Measuring and Augmenting Large Language Models for Solving Capture-the-Flag Challenges
Project Overview
The document explores the role of generative AI, particularly Large Language Models (LLMs), in enhancing cybersecurity education through Capture-the-Flag (CTF) competitions. It introduces a benchmark, CTFKnow, aimed at evaluating LLMs' technical proficiency in addressing CTF challenges, alongside the CTFAgent framework, which improves LLM capabilities via Retrieval-Augmented Generation (RAG) and Environmental Augmentation (EA). Findings indicate that while LLMs possess robust technical knowledge, they encounter difficulties in applying this knowledge effectively within CTF scenarios, pointing to areas needing further development in educational tools and methodologies. Additionally, the use of generative AI in assessing cybersecurity knowledge through CTF challenges is highlighted, focusing on LLMs' abilities in knowledge extraction, question generation, and vulnerability detection. However, the document also addresses the challenges of implementing AI in education, such as the necessity for precise benchmarking and the intricacies involved in creating effective educational resources. Overall, the findings suggest that generative AI holds significant potential for advancing cybersecurity education and training, although improvements and careful consideration of challenges are essential for optimal application.
Key Applications
CTF Knowledge Generation and Assessment
Context: Cybersecurity education focused on Capture the Flag (CTF) challenges, targeting students and professionals in computer science to enhance their cybersecurity skills.
Implementation: Utilization of large language models (LLMs) for knowledge extraction from CTF write-ups and the generation of assessment questions, alongside the integration of retrieval-augmented generation (RAG) for knowledge retrieval and an interactive environment support system.
Outcomes: Enhanced understanding of cybersecurity concepts among learners and improved performance in solving CTF challenges, with significant performance gains over previous methods and improved assessment quality.
Challenges: Challenges include the accuracy of knowledge extraction, potential biases in LLMs, difficulties in applying technical knowledge to specific CTF scenarios, and limitations arising from missing tools or environment issues.
Implementation Barriers
Technical limitation
LLMs often fail to correctly apply their technical knowledge to specific CTF scenarios and may struggle with accurately extracting and inferring knowledge from complex CTF write-ups.
Proposed Solutions: Enhancing the reasoning capabilities of LLMs, continual improvement of LLM algorithms, and training on diverse datasets.
Environmental limitations
LLMs face issues with missing necessary tools or commands within the CTF environment.
Proposed Solutions: Integrating more advanced tools and dynamic command-line environments to facilitate better interaction.
Content Barrier
The generated questions might lack clarity or relevance to the CTF challenges.
Proposed Solutions: Incorporating feedback mechanisms from educators to refine question relevance and quality.
Project Team
Zimo Ji
Researcher
Daoyuan Wu
Researcher
Wenyuan Jiang
Researcher
Pingchuan Ma
Researcher
Zongjie Li
Researcher
Shuai Wang
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Zimo Ji, Daoyuan Wu, Wenyuan Jiang, Pingchuan Ma, Zongjie Li, Shuai Wang
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