The field of large language models (LLMs) is moving towards a focus on security and robustness, with a particular emphasis on preventing prompt injection attacks. Recent research has highlighted the vulnerability of LLMs to such attacks, which can be used to manipulate the model's behavior and extract sensitive information. In response, researchers are developing new methods to defend against these attacks, including the use of system vectors, inference-time scaling, and type-directed privilege separation. These approaches aim to improve the security and reliability of LLMs, while also enhancing their performance and functionality. Notably, some papers have made significant contributions to this area, including the proposal of novel defense mechanisms and the development of benchmarking tools to evaluate the effectiveness of these defenses. For example, the paper on SecInfer presents a novel defense against prompt injection attacks using inference-time scaling, while the paper on WAInjectBench provides a comprehensive benchmark study on detecting prompt injection attacks targeting web agents.