Large Language Models (LLMs) are susceptible to adaptive jailbreaking attacks that leverage their semantic understanding capabilities. The MEF framework illustrates how attacks can be customized to align with the model's comprehension level, classified as Type I or Type II, thereby enhancing the evasion of defenses at the input, inference, and output levels. This is accomplished through the application of layered semantic mutations and dual-ended encryption techniques, enabling the circumvention of security protocols even in sophisticated models such as GPT-4o. Mitigation steps: **For AI Developers:** * Improve input and output filtering by implementing advanced keyword and semantic checks. * Develop dynamic defense mechanisms that adapt to new jailbreaking techniques. * Implement multiple layers of defense to increase difficulty in bypassing security measures. **For Model Trainers/Fine-tuners:** * Implement robust semantic analysis of prompt intent to enhance model understanding. * Enhance internal model safeguards to prevent processing of harmful content, regardless of input phrasing. * Regularly evaluate and update model safety mechanisms to counteract evolving jailbreaking methods.