The field of multimodal learning and adversarial robustness is rapidly evolving, with a focus on developing more effective defense mechanisms and attack methods. Researchers are exploring new approaches to improve the resilience of multimodal large language models and visual-language pre-training models to adversarial manipulations. Notable trends include the use of noise perturbation and clustering aggregation to enhance robustness, as well as the development of novel attack methods that balance exploration and exploitation. Additionally, there is a growing interest in understanding the vulnerabilities of contrastive language-image pre-training models and developing more effective adversarial attacks. Overall, the field is moving towards more sophisticated and nuanced approaches to adversarial robustness and multimodal learning. Noteworthy papers include: SmoothGuard, which introduces a lightweight defense framework for multimodal large language models, and ToxicTextCLIP, which proposes a framework for generating high-quality adversarial texts that target CLIP during pre-training. Enhancing Adversarial Transferability by Balancing Exploration and Exploitation with Gradient-Guided Sampling is also notable for its simple yet effective approach to improving adversarial transferability. MIFO proposes a method for precise learning and synthesizing multi-instance semantics from a single image. Beyond Deceptive Flatness: Dual-Order Solution for Strengthening Adversarial Transferability introduces a novel black-box gradient-based transferable attack. A Generative Adversarial Approach to Adversarial Attacks Guided by Contrastive Language-Image Pre-trained Model proposes a generative adversarial attack method that uses the CLIP model to create highly effective and visually imperceptible adversarial perturbations.