  Honeypots are essential tools in cybersecurity. However, most of them (even
the high-interaction ones) lack the required realism to engage and fool human
attackers. This limitation makes them easily discernible, hindering their
effectiveness. This work introduces a novel method to create dynamic and
realistic software honeypots based on Large Language Models. Preliminary
results indicate that LLMs can create credible and dynamic honeypots capable of
addressing important limitations of previous honeypots, such as deterministic
responses, lack of adaptability, etc. We evaluated the realism of each command
by conducting an experiment with human attackers who needed to say if the
answer from the honeypot was fake or not. Our proposed honeypot, called shelLM,
reached an accuracy of 0.92. The source code and prompts necessary for
replicating the experiments have been made publicly available.
