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

arXiv Security Archived Jun 29, 2026 ✓ Full text saved

arXiv:2606.27990v1 Announce Type: new Abstract: 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

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    Computer Science > Cryptography and Security [Submitted on 26 Jun 2026] AdvancedShelLM: A Stateful Multi-Agent LLM Honeypot for SSH Deception Muris Sladić, Eman Alibalić, Veronica Valeros, Carlos Catania, Sebastian Garcia 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. Comments: 18 pages Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2606.27990 [cs.CR]   (or arXiv:2606.27990v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2606.27990 Focus to learn more Submission history From: Veronica Valeros [view email] [v1] Fri, 26 Jun 2026 11:39:05 UTC (875 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
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    arXiv Security
    Category
    ◬ AI & Machine Learning
    Published
    Jun 29, 2026
    Archived
    Jun 29, 2026
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