A recent study highlights the potential of large language models (LLMs) in generating robust responses without extensive training, which is a game-changer for various applications. However, the research emphasizes the importance of evaluating these models against adversarial inputs to ensure their reliability. The introduction of two new frameworks, Static Deceptor and Dynamic Deceptor, aims to enhance the security of LLMs by systematically generating challenging inputs. This advancement is crucial as it not only improves the models' performance but also safeguards sensitive tasks from potential exploitation.