The field of large language models (LLMs) is rapidly advancing, with a growing focus on understanding and addressing their vulnerabilities. Recent research has highlighted the susceptibility of LLMs to various attacks, including jailbreaking, adversarial mislabeling, and multimodal jailbreaks. These attacks can have significant consequences, such as bypassing safety mechanisms, injecting poisoned data, and compromising model reliability. In response, researchers are developing innovative defense strategies, including hybrid approaches that integrate token- and prompt-level techniques, multi-agent systems, and prune-then-restore mechanisms. These defenses aim to enhance resistance to attacks, reduce false negatives, and improve model generalization. Notably, some papers have demonstrated the effectiveness of logit-gap steering, a fast jailbreak framework, and SafePTR, a training-free defense framework that selectively prunes harmful tokens. Additionally, researchers are exploring the use of LLMs in penetration testing, highlighting their potential for efficient and effective vulnerability assessment. Overall, the field is moving towards a deeper understanding of LLM vulnerabilities and the development of robust defenses to ensure safe and reliable deployment. Noteworthy papers include: Advancing Jailbreak Strategies, which proposes hybrid approaches to enhance jailbreak effectiveness, and SafePTR, which presents a comprehensive analysis of harmful multimodal tokens and a novel defense framework. Text Detoxification is also notable for its two-stage training framework that achieves state-of-the-art performance in detoxification while preserving semantics. Visual Contextual Attack is another significant contribution, demonstrating a novel setting for visual-centric jailbreak and achieving a high attack success rate.