Multimodal large language models (MLLMs) have become the cornerstone of today's generative AI ecosystem, sparking intense competition among tech giants and startups. In particular, an MLLM generates a text response given a prompt consisting of an image and a question. While state-of-the-art MLLMs use safety filters and alignment techniques to refuse unsafe prompts, in this work, we introduce MLLM-Refusal, the first method that induces refusals for safe prompts. In particular, our MLLM-Refusal optimizes a nearly-imperceptible refusal perturbation and adds it to an image, causing target MLLMs to likely refuse a safe prompt containing the perturbed image and a safe question. Specifically, we formulate MLLM-Refusal as a constrained optimization problem and propose an algorithm to solve it. Our method offers competitive advantages for MLLM model providers by potentially disrupting user experiences of competing MLLMs, since competing MLLM's users will receive unexpected refusals when they unwittingly use these perturbed images in their prompts. We evaluate MLLM-Refusal on four MLLMs across four datasets, demonstrating its effectiveness in causing competing MLLMs to refuse safe prompts while not affecting non-competing MLLMs. Furthermore, we explore three potential countermeasures-adding Gaussian noise, DiffPure, and adversarial training. Our results show that though they can mitigate MLLM-Refusal's effectiveness, they also sacrifice the accuracy and/or efficiency of the competing MLLM. The code is available at https://github.com/Sadcardation/MLLM-Refusal.
Safety Layers in Aligned Large Language Models: The Key to LLM Security
University of Science and Technology of China（中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）
Do We Really Need Curated Malicious Data for Safety Alignment in Multi-modal Large Language Models?
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Evolving Safety Landscape of Multi-modal Large Language Models: A Survey of Emerging Threats and Safeguards
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；NVIDIA（英伟达公司）；Penn State University（宾夕法尼亚州立大学）；Columbia University（哥伦比亚大学）；University of Missouri-Kansas City（密苏里大学堪萨斯分校）；Florida State University（佛罗里达州立大学）；Auburn University（奥本大学）
NEXUS: Learning Safety Constraints and Action Models for Safe and Robust Embodied Planning
School of Artificial Intelligence and Automation（人工智能与自动化学院）；Huazhong University of Science and Technology（华中科技大学）；School of Software（软件学院）；Henan University（河南大学）；Institute of Information Engineering（信息工程研究所）；Chinese Academy of Sciences（中国科学院）；School of Cyberspace Security（网络空间安全学院）；University of the Chinese Academy of Sciences（中国科学院大学）；Peking University（北京大学）
Immune: Improving Safety Against Jailbreaks in Multi-modal LLMs via Inference-Time Alignment
University of Maryland（马里兰大学）；Indian Institute of Technology Bombay（印度班加罗尔理工学院）；Princeton University（普林斯顿大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Capital One（Capital One公司）；University of Central Florida（佛罗里达中央大学）
Diagnosing and Repairing Unsafe Channels in Vision-Language Models via Causal Discovery and Dual-Modal Safety Subspace Projection
Beijing University of Posts and Telecommunications（北京邮电大学）；Huawei Technologies Ltd.（华为技术有限公司）；Peking University（北京大学）；Chongqing University of Posts and Telecommunications（重庆邮电大学）
Omni-Safety under Cross-Modality Conflict: Vulnerabilities, Dynamics Mechanisms and Efficient Alignment
Nanyang Technological University（南洋理工大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Tsinghua University（清华大学）；Fudan University（复旦大学）；University of Science and Technology of China（中国科学技术大学）；Renmin University of China（中国人民大学）
CrossGuard: Safeguarding MLLMs against Joint-Modal Implicit Malicious Attacks
Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Department of Computer Science & Engineering, Washington University in St. Louis（华盛顿大学圣路易斯分校计算机科学与工程系）
When Reasoning Is Not Enough: Proactive Inquiry for Cross-Modal Safety in Multimodal Large Language Models
Institute of Artificial Intelligence (TeleAI), China Telecom（中国电信人工智能研究院（TeleAI））；Peking University（北京大学）；School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University（西北工业大学人工智能与光电子学院（iOPEN））
Jailbreaking Vision-Language Models Through the Visual Modality
Warsaw University of Technology（华沙理工大学）；NASK National Research Institute（波兰国家研究研究院）；University of Liverpool（利物浦大学）；Indian Institute of Technology Roorkee（印度理工学院拉胡尔分校）；Toyota Technological Institute at Chicago（芝加哥丰田技术研究所）
Risk Awareness Injection: Calibrating Vision-Language Models for Safety without Compromising Utility
Shien-Ming Wu School of Intelligent Engineering, South China University of Technology（华南理工大学吴贤铭智能工程学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济实验室（深圳））；Computer Science and Engineering, The Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；University of Electronic Science and Technology of China（电子科技大学）
Strategic Deflection: Defending LLMs from Logit Manipulation
International Artificial Intelligence Center of Morocco（摩洛哥国际人工智能中心）；Mohammed VI Polytechnic University（摩洛哥穆莱·伊斯梅尔理工学院）；AgriEdge；Sorbonne University, LIP6 - UMR 7606 CNRS（索邦大学，LIP6 - UMR 7606 CNRS）
Red-teaming the Multimodal Reasoning: Jailbreaking Vision-Language Models via Cross-modal Entanglement Attacks
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；People’s Public Security University of China（中国人民公安大学）
