The increasing popularity of large language models (LLM) has raised important concerns regardingtheir security and the implications of their misuse. This thesis evaluates the vulnerability of LLMs toknown jailbreaking techniques and the potential harm caused by successful attacks. Using a structuredscoring framework, three popular LLMs (GPT-4o, DeepSeek-V3, and Mistral’s Le Chat) were testedagainst five jailbreaking techniques. All models were susceptible to varying degrees, with DeepSeek-V3 being the most vulnerable, particularly to role-playing. Although most jailbroken responses wereconsidered Low Harm, a few were highly actionable with weak ethical disclaimers, underscoring real-world risks. The thesis emphasizes the importance of prioritizing improved pattern recognition, strictercontextual adherence, and responsible access controls for sensitive capabilities, especially when itcomes to cybersecurity-related content. Until these issues are addressed, jailbreaking will remain athreat requiring proactive and adaptive mitigation strategies.