Finding thousands of exposed Ollama instances using Shodan
7 days ago
- #LLM Security
- #Ollama Vulnerabilities
- #AI Deployment Risks
- The rapid deployment of large language models (LLMs) has introduced significant security vulnerabilities due to misconfigurations and inadequate access controls.
- A study identified over 1,100 exposed Ollama servers, with approximately 20% actively hosting models susceptible to unauthorized access.
- Common vulnerabilities include unauthorized API access, model extraction attacks, jailbreaking, resource hijacking, and backdoor injection.
- The methodology involved using Shodan to detect exposed Ollama servers and assess their security configurations.
- Findings revealed that many servers lacked authentication, with the majority hosted in the US, China, and Germany.
- Mitigation strategies include enforcing authentication, network segmentation, rate limiting, and continuous monitoring.
- The study highlights the urgent need for standardized security practices in LLM deployments.