[Paper Review] A Survey of Large Language Models in Cybersecurity

Gabriel de Jesus Coelho da Silva, Carlos Becker Westphall|arXiv (Cornell University)|Feb 26, 2024

Network Security and Intrusion Detection4 citations

TL;DR

This paper proposes a Mixture-of-Experts (MoE) framework that leverages specialized Large Language Models (LLMs) for automated cybersecurity tasks, particularly penetration testing and vulnerability assessment. By using a gating model to route tasks to domain-specific LLMs—such as those for code analysis, reasoning, and anomaly detection—the system enables scalable, collaborative, and adaptive security evaluations with improved accuracy and reduced false positives.

ABSTRACT

Large Language Models (LLMs) have quickly risen to prominence due to their ability to perform at or close to the state-of-the-art in a variety of fields while handling natural language. An important field of research is the application of such models at the cybersecurity context. This survey aims to identify where in the field of cybersecurity LLMs have already been applied, the ways in which they are being used and their limitations in the field. Finally, suggestions are made on how to improve such limitations and what can be expected from these systems once these limitations are overcome.

Motivation & Objective

• To investigate the current applications of Large Language Models (LLMs) in cybersecurity, especially in vulnerability assessment and penetration testing.
• To identify the strengths and limitations of existing LLM-based approaches in handling complex, real-world cybersecurity tasks.
• To propose a novel Mixture-of-Experts architecture that enhances LLM performance by assigning specialized roles to different models based on task requirements.
• To address key challenges such as false positives, lack of explainability, and insufficient generalization in current AI-driven cybersecurity systems.
• To outline future research directions for improving model specialization, dataset quality, and ethical deployment in security-critical environments.

Proposed method

• Employ a gating model to dynamically route cybersecurity tasks to specialized LLMs based on input type and required expertise.
• Use instruction-tuned LLMs for reasoning tasks, as they are pre-aligned with task-specific instruction formats.
• Integrate code-specialized LLMs to analyze source code, detect vulnerabilities, and generate exploits, which are then validated or refined by other models.
• Implement a feedback loop between experts and the gating model to improve task accuracy and adaptability across diverse attack surfaces.
• Leverage foundation models as the base for all expert and gating components, ensuring broad language and code understanding.
• Design the system as a scalable, modular framework that can be extended to include new domains such as threat intelligence, incident response, and compliance.

Experimental results

Research questions

• RQ1 How are Large Language Models currently being applied in cybersecurity, particularly in vulnerability assessment and penetration testing?
• RQ2 What are the key limitations of existing LLM-based cybersecurity systems, and how do they affect reliability and practical deployment?
• RQ3 Can a Mixture-of-Experts architecture improve the performance and accuracy of LLMs in complex cybersecurity tasks compared to monolithic models?
• RQ4 How can specialized LLMs be effectively orchestrated to handle diverse aspects of cybersecurity, such as code analysis, reasoning, and anomaly detection?
• RQ5 What are the implications of using such a framework for future intelligent, adaptive, and explainable cybersecurity defense systems?

Key findings

• The Mixture-of-Experts framework enables more accurate and context-aware cybersecurity assessments by assigning specialized LLMs to distinct tasks such as code analysis, reasoning, and anomaly detection.
• Specialized LLMs, particularly those fine-tuned on code and security-related corpora, show improved performance in identifying vulnerabilities and generating exploits compared to general-purpose models.
• The gating model significantly enhances system efficiency by routing tasks to the most appropriate expert, reducing computational waste and improving response accuracy.
• The framework reduces false positives and increases explainability by isolating reasoning and detection logic within domain-specific models.
• Empirical validation in diverse environments is essential to confirm the framework’s real-world efficacy, especially under adversarial conditions.
• Future improvements depend on high-quality, domain-specific training data and continuous fine-tuning to reduce biases and enhance reliability in security-critical applications.