Large Language Models (LLMs) are susceptible to jailbreak attacks that exploit their inherent persuasive capabilities. The novel attack framework, CL-GSO, systematically decomposes jailbreak strategies into four distinct components: Role, Content Support, Context, and Communication Skills. This decomposition results in a significantly expanded strategy space compared to previous methodologies. Such an expanded space facilitates the creation of prompts that successfully bypass safety protocols, achieving a success rate of over 90% on models previously deemed resistant, such as Claude-3.5. The vulnerability is rooted in the LLM's reasoning and response generation mechanisms, which can be manipulated through strategically crafted prompts utilizing these four components. Mitigation steps: **For AI Developers:** * Develop robust safety mechanisms resistant to diverse attack strategies, focusing on both content and underlying persuasive intent. * Employ sophisticated filtering techniques that analyze prompts for underlying persuasive intent, beyond keyword-based or content-based filtering. **For Model Trainers/Fine-tuners:** * Expand training data for safety alignment to include a wider variety of adversarial prompts and attack strategies, such as those from the CL-GSO framework. * Conduct regular security audits of LLMs using diverse and advanced adversarial testing methods to identify and address vulnerabilities.