EMNLP 2024main3 citations
From LLMs to MLLMs: Exploring the Landscape of Multimodal Jailbreaking
Siyuan Wang, Zhuohan Long, Zhihao Fan, Zhongyu Wei
Abstract
The rapid development of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) has exposed vulnerabilities to various adversarial attacks. This paper provides a comprehensive overview of jailbreaking research targeting both LLMs and MLLMs, highlighting recent advancements in evaluation benchmarks, attack techniques and defense strategies. Compared to the more advanced state of unimodal jailbreaking, multimodal domain remains underexplored. We summarize the limitations and potential research directions of multimodal jailbreaking, aiming to inspire future research and further enhance the robustness and security of MLLMs.
BibTeX
@inproceedings{wang-etal-2024-llms-mllms,
title = "From {LLM}s to {MLLM}s: Exploring the Landscape of Multimodal Jailbreaking",
author = "Wang, Siyuan and
Long, Zhuohan and
Fan, Zhihao and
Wei, Zhongyu",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.973/",
doi = "10.18653/v1/2024.emnlp-main.973",
pages = "17568--17582"
}