Applications of LLMs for Generating Cyber Security Exercise Scenarios
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Abstract
Abstract This study proposes a novel approach leveraging Large Language Models (LLMs) to generate dynamic and complex adaptable cybersecu-rity exercise scenarios. Motivated by Turing’s sem-inal exploration into machine cognition, which questions the ability of machines to mimic human thought and intelligence. By exploiting the generative potential of LLMs, our methodology simulates a wide range of cyber threats, both known and novel, thereby enhancing cybersecurity training and awareness. This approach transforms the potential for ’hal-lucination’ inherent in LLMs into a potential advantage , enabling the creation of complex exercise scenarios that push the boundaries of traditional cybersecurity training. The innovation lies in the sophisticated application of AI, aiming to advance the preparedness of security professionals against diverse cyber threats. The scenarios generated through this method were subject to meticulous testing and a rigorous evaluation process involving GPT models and expert review to ensure their realism and applicability. Furthermore, the prompts provided to the LLMs were meticulously designed to adopt a Retrieval-Augmented Generation (RAG) approach, enriching the complexity and relevance of the scenarios. This incorporation of RAG, alongside the inspiration drawn from Tur-ing’s exploration of machine intelligence, showcases an advanced application of AI in cybersecurity training , reflecting a deep understanding of how machines can augment our capabilities to anticipate and mitigate cyber threats.
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A certain degree of hallucination is intrinsic to models that generalize from their training distributions.
“and the literature indicates that a certain degree of hallucination is intrinsic to models that generalize from their training distributions [17], [18], [19].”
From The Evolution of Agentic AI in Cybersecurity: From Single LLM Reasoners to Multi-Agent Systems and Autonomous Pipelines (2026) · cites this paper as [19]
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Multi-domain text classification via linguistic and semantic feature integration
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Self-citationLLMAC: A Global and Explainable Access Control Framework with Large Language Model
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Integrating IT and OT for Cybersecurity: A Stochastic Optimization Approach via Attack Graphs
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Building adaptative and transparent cyber agents with local language models
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Evaluating Three Large Language Models for Security Incident Detection and Analysis
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LLM-Generated Samples for Android Malware Detection
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Large Language Models Applications in Oil and Gas: Case Studies and Insights
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Integrating Generative AI in Cybersecurity Curricula
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Threatify: APT Threat Variant Generation Using Graph-Based Machine Learning
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CyberLlama2 - MEDICALHARM Threat Modeling Assistant
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Navigating the CISO’s Mind by Integrating GenAI for Strategic Cyber Resilience
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Leveraging EUD and Generative AI for Ethical Phishing Campaigns
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Enhancing Medical Training with AI-Driven Scenario Generation
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Extracting Threats from System Descriptions with LLMs Comparing One and Two Agents Strategies
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Gamifying AI Evaluation: How Well Do Chatbots Perform in Cybersecurity Challenges?
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Language Model-Driven Agent-Based Framework for Generating and Evaluating Cyber Attack Scenarios
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LLM-Augmented Approach for Learning of Human-Centric Cybersecurity
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Goal-Driven Risk Assessment for LLM-Powered Systems: A Healthcare Case Study
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Large Language Models as a Cyber Range
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Self-citationHybrid LLM-Vision Pipeline for Detection of Malicious QR Codes in Social Engineering Attacks
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An Ontology-Based Framework for Semantic Representation of the Cyber Range Domain
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IoT-Specific Cybersecurity Awareness Training (CSAT) Framework
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Developing Adaptive AI Cyber Range Training Modules for Critical Infrastructure Sectors
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AI-Augmented Scenario Design: Experiences from a National Cybersecurity Exercise
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A Dynamic AI-Powered Cyber Range for Cybersecurity Awareness and Skill Testing
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- Authors
- Mudassar Yamin, M., Hashmi, E., Ullah, M., & Katt, B.
- Affiliations
- Department of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology (NTNU), Gjøvik, Norway
- Norwegian University of Science and Technology (NO)
- Venue
- IEEE Access
- Published
- 2024
- Volume
- 12
- Pages
- 143806-143822
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4402892453
- Cited by
- 91
- References
- 62
- Citations by year
- 2026: 33 · 2025: 45 · 2024: 13
- FWCI
- 34.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Norwegian Research Council through the ASCERT Project 3290
- Topics
- Physical Sciences › Computer Science › Information Systems › Information and Cyber Security
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- large language models, scenario generation, cybersecurity training, GPT, retrieval-augmented generation, hallucinations, Turing test, machine intelligence, security awareness, prompt engineering
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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