The rise of Large Language Models (LLMs) has revolutionized our comprehension of intelligence bringing us closer to Artificial Intelligence. Since their introduction, researchers have actively explored the applications of LLMs across diverse fields, significantly elevating capabilities. Cybersecurity, traditionally resistant to data-driven solutions and slow to embrace machine learning, stands out as a domain. This study examines the existing literature, providing a thorough characterization of both defensive and adversarial applications of LLMs within the realm of cybersecurity. Our review not only surveys and categorizes the current landscape but also identifies critical research gaps. By evaluating both offensive and defensive applications, we aim to provide a holistic understanding of the potential risks and opportunities associated with LLM-driven cybersecurity.

翻译：大型语言模型（LLMs）的兴起彻底改变了我们对智能的理解，使我们距离人工智能更近一步。自其问世以来，研究人员积极探索LLMs在各个领域的应用，显著提升了相关能力。网络安全作为传统上对数据驱动解决方案持抵制态度且对机器学习接纳缓慢的领域，成为其中的一个典型领域。本研究梳理现有文献，对网络安全领域中LLMs的防御性和对抗性应用进行了全面刻画。本文不仅对当前研究状况进行了调查与分类，还识别出关键的研究空白。通过评估进攻性与防御性应用，我们旨在全面理解LLMs驱动的网络安全所带来的潜在风险与机遇。