OllamaDrama：设计并部署蜜罐以测量针对暴露 LLM 基础设施的攻击

Computer Science > Cryptography and Security arXiv:2609.29757 (cs) [Submitted on 24 Sep 2026] Title:OllamaDrama: Designing and Deploying a Honeypot to Measure Attacks on Exposed LLM Infrastructure Authors:Karina Elzer, Niklas Netterstrøm Johansen, Emmanouil Vasilomanolakis View a PDF of the paper titled OllamaDrama: Designing and Deploying a Honeypot to Measure Attacks on Exposed LLM Infrastructure, by Karina Elzer and 2 other authors View PDF HTML (experimental) Abstract:Publicly exposed large language model (LLM) infrastructure creates a growing attack surface, yet real-world targeting remains poorly understood. We present Ollure, a low- and medium-interaction honeypot that emulates the Ollama API without a backend LLM. Spanning four deployments across cloud and university networks, Ollure operated for 84 days and recorded 290,887 interactions from 2,793 unique source IP addresses. Most of the activity consisted of automated discovery, fingerprinting, and model enumeration. However, we also observed concrete exploitation attempts against both the infrastructure and LLM layers. These included model management abuse, path traversal and SSRF probes, RCE and cryptocurrency mining payloads, resource exhaustion attempts, prompt injection, information extraction, and agent-oriented tool use. Our results provide empirical insight into real-world threats against exposed, self-hosted LLM services. Comments: Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2609.29757 [cs.CR] (or arXiv:2609.29757v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2609.29757 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Karina Elzer [view email] [v1] Thu, 24 Sep 2026 13:06:48 UTC (316 KB) Full-text links: Access Paper: View a PDF of the paper titled OllamaDrama: Designing and Deploying a Honeypot to Measure Attacks on Exposed LLM Infrastructure, by Karina Elzer and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CR new | recent | 2026-09 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: 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 Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)