The recent advancements in the field of large language models (LLMs) have primarily focused on enhancing their robustness against adversarial attacks, particularly in the context of jailbreaking and privacy leakage. Researchers are exploring innovative methods to adapt LLMs for specific tasks, such as time series forecasting, while ensuring they remain secure against malicious prompts and model manipulations. Key developments include the use of nearest neighbor contrastive learning for time series forecasting, novel jailbreak techniques leveraging alignment and bit-flip manipulations, and advanced red-teaming frameworks for privacy leakage. These approaches aim to balance the performance and security of LLMs, addressing critical vulnerabilities while maintaining or improving their functionality. Notably, some papers have introduced groundbreaking methods that significantly reduce computational requirements and increase attack success rates, underscoring the evolving landscape of LLM security. As the field progresses, the emphasis on developing robust defense mechanisms and moving target defense strategies is becoming increasingly important to safeguard LLMs against a wide array of threats.