Researchers have developed new techniques to jailbreak large language models (LLMs), raising concerns about their safety and alignment. A benchmark called JailbreakBench has been introduced to evaluate the security of these models. A powerful new jailbreak technique has been found effective against all tested models, indicating that current methods to safeguard LLMs may be insufficient. The RED QUEEN attack, a multi-turn jailbreak approach, has shown high success rates, particularly on larger models such as GPT-4o and Llama3-70B, with success rates of 87.62% and 75.4%, respectively. This increased vulnerability is attributed to the mismatch between model capabilities and safety alignment training. Additionally, Liquid AI and LIQUID-40B models have been easily compromised, highlighting the challenges faced by this new generation of generative AI.