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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

Korean

대형 언어 모델 (LLMs)은 동적, 컨텍스트 인식 반응을 생성함으로써 꿀포트 리얼리즘을 향상시킬 수 있지만 강력한 클라우드 모델은 상당한 비용과 지연률을 부담하지만 가벼운 모델은 약한 속임수를 제공 할 수 있습니다. 이것은 실무자들에게 운영 효율성과 상호 작용 충실성 사이를 선택하도록 강요합니다. 이 작업에서 우리는 HydraPoT를 소개합니다. 전통적인 에뮬레이션, 가벼운 에이전트 컨텍스트 보존 벤치마크 및 강력한 클라우드 LLM을 통합하는 구성 가능한 멀티 에이전트 꿀포트 프레임 워크. HydraPoT는 구성 가능한 복잡성 기반 정책에 따라 각 공격자 명령을 적절한 에

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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