Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques

TL;DR

This survey explores how Large Language Models are revolutionizing cybersecurity through innovative applications, while also examining their vulnerabilities and proposing strategies for secure deployment.

Contribution

It provides a comprehensive overview of LLM applications in cybersecurity and analyzes their vulnerabilities with mitigation strategies, highlighting recent advancements and limitations.

Findings

LLMs enhance threat detection and incident response.

Vulnerabilities of LLMs include adversarial attacks and data privacy issues.

Mitigation strategies improve LLM robustness in cybersecurity.

Abstract

Large Language Models (LLMs) are transforming cybersecurity by enabling intelligent, adaptive, and automated approaches to threat detection, vulnerability assessment, and incident response. With their advanced language understanding and contextual reasoning, LLMs surpass traditional methods in tackling challenges across domains such as IoT, blockchain, and hardware security. This survey provides a comprehensive overview of LLM applications in cybersecurity, focusing on two core areas: (1) the integration of LLMs into key cybersecurity domains, and (2) the vulnerabilities of LLMs themselves, along with mitigation strategies. By synthesizing recent advancements and identifying key limitations, this work offers practical insights and strategic recommendations for leveraging LLMs to build secure, scalable, and future-ready cyber defense systems.