Critical Ollama Flaw 'Bleeding Llama' Exposes Process Memory on 300,000+ Servers

A critical security vulnerability in Ollama could allow remote, unauthenticated attackers to leak the entire process memory of affected servers, according to researchers at Cyera. The out-of-bounds read flaw, tracked as CVE-2026-7482, carries a CVSS severity score of 9.1 and has been codenamed "Bleeding Llama." With an estimated 300,000-plus servers globally running Ollama deployments, the vulnerability presents a significant exposure risk for organizations using the popular AI model serving platform.

The flaw enables attackers to read arbitrary memory from the Ollama process without requiring authentication or user interaction. Successful exploitation could expose sensitive data contained within process memory at the time of the attack. The remote nature of the vulnerability, combined with the lack of authentication requirements, elevates its severity and potential appeal to threat actors scanning for accessible targets across enterprise environments.

Ollama has become widely adopted for deploying and running large language models locally, making this disclosure particularly relevant for organizations integrating AI workloads into their infrastructure. Security teams running Ollama instances should immediately assess their exposure, particularly for systems accessible from external networks, and apply available patches. The incident adds to growing concerns about the security posture of AI tooling as adoption accelerates across industries handling sensitive data and proprietary model assets.