Abstract. This document provides a comprehensive overview of the future of cybersecurity through Large Language Models (LLM). We present an overview of the evolution of LLM and its current state, focusing on advances in models such as GPT-4, GPT-3.5, BERT, Falcon2, and LLaMA. Our analysis extends to LLM vulnerabilities such as rapid deployment, insecure output processing, data poisoning, DDoS attacks, and adversarial instructions. We will take a detailed look at mitigation strategies to protect these models, providing a comprehensive overview of potential attack scenarios and methods to prevent them. This analytical data is aimed at improving real-time cybersecurity protection and increasing the complexity of LLM applications for threat detection and response. Our document provides a fundamental understanding and strategic direction for integrating LLM into future cybersecurity systems to protect against evolving cyber threats.

Keywords: LLM, cybersecurity, large language models, language modeling, machine learning, NLP, natural language processing.