The field of large language models is rapidly evolving, with a growing focus on security and resilience against adversarial attacks. Recent research has highlighted the importance of comprehensive evaluation benchmarks to assess the strengths and weaknesses of these models. The development of innovative benchmarks and evaluation metrics is crucial for advancing the field and ensuring the safe deployment of large language models. Noteworthy papers in this area include: SecReEvalBench, which introduces a multi-turned security resilience evaluation benchmark for large language models, providing critical insights into their strengths and weaknesses. A Large-Scale Empirical Analysis of Custom GPTs' Vulnerabilities, which reveals the prevalence of security vulnerabilities in custom GPTs and highlights the need for enhanced security measures. On the Account Security Risks Posed by Password Strength Meters, which exposes the risks of password strength meters and demonstrates the need for privacy-preserving meters. Towards Contamination Resistant Benchmarks, which proposes a novel approach to contamination-resistant benchmarks, enabling more rigorous evaluation of large language models.