Face morphing attacks threaten biometric verification, yet most morphing attack detection (MAD) systems require task-specific training and generalize poorly to unseen attack types. Meanwhile, open-source multimodal large language models (MLLMs) have demonstrated strong visual-linguistic reasoning, but their potential in biometric forensics remains underexplored. In this paper, we present the first systematic zero-shot evaluation of open-source MLLMs for single-image MAD, using publicly available weights and a standardized, reproducible protocol. Across diverse morphing techniques, many MLLMs show non-trivial discriminative ability without any fine-tuning or domain adaptation, and LLaVA1.6-Mistral-7B achieves state-of-the-art performance, surpassing highly competitive task-specific MAD baselines by at least 23% in terms of equal error rate (EER). The results indicate that multimodal pretraining can implicitly encode fine-grained facial inconsistencies indicative of morphing artifacts, enabling zero-shot forensic sensitivity. Our findings position open-source MLLMs as reproducible, interpretable, and competitive foundations for biometric security and forensic image analysis. This emergent capability also highlights new opportunities to develop state-of-the-art MAD systems through targeted fine-tuning or lightweight adaptation, further improving accuracy and efficiency while preserving interpretability. To support future research, all code and evaluation protocols will be released upon publication.
ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Bytedance Inc.（字节跳动公司）
Curia: A Multi-Modal Foundation Model for Radiology
Raidium；Hôpital Européen Georges Pompidou, AP-HP（欧洲乔治·蓬皮杜医院, AP-HP）；Université Paris-Cité（巴黎城市大学）；INRIA（法国国家信息与自动化技术研究所）；INSERM（法国国家卫生与医学研究中心）；Beaujon Hospital（贝琼医院）；Medical University of South Carolina（南卡罗来纳医科大学）；Columbia University Irving Medical Center（哥伦比亚大学伊万斯医学中心）；Centre Cardiologique du Nord（北心脑血管中心）
Investigating Cross-Modal Skill Injection: Scenarios, Methods, and Hyperparameters
State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University（多媒体信息处理国家重点实验室，计算机科学学院，北京大学）；WeChat AI, Tencent Inc., China（腾讯公司，中国）；The University of Hong Kong（香港大学）
