Roughly 300,000 Ollama deployments are prone to sensitive information theft through a remotely exploitable, unauthenticated critical vulnerability, Cyera warns. Ollama is an open source solution for running LLMs on local machines and is highly popular among organizations as a self-hosted AI inference engine. A heap out-of-bounds read issue in Ollama could be exploited to access sensitive information stored on the heap, including prompts, messages, and environment variables, including API keys, tokens, and secrets, Cyera says. Tracked as CVE-2026-7482 (CVSS score of 9.3) and dubbed Bleeding Llama, the bug affects the GGUF model loader, which accepts an attacker-supplied GGUF file containing a declared tensor offset and size larger than the file’s length. When processing the file, the…