The rapid deployment of large language models (LLMs) has introduced significant security vulnerabilities due to misconfigurations and inadequate access controls. This paper presents a systematic approach to identifying publicly exposed LLM servers, focusing on instances running the Ollama framework. Utilizing Shodan, a search engine for internet-connected devices, we developed a Python-based tool to detect unsecured LLM endpoints. Our study uncovered over 1,100 exposed Ollama servers, with appro...