LLM-Enabled Cloud-Native Dynamic Honeypot Systems: Architecture, Ethical Governance, and Empirical Evaluation

LLM-Enabled Cloud-Native Dynamic Honeypot Systems: Architecture, Ethical Governance, and Empirical Evaluation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article LLM-Enabled Cloud-Native Dynamic Honeypot Systems: Architecture, Ethical Governance, and Empirical Evaluation Shang E. Tsai, Ting T. Tsai, Meng H. Aun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9003091/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Large Language Models (LLMs) can substantially improve honeypot interaction realism, but naïve integration increases operational risk (e.g., prompt injection, unsafe guidance, state inconsistency, and resource exhaustion). This paper presents an LLM-enabled, cloud-native dynamic honeypot architecture that treats the LLM as a strictly text-only synthesizer behind deterministic policy gating and state verification. The exposed SSH/Web surfaces are mediated by a session broker that never executes attacker commands; instead, commands are classified into deterministic emulation, bounded LLM synthesis, plausible error simulation, or quarantined payload capture. The system is deployed as decomposed microservices with deny-by-default networking, controlled egress, authenticated internal service calls, and centralized tamper-evident telemetry. To make the deployment ethically and legally defensible, we operationalize a principlist governance framework into concrete controls including data minimization, bounded retention, access governance, and abuse-rate limiting. Finally, we provide an IJIS-aligned evaluation protocol that separates background Internet scanning noise from adaptive interactive sessions and reports realism, engagement, fingerprint resistance, and safety metrics, including timing-based distribution tests against a real OpenSSH baseline. The resulting design offers a practical path to high-fidelity deception with auditable containment and reproducible measurement. Cyber deception honeypots large language models cloud-native security ethical governance containment Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 24 Apr, 2026 Reviewers invited by journal 24 Apr, 2026 Editor assigned by journal 07 Mar, 2026 Submission checks completed at journal 07 Mar, 2026 First submitted to journal 01 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9003091","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":602259379,"identity":"a7aaf2ef-b209-4327-aa57-30b1f07d0702","order_by":0,"name":"Shang E. 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