  The rapid integration of Generative AI (GenAI) and Large Language Models
(LLMs) in sectors such as education and healthcare have marked a significant
advancement in technology. However, this growth has also led to a largely
unexplored aspect: their security vulnerabilities. As the ecosystem that
includes both offline and online models, various tools, browser plugins, and
third-party applications continues to expand, it significantly widens the
attack surface, thereby escalating the potential for security breaches. These
expansions in the 6G and beyond landscape provide new avenues for adversaries
to manipulate LLMs for malicious purposes. We focus on the security aspects of
LLMs from the viewpoint of potential adversaries. We aim to dissect their
objectives and methodologies, providing an in-depth analysis of known security
weaknesses. This will include the development of a comprehensive threat
taxonomy, categorizing various adversary behaviors. Also, our research will
concentrate on how LLMs can be integrated into cybersecurity efforts by defense
teams, also known as blue teams. We will explore the potential synergy between
LLMs and blockchain technology, and how this combination could lead to the
development of next-generation, fully autonomous security solutions. This
approach aims to establish a unified cybersecurity strategy across the entire
computing continuum, enhancing overall digital security infrastructure.
