A critical vulnerability, CVE-2026-7482, has been discovered in Ollama, a popular framework for running AI models on local hardware, posing a significant risk of sensitive information leaks to over 300,000 internet-exposed servers. The flaw, known as Bleeding Llama, arises from an out-of-bounds heap read in Ollama's model quantization pipeline, allowing unauthorized access to sensitive data. This vulnerability not only affects internet-exposed servers but also those on local LANs if access is not properly restricted. The disclosure of CVE-2026-7482 expands the active attack surface, making it essential for practitioners to prioritize mitigation based on their exposure and exploitation evidence1. The vulnerability highlights the dangers of AI frameworks with unrestricted access, emphasizing the need for secure configuration and access controls to prevent sensitive information leaks. This matters to practitioners as it necessitates immediate attention to patch and restrict access to vulnerable Ollama instances to prevent potential data breaches.