Kamolchanok Saengtong, Chutirat Saengyingyongwattana, Sasin Janpuangtong, Norrathep Rattanavipanon · 2026 · arXiv
DOI: 10.48550/arXiv.2610.27722 · open-access PDF
Große Sprachmodelle (LLMs) können Honeypot-Realismus verbessern, indem sie dynamische, kontextbewusste Reaktionen erzeugen, aber leistungsstarke Cloud-Modelle erleiden erhebliche Kosten und Latenzen, während leichte Modelle schwächeren Betrug bieten können. Dies zwingt Praktizierende, zwischen Betriebseffizienz und Interaktionsloyalität zu wählen. In dieser Arbeit präsentieren wir HydraPoT, ein konfigurierbares Multi-Agent Honeypot-Framework, das traditionelle Emulationen, leichte LLMs und leistungsstarke Cloud-LLMs integriert.
Machine translation (Meta M2M100); the English original follows.
Large language models (LLMs) can improve honeypot realism by generating dynamic, context-aware responses, but powerful cloud models incur substantial cost and latency, while lightweight models may provide weaker deception. This forces practitioners to choose between operational efficiency and interaction fidelity. In this work, we present HydraPoT, a configurable multi-agent honeypot framework that integrates traditional emulation, lightweight LLMs, and powerful cloud LLMs. HydraPoT routes each attacker command to an appropriate agent according to a configurable complexity-based policy, while
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