Large Language Models (LLMs) are transforming cybersecurity by offering new opportunities and challenges. This paper explores the defensive and adversarial applications of LLMs in cybersecurity, focusing on their potential to enhance security measures and the risks they pose. The study reviews existing literature, categorizing LLM-based approaches within the NIST cybersecurity framework and the MITRE attack framework. It highlights the use of LLMs in identifying, protecting, detecting, responding to, and recovering from cyber threats. LLMs are used for risk assessment, threat detection, and automated vulnerability fixing, improving cybersecurity defenses. However, they can also be misused by attackers to generate phishing emails, evade detection, and steal credentials. The paper also discusses the use of LLMs in offensive attacks, such as reconnaissance, initial access, execution, defense evasion, credential access, collection, and command and control. The study emphasizes the need for further research to address the risks associated with LLMs in cybersecurity. The paper concludes that while LLMs offer significant benefits for cybersecurity, their potential for misuse requires careful consideration and mitigation strategies.Large Language Models (LLMs) are transforming cybersecurity by offering new opportunities and challenges. This paper explores the defensive and adversarial applications of LLMs in cybersecurity, focusing on their potential to enhance security measures and the risks they pose. The study reviews existing literature, categorizing LLM-based approaches within the NIST cybersecurity framework and the MITRE attack framework. It highlights the use of LLMs in identifying, protecting, detecting, responding to, and recovering from cyber threats. LLMs are used for risk assessment, threat detection, and automated vulnerability fixing, improving cybersecurity defenses. However, they can also be misused by attackers to generate phishing emails, evade detection, and steal credentials. The paper also discusses the use of LLMs in offensive attacks, such as reconnaissance, initial access, execution, defense evasion, credential access, collection, and command and control. The study emphasizes the need for further research to address the risks associated with LLMs in cybersecurity. The paper concludes that while LLMs offer significant benefits for cybersecurity, their potential for misuse requires careful consideration and mitigation strategies.