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A Comprehensive Overview of Large Language Models (LLMs) for Cyber Defences: Opportunities and Directions

Mohammed Hassanin, Nour Moustafa

TL;DR

This paper addresses the problem of understanding how large language models can enhance cyber defence across multiple domains. It adopts a comprehensive survey and taxonomy approach to organize LLM-based methods into threat intelligence, vulnerability assessment, network security, privacy preservation, awareness, and automation, including ethical considerations. The main contributions are a structured synthesis of representative works (e.g., PentestGPT, CAN-BERT, KnowPhish, LLM-TikG) and a critical analysis of strengths, limitations, and open challenges such as data scarcity, real-time constraints, and privacy risks. The findings highlight that LLMs offer significant gains in information processing, threat forecasting, and automated response, but demand careful governance, domain-specific customization, and robust privacy and security safeguards to be practically deployed. The practical impact lies in guiding researchers and practitioners toward responsible, scalable integration of LLMs into cyber security operations, training, and policy development.

Abstract

The recent progression of Large Language Models (LLMs) has witnessed great success in the fields of data-centric applications. LLMs trained on massive textual datasets showed ability to encode not only context but also ability to provide powerful comprehension to downstream tasks. Interestingly, Generative Pre-trained Transformers utilised this ability to bring AI a step closer to human being replacement in at least datacentric applications. Such power can be leveraged to identify anomalies of cyber threats, enhance incident response, and automate routine security operations. We provide an overview for the recent activities of LLMs in cyber defence sections, as well as categorization for the cyber defence sections such as threat intelligence, vulnerability assessment, network security, privacy preserving, awareness and training, automation, and ethical guidelines. Fundamental concepts of the progression of LLMs from Transformers, Pre-trained Transformers, and GPT is presented. Next, the recent works of each section is surveyed with the related strengths and weaknesses. A special section about the challenges and directions of LLMs in cyber security is provided. Finally, possible future research directions for benefiting from LLMs in cyber security is discussed.

A Comprehensive Overview of Large Language Models (LLMs) for Cyber Defences: Opportunities and Directions

TL;DR

This paper addresses the problem of understanding how large language models can enhance cyber defence across multiple domains. It adopts a comprehensive survey and taxonomy approach to organize LLM-based methods into threat intelligence, vulnerability assessment, network security, privacy preservation, awareness, and automation, including ethical considerations. The main contributions are a structured synthesis of representative works (e.g., PentestGPT, CAN-BERT, KnowPhish, LLM-TikG) and a critical analysis of strengths, limitations, and open challenges such as data scarcity, real-time constraints, and privacy risks. The findings highlight that LLMs offer significant gains in information processing, threat forecasting, and automated response, but demand careful governance, domain-specific customization, and robust privacy and security safeguards to be practically deployed. The practical impact lies in guiding researchers and practitioners toward responsible, scalable integration of LLMs into cyber security operations, training, and policy development.

Abstract

The recent progression of Large Language Models (LLMs) has witnessed great success in the fields of data-centric applications. LLMs trained on massive textual datasets showed ability to encode not only context but also ability to provide powerful comprehension to downstream tasks. Interestingly, Generative Pre-trained Transformers utilised this ability to bring AI a step closer to human being replacement in at least datacentric applications. Such power can be leveraged to identify anomalies of cyber threats, enhance incident response, and automate routine security operations. We provide an overview for the recent activities of LLMs in cyber defence sections, as well as categorization for the cyber defence sections such as threat intelligence, vulnerability assessment, network security, privacy preserving, awareness and training, automation, and ethical guidelines. Fundamental concepts of the progression of LLMs from Transformers, Pre-trained Transformers, and GPT is presented. Next, the recent works of each section is surveyed with the related strengths and weaknesses. A special section about the challenges and directions of LLMs in cyber security is provided. Finally, possible future research directions for benefiting from LLMs in cyber security is discussed.
Paper Structure (10 sections, 15 figures)

This paper contains 10 sections, 15 figures.

Table of Contents

  1. Introduction
  2. Threat Intelligence
  3. Vulnerability Assessment
  4. Network Security
  5. Privacy Preservation
  6. Awareness
  7. cyber security operations automation
  8. Ethical LLMs
  9. Challenges and Open Problems
  10. Conclusions

Figures (15)

  • Figure 1: The evolution of LLMs and GPT is based on deep learning, GANs, and Transformers.
  • Figure 2: The publicly available LLMs in the most existing in recent years in a timeline. The timeline is ordered based on the publishing date. Figure from zhao2023survey
  • Figure 3: Visual description of the number of LLMs usage in research in all fields in general.
  • Figure 4: A taxonomy of using LLMs in cyber defence sections. They are categorized based on the type of security. Some techniques may intersect in multiple categories; in this case, they are grouped based on the most dominant characteristic.
  • Figure 5: Visual description of the number of LLMs usage in research in all fields in cyber security fields.
  • ...and 10 more figures