The field of large language models (LLMs) is rapidly evolving, with a focus on improving security and interpretability. Recent developments have highlighted the susceptibility of LLMs to prompt injection attacks, which can manipulate model behavior and override intended instructions. In response, researchers are exploring new defense strategies and evaluation frameworks to assess their effectiveness. Another area of focus is on understanding how LLMs process and generate language, with techniques such as contrastive activation engineering and sparse autoencoders being used to analyze and control model behavior. Additionally, there is a growing interest in applying LLMs to specific domains, such as geography, and developing methods to interpret and understand their internal representations. Noteworthy papers in this area include: OET, which introduces an optimization-based evaluation toolkit for assessing prompt injection attacks and defenses. Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders, which discovers language-specific features in LLMs and demonstrates their potential for controlling language generation.