leng , Junjie Chu , Mingjie Li , Chenhao Lin , Chao Shen , Michael Backes , Yun Shen , Yang Zhang

Spurred by the recent rapid increase in the development and distribution of large language models (LLMs) across industry and academia, much recent work has drawn attention to safety- and security-related threats and vulnerabilities of LLMs,…

Computation and Language · Computer Science 2023-08-25 Maximilian Mozes , Xuanli He , Bennett Kleinberg , Lewis D. Griffin

Large language models (LLMs) excel in diverse applications but face dual challenges: generating harmful content under jailbreak attacks and over-refusal of benign queries due to rigid safety mechanisms. These issues are further complicated…

Artificial Intelligence · Computer Science 2025-11-04 Yifan Xia , Guorui Chen , Wenqian Yu , Zhijiang Li , Philip Torr , Jindong Gu

The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural language understanding and generation. However, the increasing…

Artificial Intelligence · Computer Science 2024-12-25 Dan Shi , Tianhao Shen , Yufei Huang , Zhigen Li , Yongqi Leng , Renren Jin , Chuang Liu , Xinwei Wu , Zishan Guo , Linhao Yu , Ling Shi , Bojian Jiang , Deyi Xiong

Large Language Models (LLMs), now a foundation in advancing natural language processing, power applications such as text generation, machine translation, and conversational systems. Despite their transformative potential, these models…

Cryptography and Security · Computer Science 2025-08-05 Kang Chen , Xiuze Zhou , Yuanguo Lin , Jinhe Su , Yuanhui Yu , Li Shen , Fan Lin

Multi-modal large language models (MLLMs) have emerged as powerful tools for analyzing Internet-scale image data, offering significant benefits but also raising critical safety and societal concerns. In particular, open-weight MLLMs may be…

Computer Vision and Pattern Recognition · Computer Science 2026-04-13 Zedian Shao , Hongbin Liu , Yuepeng Hu , Neil Zhenqiang Gong

With the significant development of large models in recent years, Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a wide range of multimodal understanding and reasoning tasks. Compared to traditional…

Computer Vision and Pattern Recognition · Computer Science 2024-07-15 Daizong Liu , Mingyu Yang , Xiaoye Qu , Pan Zhou , Yu Cheng , Wei Hu

Large Language Models (LLMs) are swiftly advancing in architecture and capability, and as they integrate more deeply into complex systems, the urgency to scrutinize their security properties grows. This paper surveys research in the…

Computation and Language · Computer Science 2023-10-18 Erfan Shayegani , Md Abdullah Al Mamun , Yu Fu , Pedram Zaree , Yue Dong , Nael Abu-Ghazaleh

Large Language Models (LLMs) have revolutionized artificial intelligence and machine learning through their advanced text processing and generating capabilities. However, their widespread deployment has raised significant safety and…

Cryptography and Security · Computer Science 2024-12-03 Jing Cui , Yishi Xu , Zhewei Huang , Shuchang Zhou , Jianbin Jiao , Junge Zhang

This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact on AI and Generative Models. Starting with foundational…

Artificial Intelligence · Computer Science 2025-12-02 Chia Xin Liang , Pu Tian , Caitlyn Heqi Yin , Yao Yua , Wei An-Hou , Li Ming , Xinyuan Song , Tianyang Wang , Ziqian Bi , Ming Liu
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