With the rapid advancements in Multimodal Large Language Models (MLLMs), securing these models against malicious inputs while aligning them with human values has emerged as a critical challenge. In this paper, we investigate an important and unexplored question of whether techniques that successfully jailbreak Large Language Models (LLMs) can be equally effective in jailbreaking MLLMs. To explore this issue, we introduce JailBreakV-28K, a pioneering benchmark designed to assess the transferability of LLM jailbreak techniques to MLLMs, thereby evaluating the robustness of MLLMs against diverse jailbreak attacks. Utilizing a dataset of 2, 000 malicious queries that is also proposed in this paper, we generate 20, 000 text-based jailbreak prompts using advanced jailbreak attacks on LLMs, alongside 8, 000 image-based jailbreak inputs from recent MLLMs jailbreak attacks, our comprehensive dataset includes 28, 000 test cases across a spectrum of adversarial scenarios. Our evaluation of 10 open-source MLLMs reveals a notably high Attack Success Rate (ASR) for attacks transferred from LLMs, highlighting a critical vulnerability in MLLMs that stems from their text-processing capabilities. Our findings underscore the urgent need for future research to address alignment vulnerabilities in MLLMs from both textual and visual inputs.
The Art of the Jailbreak: Formulating Jailbreak Attacks for LLM Security Beyond Binary Scoring
Department of Computer Science, University of Texas at El Paso（德克萨斯大学埃尔帕索分校计算机科学系）；School of Computing, Southern Illinois University Carbondale（南方伊利诺伊大学卡罗尔丹分校计算学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Jailbreaking Attacks vs. Content Safety Filters: How Far Are We in the LLM Safety Arms Race?
CISPA Helmholtz Center for Information Security（CISPA 欧洲信息安全中心）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Southern University of Science and Technology（南方科技大学）
SafeMobile: Chain-level Jailbreak Detection and Automated Evaluation for Multimodal Mobile Agents
Nanyang Technological University（南洋理工大学）；Institute of Software, Chinese Academy of Sciences（中国科学院软件研究所）；Beihang University（北京航空航天大学）；Sun Yat-sen University（中山大学）；Beijing Institute of Technology（北京理工大学）；National University of Singapore（新加坡国立大学）
Conversational Complexity for Assessing Risk in Large Language Models
University of Cambridge（剑桥大学）；Spanish National Research Council（西班牙国家研究委员会）；Universitat Politècnica de València（瓦伦西亚理工大学）；Leverhulme Centre for the Future of Intelligence（勒沃休姆未来智能中心）；Center for Automation and Robotics（自动化与机器人中心）；Valencian Research Institute for Artificial Intelligence（瓦伦西亚人工智能研究所）
TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
Parameter Lab（参数实验室）；IT University of Copenhagen（哥本哈根信息技术大学）；Pioneer Centre for Artificial Intelligence（人工智能先锋中心）；NAVER AI Lab（NAVER人工智能实验室）；University of Tübingen（蒂宾根大学）
SciSafeEval: A Comprehensive Benchmark for Safety Alignment of Large Language Models in Scientific Tasks
Duke University（杜克大学）；Zhejiang University（浙江大学）；Australian National University（澳大利亚国立大学）；Institute of Software Chinese Academy of Sciences（中国科学院软件研究所）；Beijing University of Posts and Telecommunications（北京邮电大学）；University College London（伦敦大学学院）；University of Glasgow（格拉斯哥大学）；China Mobile Communications Group Co.,Ltd（中国移动通信集团有限公司）；Hong Kong Polytechnic University（香港理工大学）；North China University of Technology（北方工业大学）
Root Defence Strategies: Ensuring Safety of LLM at the Decoding Level
Aerospace Information Research Institute, Chinese Academy of Sciences（航天信息研究所，中国科学院）；Dept. of Comp. Sci. and Tech., Institute for AI, Tsinghua University（计算机科学与技术系，人工智能研究院，清华大学）；Shanghai Key Laboratory of Multi. Info. Processing, East China Normal University（上海多信息处理重点实验室，华东师范大学）；Kuaishou-Inc（快手公司）
East China Normal University（东华大学）；Shanghai Innovation Institute（上海创新研究院）；University of Southampton（南安普顿大学）；Shanghai Jiao Tong University（上海交通大学）；Software Engineering Institute, East China Normal University（东华大学软件工程研究院）
