HydraPoT: A Configurable Multi-Agent Framework for Cost-Aware LLM-Assisted Honeypots
HydraPoT introduces a multi-agent honeypot framework that dynamically routes attacker interactions between cloud and lightweight LLMs, balancing deception realism against operational cost — practical for SOC teams scaling deceptive defences.
Summary written by editorial AI · Source link below
arXiv:2610.27722v1 Announce Type: new Abstract: 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 LLM
Editorial Analysis
AI-augmented honeypots can dramatically improve attacker engagement, but cloud-LLM costs can spiral; a cost-aware orchestration framework makes this approach viable for budget-conscious security teams.
Pilot a cost-tiered LLM honeypot using HydraPoT's approach and measure attacker dwell time versus operational spend.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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