In the contemporary realm of cybersecurity, the dynamic and rapidly evolving nature of cyber threats necessitates advanced protective measures. Large Language Models (LLMs), with their sophisticated capabilities in natural language processing, have emerged as a cutting-edge tool in bolstering cybersecurity defenses. This paper investigates the application of LLMs in two pivotal cybersecurity domains: penetration testing and threat detection. It underscores the innovations introduced by tools such as Open Interpreter, which enable LLMs to interact with system terminals for executing code and conducting comprehensive security protocols. Furthermore, the paper critically examines the integration of LLMs in creating intelligent firewalls and smart defense systems, harnessing databases like MITRE's ATT&CK and NIST frameworks [a][b] for proactive threat management. However, the powerful functionalities of LLMs also present potential risks and ethical dilemmas, particularly concerning their dual-use nature and potential for misuse in accelerating hacking techniques or automating malware creation. The paper delves into the ethical landscape, emphasizing the importance of security by design, ethical guidelines, and regulatory frameworks to ensure responsible AI development and application in cybersecurity. Moreover, privacy concerns are addressed, highlighting the necessity for stringent data protection measures in LLM deployment. In conclusion, the Experimental Procedure section of this paper demonstrates the effective application of Open Interpreter in system vulnerability assessments, highlighting the critical role of AI-driven security measures in modern cybersecurity. The findings emphasize the necessity for continuous investment in these technologies and collaboration among various stakeholders to create a cybersecurity environment that is both resilient and adaptable to emerging threats.