MetaAID 2.5: A Secure Framework for Developing Metaverse Applications
  via Large Language Models
Hongyin Zhu
Large language models (LLMs) are increasingly being used in Metaverse
environments to generate dynamic and realistic content and to control the
behavior of non-player characters (NPCs). However, the cybersecurity concerns
associated with LLMs have become increasingly prominent. Previous research has
primarily focused on patching system vulnerabilities to enhance cybersecurity,
but these approaches are not well-suited to the Metaverse, where the virtual
space is more complex, LLMs are vulnerable, and ethical user interaction is
critical. Moreover, the scope of cybersecurity in the Metaverse is expected to
expand significantly. This paper proposes a method for enhancing cybersecurity
through the simulation of user interaction with LLMs. Our goal is to educate
users and strengthen their defense capabilities through exposure to a
comprehensive simulation system. This system includes extensive Metaverse
cybersecurity Q&A and attack simulation scenarios. By engaging with these,
users will improve their ability to recognize and withstand risks.
Additionally, to address the ethical implications of user input, we propose
using LLMs as evaluators to assess user content across five dimensions. We
further adapt the models through vocabulary expansion training to better
understand personalized inputs and emoticons. We conduct experiments on
multiple LLMs and find that our approach is effective.