The field of large language models (LLMs) is rapidly evolving, with a growing focus on evaluating and securing these models. Recent research has highlighted the importance of developing comprehensive benchmarks to assess the security and trustworthiness of LLMs. These benchmarks aim to identify vulnerabilities and weaknesses in LLMs, such as their susceptibility to adversarial manipulations, dark patterns, and exploitation of web application vulnerabilities. The development of evaluation frameworks and tools has also been a key area of research, with a focus on providing reliable and accessible methods for assessing LLM trustworthiness. Noteworthy papers in this area include: SecureWebArena, which introduces a holistic security evaluation benchmark for LVLM-based web agents, and SusBench, which presents an online benchmark for evaluating the susceptibility of computer-use agents to UI dark patterns. These papers demonstrate the need for continued innovation and advancement in the field of LLM evaluation and security.