LLMs (Large Language Models) are susceptible to various jailbreak techniques that exploit their instruction-following capabilities, context manipulation, and misdirection to bypass safety measures designed to prevent harmful outputs. Common strategies include direct injection, system override, and prompt engineering attacks, often leveraging academic or research contexts to legitimize requests for restricted content. These approaches manipulate LLMs by presenting harmful requests as legitimate tasks such as documentation, data analysis, or storytelling, thereby exploiting their understanding of language and context. Defensive measures against such attacks involve layered strategies, including input preprocessing, conversation monitoring, behavioral analysis, response filtering, and proactive security testing, all aimed at creating a robust system to detect and prevent manipulation attempts. The article emphasizes the importance of understanding these vulnerabilities for developers and security professionals as LLMs become more integrated into applications, highlighting the similarities between social engineering tactics used on humans and those employed to manipulate AI systems.