LLMs' Suitability for Network Security: A Case Study of STRIDE Threat Modeling

2025/05/07 by AbdulAziz AbdulGhaffar, AbdulGhaffar, AbdulAziz, Ashraf Matrawy +1 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Information and Cyber Security #Network Security and Intrusion Detection #Networking and Internet Architecture (cs.NI) #Software-Defined Networks and 5G

paper · pdf · doi:10.48550/arxiv.2505.04101

openalex publication_date 2025/05/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Artificial Intelligence (AI) is expected to be an integral part of next-generation AI-native 6G networks. With the prevalence of AI, researchers have identified numerous use cases of AI in network security. However, there are very few studies that analyze the suitability of Large Language Models (LLMs) in network security. To fill this gap, we examine the suitability of LLMs in network security, particularly with the case study of STRIDE threat modeling. We utilize four prompting techniques with five LLMs to perform STRIDE classification of 5G threats. From our evaluation results, we point out key findings and detailed insights along with the explanation of the possible underlying factors influencing the behavior of LLMs in the modeling of certain threats. The numerical results and the insights support the necessity for adjusting and fine-tuning LLMs for network security use cases.

Citations

• ThreatLens: LLM-guided Threat Modeling and Test Plan Generation for Hardware Security Verification
• Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences
• A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions
• ThreatModeling-LLM: Automating Threat Modeling using Large Language Models for Banking System
• Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities
• Large Language Model (LLM) for Telecommunications: A Comprehensive Survey on Principles, Key Techniques, and Opportunities
• PLeak: Prompt Leaking Attacks against Large Language Model Applications
• Large Language Models for Networking: Applications, Enabling Techniques, and Challenges
• netFound: Principled Design for Network Foundation Models
• Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks
• Language Models are Few-Shot Learners

Cited by

• Agentic AI for 6G: A New Paradigm for Autonomous RAN Security Compliance

Related

• LLMs Killed the Script Kiddie: How Agents Supported by Large Language Models Change the Landscape of Network Threat Testing
• Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions
• Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey
• Information Security Based on LLM Approaches: A Review
• Large Language Models in Cybersecurity: State-of-the-Art