Using Large Language Models for Cybersecurity Capture-The-Flag Challenges and Certification Questions
Project Overview
The document explores the integration of generative AI, particularly Large Language Models (LLMs) like OpenAI's ChatGPT, Google Bard, and Microsoft Bing, in cybersecurity education, emphasizing their use in solving Capture-The-Flag (CTF) challenges and answering professional certification questions. The evaluation highlights that while these models can proficiently handle factual inquiries, they face challenges with more conceptual questions, indicating a limitation in their utility for deeper learning. Furthermore, the findings raise significant concerns about academic integrity and underscore the need for educational institutions to adapt their strategies in response to the capabilities and limitations of generative AI. As such, the study suggests that while LLMs can be beneficial as supplementary tools in education, careful consideration is required to mitigate potential misuse and enhance learning outcomes. Overall, the document advocates for a balanced approach to integrating AI in educational settings, leveraging its strengths while addressing its shortcomings.
Key Applications
Large Language Models (LLMs) for CTF challenges and certification questions
Context: Cybersecurity education, specifically for students participating in CTF exercises and preparing for Cisco certification exams
Implementation: Evaluating LLMs' performance on CTF challenges and Cisco certification questions through structured testing
Outcomes: LLMs demonstrated high accuracy in answering factual questions but struggled with conceptual understanding. ChatGPT solved most CTF challenges while Bard and Bing had limited success.
Challenges: LLMs may bypass ethical guidelines through jailbreak prompts, leading to concerns about academic integrity and reliance on AI for learning.
Implementation Barriers
Ethical
Concerns about academic integrity arise from students using LLMs to solve assignments and challenges, potentially undermining the learning process.
Proposed Solutions: Educators should adapt teaching methods to incorporate LLMs responsibly and emphasize the importance of understanding underlying concepts.
Technical
LLMs have limitations in understanding and generating answers for conceptual questions due to lack of reasoning abilities and up-to-date knowledge.
Proposed Solutions: Continuous improvement of LLMs through better training data specific to the cybersecurity domain and enhanced reasoning capabilities.
Project Team
Wesley Tann
Researcher
Yuancheng Liu
Researcher
Jun Heng Sim
Researcher
Choon Meng Seah
Researcher
Ee-Chien Chang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Wesley Tann, Yuancheng Liu, Jun Heng Sim, Choon Meng Seah, Ee-Chien Chang
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai