LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systems

Moreover, these honeypots are easier for attackers In the realm of cybersecurity, honeypots have to detect and can only gather limited information proven to be a valuable tool for detecting and about the nature of attacks, resulting in restricted analyzing malicious activity by serving as decoy responses to threats [6]. Additionally, the use of systems that attract potential attackers, allowing fixed rate-limiting thresholds in experiments to prevent organizations to study their tactics and enhance damage may inadvertently reveal their presence their overall security infrastructure [1]. Honeypots to scanners, thus compromising their covert come in various forms, including low-interaction nature [7]. These factors highlight the necessity for honeypots that simulate services with minimal a balanced approach in the deployment and configuration functionality to gather information about general of honeypots to optimize their effectiveness attack patterns [2], and high-interaction honeypots while mitigating inherent limitations.