The paper "Large Language Models in Cybersecurity: State-of-the-Art" by Farzad Nourmohammadzadeh Motlagh explores the integration of Large Language Models (LLMs) into cybersecurity, a field traditionally resistant to data-driven solutions. The study reviews existing literature to provide a comprehensive characterization of both defensive and adversarial applications of LLMs in cybersecurity. It categorizes these applications using the NIST cybersecurity framework and the MITRE attack framework, highlighting the potential risks and opportunities associated with LLM-driven cybersecurity. Key findings include: - **Defensive Applications**: LLMs are effective in identifying risks, protecting systems, detecting anomalies, and responding to cyberattacks. They can enhance risk management, improve web content filtration, and automate vulnerability fixes. - **Adversarial Applications**: LLMs can be misused for reconnaissance, initial access, execution, defense evasion, credential access, collection, and command and control. They can generate sophisticated phishing messages, create malware, and bypass security measures. The paper concludes by emphasizing the need for further research to address the challenges posed by LLMs in cybersecurity, particularly in post-attack scenarios and offensive applications. The study aims to guide future research in developing robust defensive strategies and mitigating the risks associated with LLMs in cybersecurity.The paper "Large Language Models in Cybersecurity: State-of-the-Art" by Farzad Nourmohammadzadeh Motlagh explores the integration of Large Language Models (LLMs) into cybersecurity, a field traditionally resistant to data-driven solutions. The study reviews existing literature to provide a comprehensive characterization of both defensive and adversarial applications of LLMs in cybersecurity. It categorizes these applications using the NIST cybersecurity framework and the MITRE attack framework, highlighting the potential risks and opportunities associated with LLM-driven cybersecurity. Key findings include: - **Defensive Applications**: LLMs are effective in identifying risks, protecting systems, detecting anomalies, and responding to cyberattacks. They can enhance risk management, improve web content filtration, and automate vulnerability fixes. - **Adversarial Applications**: LLMs can be misused for reconnaissance, initial access, execution, defense evasion, credential access, collection, and command and control. They can generate sophisticated phishing messages, create malware, and bypass security measures. The paper concludes by emphasizing the need for further research to address the challenges posed by LLMs in cybersecurity, particularly in post-attack scenarios and offensive applications. The study aims to guide future research in developing robust defensive strategies and mitigating the risks associated with LLMs in cybersecurity.