Researchers disclosed a critical out-of-bounds read vulnerability in Ollama that exposes entire process memory to remote attackers without authentication. The flaw, tracked as CVE-2026-7482, carries a CVSS score of 9.1 and affects over 300,000 servers worldwide.
Cyera researchers named the vulnerability Bleeding Llama. An unauthenticated attacker can exploit the memory leak to extract sensitive data from the Ollama process, including credentials, API keys, and other confidential information stored in RAM during runtime.
Ollama is a popular open-source framework for running large language models locally. The tool has gained adoption among developers and organizations deploying LLMs on-premises or in isolated environments. The widespread deployment increases the attack surface for this vulnerability.
The out-of-bounds read occurs when the application attempts to access memory beyond allocated buffer boundaries. This programming error allows attackers to read adjacent memory regions without proper access controls. The remoteness of the exploit and lack of authentication requirements make this a particularly dangerous flaw.
Organizations operating Ollama instances should prioritize patching immediately. The vulnerability poses direct risk to any system storing sensitive data in process memory during model inference operations. Companies using Ollama in production environments for customer-facing or internal applications face exposure of API credentials, authentication tokens, and potentially training data.
Users should update to patched versions as soon as they become available. In the interim, isolating Ollama instances on trusted networks and restricting access to authorized users only reduces immediate exposure. Organizations should audit Ollama deployments across their infrastructure to understand the scope of affected systems.
Cyera's disclosure demonstrates the security risks inherent in the rapidly expanding LLM ecosystem. As organizations increasingly integrate AI frameworks into production systems, security vulnerabilities in these tools cascade across thousands of deployments globally. The high CVSS