Cisco’s security team has identified more than 1,100 publicly exposed Ollama servers, with roughly one in five actively hosting models accessible without authentication, following a targeted scan using Shodan published on 1 September 2025 . The findings suggest a widespread pattern of weak defaults and misconfiguration in self-hosted AI infrastructure that could enable prompt injection, resource abuse, and model manipulation at scale . The exposure spans multiple regions and mirrors past eras of unsecured Elasticsearch clusters and open S3 buckets, but with live AI systems as the risk centre, raising immediate concerns for the AI and DevSecOps communities .

Context and background

The study focused on Ollama due to its rapid adoption for local LLM deployments and ease of setup, which can mask security baselines like authentication and network isolation if operators rely on defaults or informal guidance . Using Shodan’s indexed metadata rather than active probing, the team fingerprinted services via default ports (e.g., 11434 for Ollama) and banners, with Uvicorn headers serving as a secondary indicator for LLM app exposure . Within minutes, the scan surfaced over a thousand endpoints; approximately 214 responded with live models, including Mistral and LLaMA variants, confirming unauthenticated interaction potential .

Cisco reports geospatial clustering, with the United States, China and Germany among the top locations, underscoring that exposure patterns are global and cloud-distribution driven rather than niche or localised . Commentary from industry observers frames this as a predictable outcome of AI enthusiasm outpacing governance, echoing public warnings not to place private AI endpoints on the open internet without layered controls . The uniformity of OpenAI‑compatible APIs further simplifies attacker playbooks, allowing exploit attempts to scale across disparate platforms with minimal adaptation .

Looking forward

Cisco recommends immediate enforcement of authentication, role-based access control, segmentation behind firewalls or VPNs, and restricting default ports from public exposure, alongside rate limiting and monitoring to detect misuse patterns such as prompt injection or model hijacking . For organisations in the UK tech ecosystem, this aligns with ongoing efforts to professionalise AI operations, where security-by-design and programme governance are increasingly prerequisites for enterprise adoption and regulatory trust on 6 September 2025 .

Future work highlighted by the team includes integrating data from Censys and ZoomEye, adaptive fingerprinting, and active probes to catch non‑standard configurations, plus extending coverage to frameworks like vLLM and Triton . As more businesses realise the operational risks, expect a pivot towards secure-by-default tooling and opinionated deployment templates that optimise for least privilege and private-by-default connectivity whilst maintaining developer velocity .

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