The recent developments in the research area of large language models (LLMs) and their applications have shown significant advancements in both defensive and offensive strategies. The field is moving towards more nuanced and robust methods for handling harmful content, privacy preservation, and safety alignment. Innovations in clustering and encoding techniques are enabling more versatile data handling, while new objectives like AdvPrefix are enhancing jailbreak attack capabilities with greater control and optimization. Defensive strategies, such as GuidelineLLM and IRR, are focusing on post-hoc safety realignment and risk identification without the need for additional fine-tuning, thereby improving general applicability and reducing attack success rates. Additionally, the integration of in-context learning with adversative structures is proving effective against prefilling attacks, highlighting the importance of context in defense mechanisms. Privacy concerns in summarization tasks are being addressed through comprehensive studies and fine-tuning strategies, while text summarization is emerging as a powerful tool for mitigating adversarial text-to-image prompts. The field is also witnessing the development of training-free frameworks like NLSR for neuron-level safety realignment, which offer significant safety enhancements without compromising task-level accuracy. Overall, the research is advancing towards more sophisticated, context-aware, and efficient solutions for both enhancing model capabilities and safeguarding against misuse.