MetaAID 2.5: A Secure Framework for Developing Metaverse Applications via Large Language Models

TL;DR

This paper introduces MetaAID 2.5, a secure framework that enhances Metaverse cybersecurity by simulating user interactions with LLMs, educating users, and ethically evaluating content to improve defense capabilities.

Contribution

It presents a novel cybersecurity simulation system for Metaverse applications that uses LLMs for user education and ethical content evaluation, addressing complex virtual environment challenges.

Findings

Effective in improving user cybersecurity awareness

Enhances LLM understanding of personalized inputs and emoticons

Demonstrates robustness across multiple LLMs

Abstract

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.