GenPot: A generative honeypot architecture for adaptive web and API interaction

Abstract

Honeypots are widely used as cyber-deception tools to study adversarial behaviour, yet their effectiveness is limited by a trade-off between realism and security risk. Low-interaction honeypots are easily detected, while high-interaction honeypots provide realistic data at the cost of network exposure. Recent advances suggest that Large Language Models (LLMs) can mitigate this trade-off by dynamically generating convincing outputs without requiring a vulnerable backend. In this paper, we present

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@article{laragutierrez2026genpot,
  title = {{GenPot: A generative honeypot architecture for adaptive web and API interaction}},
  author = {Antonio Lara-Gutierrez and Juan Zamorano and J. A. Onieva},
  year = {2026},
  month = sep,
  journal = {Applied intelligence (Boston)},
  doi = {10.1007/s10489-026-07442-4},
  url = {https://www.semanticscholar.org/paper/c8446206f0a9cba0979e0efba154610fd12b08a3}
}