Recent advancements in Generative Artificial Intelligence have revolutionized the landscape of content creation and have significantly changed how content is conceptualized, developed, and delivered across various industries. Large Language Models (LLMs), such as BERT, T5, ChatGPT, GPT-4, Falcon 180B, and Codex, have influenced most disciplines of science and technology that support content generation in diverse applications, including cybersecurity. In cybersecurity, LLMs represent a dual-purpose tool. On the one hand, they empower malicious actors to identify vulnerabilities and enhance attack strategies. Conversely, they empower security teams to fortify defenses, identify threats, and effectively streamline risk management and operational processes. Despite the anticipated widespread adoption of these LLMs, our understanding of their full impact on cybersecurity still needs to be completed. There is a critical need to assess how they contribute to the discovery of vulnerabilities comprehensively, the development of new attack tactics and techniques, the creation of complex malware patterns, the identification of potential threats, and the mitigation of risks through automated vulnerability remediation

We invite the submission of original papers on all topics related to LLMs and cybersecurity, with special interest in but not limited to:

LLMs-empowered defensive strategies
Offensive approaches using LLMs
LLMs and cybercrime laws
Chatbot software/Apps (BERT, T5, ChatGPT, GPT-4, Falcon 180B , ...) impact on cybersecurity education
LLM for creating cybersecurity policies
Security of LLM-generated code
LLMs-driven threat modeling
LLMs for Solving Offensive Security Challenges such as Capture the Flag
Reliability Issues of using LLMs in the cybersecurity contex
LLMs for generation and analysis of Cyber Threat Intelligence (CTI)
Privacy issues of LLMs and privacy-preserving LLMs
Generating Adversarial machine learning examples using LLMs
LLMs driven threat prevention
LLMs based cybersecurity awareness framework