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HydraPoT: A Configurable Multi-Agent Framework for Cost-Aware LLM-Assisted Honeypots

Kamolchanok Saengtong, Chutirat Saengyingyongwattana, Sasin Janpuangtong, Norrathep Rattanavipanon · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27722 · cited by 0 works (OpenAlex) · open-access PDF

Indonesian

Model bahasa besar (LLM) dapat meningkatkan realisme honeypot dengan menghasilkan respons yang dinamis dan sadar konteks, tetapi model cloud yang kuat mengalami biaya dan latensi yang substansial, sementara model ringan dapat memberikan penipuan yang lebih lemah. Ini memaksa praktisi untuk memilih antara efisiensi operasional dan kesetiaan interaksi. Dalam pekerjaan ini, kami memperkenalkan HydraPoT, kerangka kerja honeypot multi-agen yang dapat dikonfigurasi yang mengintegrasikan emulasi tradisional, LLM konteks ringan, dan LLM awan yang kuat. HydraPoT mengarahkan setiap perintah penyerang ke agen yang sesuai sesuai sesuai dengan kebijakan berbasis kompleksitas yang dapat dikonfigurasi, sambil mempertahankan keadaan sesi bersama untuk mempertahankan konsistensi di seluruh transaksi agen. Kami mengevaluasi HydraPoT melalui analisis arsitektur-overhead, benchmark cross-agent

Machine translation (Meta M2M100); the English original follows.

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 LLMs, and powerful cloud LLMs. HydraPoT routes each attacker command to an appropriate agent according to a configurable complexity-based policy, while maintaining shared session state to preserve consistency across agent transitions. We evaluate HydraPoT through an architectural-overhead analysis, a cross-agent context-preservation benchmark, and a three-agent case study. HydraPoT introduces less than 5% runtime overhead, preserves context across heterogeneous agents, and achieves fidelity close to a cloud-LLM-only honeypot. Under our case-study policy, it reduces cloud-processed tokens by 72.6% and operational cost by 3.7x. These results demonstrate that configurable multi-agent routing can make realistic LLM-assisted honeypots more practical under limited operational budgets.

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Topics: Honeypot, Computer science, Cloud computing, Consistency (knowledge bases), Context (archaeology), State (computer science)

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