Hackers Can Exploit Ollama Model Uploads to Leak Sensitive Server Data

Cybersecuritynews •Abinaya • April 24, 2026

A critical, unpatched vulnerability has been discovered in Ollama, a widely used open-source platform for running Large Language Models locally. Tracked as CVE-2026-5757, this severe memory leak allows unauthenticated remote attackers to extract sensitive data directly from a server’s heap. Discovered by security researcher Jeremy Brown via AI-assisted vulnerability research and disclosed publicly on April […]

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This article is one source in a clustered incident — the cluster page carries the summary, timeline and every other outlet covering it.

Critical Vulnerability in Ollama Allows Data Exfiltration via Model Uploads A severe vulnerability, tracked as CVE-2026-5757, has been identified in Ollama's model quantization engine, which enables unauthenticated attackers to exploit the model upload interface. By uploading a specially crafted GGUF file, attackers can read and exfiltrate sensitive heap memory from the server, potentially leading to unauthorized access and broader system compromise. This vulnerability affects Ollama, an open-source tool for running large language models locally on macOS, Windows, and Linux. Currently, there is no patch available, and the vendor has not been reached for coordination. Security experts recommend restricting access to the model upload functionality, especially in untrusted environments. The vulnerability was reported by Jeremy Brown through AI-assisted research. The risk is heightened due to the lack of immediate remediation options and the potential for significant data exposure. 6 sources 70

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Hackers Can Exploit Ollama Model Uploads to Leak Sensitive Server Data

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