DecoyPot: A large language model-driven web API honeypot for realistic attacker engagement

Abstract

As cyberattacks get more sophisticated, security systems must learn to detect and deceive them. DecoyPot, a honeypot Web Application Programming Interface (API) that generates legitimate API responses, is introduced in this paper. DecoyPot's command extractor module carefully analyzes API requests to create prompt-response pairs that improve a Retrieval-Augmented Generation based (RAG) large language model (LLM). DecoyPot can instantly adjust its answers to mimic API activity in a contextually correct and convincing manner to attackers. To assess system efficacy, we used a two-phase similarity analysis. Initial queries were matched with prompt-response pairs to ensure contextually suitable responses. Second, similarity measures were used to compare generated responses to reference responses, producing an average score of 0.9780. The high score shows that the system can create API-like responses, boosting its utility. DecoyPot engaged opponents and learned their Tactics, Techniques and Procedures (TTPs). The study shows that honeypot cybersecurity effectiveness must be improved by merging AI-driven response creation with enhanced deception technologies. DecoyPot effectively adapts to incoming queries and generates API-like responses, delivering actionable cyber threat intelligence and enhancing proactive defense strategies.