# HoneyGPT: Breaking the Trilemma in Terminal Honeypots with Large Language Model

arXiv: https://arxiv.org/abs/2406.01882 · HTML: https://mlsanity.com/paper/2406.01882/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 44 references that are in the map.
- First posted on arXiv 4 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Towards Revealing the Mystery behind Chain of Thought: A Theoretical Perspective](https://mlsanity.com/paper/2305.15408/) (2023)
- [Large Language Models for Code: Security Hardening and Adversarial Testing](https://mlsanity.com/paper/2302.05319/) (2023)
- [Generating Natural Language Proofs with Verifier-Guided Search](https://mlsanity.com/paper/2205.12443/) (2022)
- [Chain of Thought Imitation with Procedure Cloning](https://mlsanity.com/paper/2205.10816/) (2022)
- [Two-in-One: A Model Hijacking Attack Against Text Generation Models](https://mlsanity.com/paper/2305.07406/) (2023)
- [Duplicity Games for Deception Design with an Application to Insider Threat Mitigation](https://mlsanity.com/paper/2006.07942/) (2020)
- [Characterizing Honeypot-Captured Cyber Attacks: Statistical Framework and Case Study](https://mlsanity.com/paper/1603.07433/) (2016)
- [Collaborative Honeypot Defense in UAV Networks: A Learning-Based Game Approach](https://mlsanity.com/paper/2211.01772/) (2022)

## Built on by (most cited first, 5 of 5)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [Honeyval: A Comprehensive Evaluation Framework for LLM-powered HTTP Honeypots](https://mlsanity.com/paper/2605.29963/) (2026)
- [Design and Development of an Intelligent LLM-based LDAP Honeypot](https://mlsanity.com/paper/2509.16682/) (2025)
- [Towards Agentic Honeynet Configuration](https://mlsanity.com/paper/2603.14122/) (2026)
- [HoneyRoute: Honeypot-Model Routing for Adversarial LLM Serving](https://mlsanity.com/paper/2609.08306/) (2026)

## Abstract (arXiv)

Honeypots, as a strategic cyber-deception mechanism designed to emulate authentic interactions and bait unauthorized entities, often struggle with balancing flexibility, interaction depth, and deception. They typically fail to adapt to evolving attacker tactics, with limited engagement and information gathering. Fortunately, the emergent capabilities of large language models and innovative prompt-based engineering offer a transformative shift in honeypot technologies. This paper introduces HoneyGPT, a pioneering shell honeypot architecture based on ChatGPT, characterized by its cost-effectiveness and proactive engagement. In particular, we propose a structured prompt engineering framework that incorporates chain-of-thought tactics to improve long-term memory and robust security analytics, enhancing deception and engagement. Our evaluation of HoneyGPT comprises a baseline comparison based on a collected dataset and a three-month field evaluation. The baseline comparison demonstrates HoneyGPT's remarkable ability to strike a balance among flexibility, interaction depth, and deceptive capability. The field evaluation further validates HoneyGPT's superior performance in engaging attackers more deeply and capturing a wider array of novel attack vectors.