mlsanityData to 14 Oct 2026

AdvancedShelLM: A Stateful Multi-Agent LLM Honeypot for SSH Deception

arXiv 2606.27990 ↗First posted 26 Jun 2026cs.CRcited by 1

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

LLM-based SSH honeypots can generate believable interactions, but evaluations indicate they remain somewhat identifiable to determined attackers, indicating the need for a better scaffolding. We present a new LLM-based honeypot design that uses a multi-agent, multi-LLM architecture to address the limitations of the previous shelLM LLM honeypot. Our honeypot, called AdvancedShelLM, uses two LLM agents, a Manager and a Worker, that better understand the commands while reducing incorrect responses and increasing deception. It implements an advanced permanent filesystem, allowing many simultaneous attackers to see the same changing files for the first time. It was evaluated with: (i) unit tests for generative capabilities, (ii) an AI attacker (ARACNE) to assess realism and deception, (iii) human attackers to assess its deceptive capability, and (iv) an Internet deployment to evaluate deception in real-world attacks. In unit test results, AdvancedShelLM achieved a pass rate of up to 99.02%. The AI attacker ARACNE had issues making a decision if the system is honeypot or not, but showed slight bias towards saying honeypot, even for a real Ubuntu shell. With human attackers, AdvancedShelLM deceived more humans than Cowrie, but had similar results as shelLM. The Internet deployment showed concrete evidence that the output of AdvancedShelLM can influence the behaviour of real-life attackers.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 9 of its 19 references that are in the map. First posted on arXiv 26 Jun 2026.

Builds on

9 of 19 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025ARACNE: An LLM-Based Autonomous Shell Pentesting Agent24 Feb
  2. 2024LLM Agent Honeypot: Monitoring AI Hacking Agents in the Wild17 Oct

Other references · 7

  1. 2023LLM in the Shell: Generative Honeypotscited by 19
  2. 2024LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systemscited by 10
  3. 2023Chatbots in a Honeypot Worldcited by 9
  4. 2025LLMHoney: A Real-Time SSH Honeypot with Large Language Model-Driven Dynamic Response Generationcited by 3
  5. 2025VelLMes: A high-interaction AI-based deception frameworkcited by 3
  6. 2025SoK: Honeypots & LLMs, More Than the Sum of Their Parts?cited by 3
  7. 2025SBASH: a Framework for Designing and Evaluating RAG vs. Prompt-Tuned LLM Honeypotscited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026Honeyval: A Comprehensive Evaluation Framework for LLM-powered HTTP Honeypots
  2. 2026LLM-Based Penetration Testing in the Presence of Honeypots ↗
  3. 2026Honeyquest for LLMs: Rethinking Cyber Deception for AI Attackers
  4. 2024HoneyGPT: Breaking the Trilemma in Terminal Honeypots with Large Language Model
  5. 2025The Dark Side of LLMs: Agent-based Attack Vectors for System-level Compromise
  6. 2026Speculative Safety Honeypot: Toward Proactive Defense Against Multi-turn Agent Attacks
  7. 2026Towards Agentic Honeynet Configuration
  8. 2025MALCDF: A Distributed Multi-Agent LLM Framework for Real-Time Cyber