Intell-Dragonfly: A Cybersecurity Attack Surface Generation Engine Based on Artificial Intelligence Generated Content Technology

With the escalating complexity of digital infrastructure, traditional cybersecurity defense methods are increasingly limited, highlighting an urgent need for innovative attack surface generation. This study proposes Intell-dragonfly, an advanced cybersecurity attack surface generation engine leveraging cutting-edge Large Language Models (LLMs), specifically harnessing the capabilities of DeepSeek. To navigate dynamic threats and ethical considerations, Intell-dragonfly incorporates robust prompt engineering strategies, enabling the creation of diversified, personalized, and context-aware attack scenarios. Its effectiveness is quantitatively verified through rigorous experiments, demonstrating significant improvements in attack surface coverage, attack vector diversity, and generation efficiency compared to traditional methods. Furthermore, the paper provides initial empirical analysis of generated vulnerability effectiveness, showcasing its potential to assist researchers in identifying novel attack paths. Beyond this, Intell-dragonfly’s design considers enhancing interpretability and traceability, and its future evolution includes multi-agent collaborative systems. This work crucially emphasizes its application in strengthening digital infrastructure security, targeting critical systems like ICS, SCADA, and cloud environments, thus contributing to a more proactive and adaptable paradigm for automated cybersecurity defense and offensive research.