Finding thousands of exposed Ollama instances using Shodan

Summary

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 approximately 20% actively hosting models susceptible to unauthorized access. These findings highlight the urgent need for security baselines in LLM deployments and provide a practical foundation for future research into LLM threat surface monitoring. Introduction The integration of large language models (LLMs) into diverse applications has surged in recent years, driven by their advanced capabilities in natural language understanding and generation. Widely adopted platforms such as ChatGPT, Grok, and DeepSeek have contributed to the mainstream visibility of LLMs, while open-source frameworks like Ollama and Hugging Face have significantly lowered the barrier to entry for deploying these models in custom environments. This has led to widespread adoption by both organizations and individuals of a broad range of tasks, including content generation, customer support, data analysis, and software development. Despite their growing utility, the pace of LLM adoption has often outstripped the development and implementation of appropriate security practices. Many self-hosted or locally deployed LLM solutions are brought online without adequate hardening, frequently exposing endpoints due to default configurations, weak or absent authentication, and insufficient network isolation. These vulnerabilities are not only a byproduct of poor deployment hygiene but are also symptomatic of an ecosystem that has largely prioritized accessibility and performance over security. As a result, improperly ...