The field of Large Language Models (LLMs) is moving towards a greater emphasis on safety and robustness, with a focus on identifying and mitigating potential vulnerabilities and biases. Researchers are exploring various approaches to improve the trustworthiness of LLMs, including the development of novel threat taxonomies, multi-metric evaluation frameworks, and safety protocols. One key area of research is the investigation of LLMs' responses to high-stakes prompts and their potential to provide confident but misguided advice. Another important direction is the analysis of threat-based manipulation in LLMs, which has revealed both vulnerabilities and opportunities for performance enhancement. Additionally, researchers are working on promoting online safety by simulating unsafe conversations with LLMs and exploiting synergistic cognitive biases to bypass safety mechanisms. Notable papers in this area include: Can You Trust an LLM with Your Life-Changing Decision, which demonstrates the need for nuanced benchmarks to ensure LLMs can be trusted with life-changing decisions. Analysis of Threat-Based Manipulation in Large Language Models, which introduces a novel threat taxonomy and multi-metric evaluation framework to quantify both negative manipulation effects and positive performance improvements. Exploiting Synergistic Cognitive Biases to Bypass Safety in LLMs, which proposes a novel red-teaming framework that systematically leverages both individual and combined cognitive biases to undermine LLM safeguards.