A recent study highlights the ongoing vulnerabilities of large language models to jailbreak attacks, which can exploit weaknesses in AI safety measures. This research emphasizes the importance of developing stronger defenses against these novel threats, as adversarial training has been the primary method for enhancing model robustness. However, challenges in optimization and defining realistic threat models complicate the process. Understanding these dynamics is crucial for advancing AI safety and ensuring that models can withstand unforeseen attacks.