The recent advancements in the field of natural language processing (NLP) have shown a significant shift towards enhancing the robustness and security of large language models (LLMs). A notable trend is the development of innovative frameworks and mechanisms to counter adversarial attacks, which pose a serious threat to the reliability of these models. For instance, the introduction of human-in-the-loop systems for generating high-quality adversarial texts, particularly for less-resourced languages, marks a step forward in creating robust benchmarks for model evaluation. Additionally, the use of defensive suffix generation algorithms to mitigate adversarial influences while maintaining model utility highlights a practical approach to enhancing LLM security. Another area of focus is the protection of system prompts within LLMs, with new defense mechanisms ensuring the privacy of sensitive information. Furthermore, the field is witnessing a statistical and multi-perspective revisiting of membership inference attacks, shedding light on the inconsistencies and dynamics of these attacks across various settings. These developments collectively underscore the importance of continuous innovation in safeguarding LLMs against emerging threats and ensuring their ethical and secure deployment in real-world applications.