Hackers Exploit Ollama Model Uploads to Leak Server Data

Gbhackers •Divya • April 24, 2026

Cybersecurity researchers have uncovered a severe, unpatched vulnerability in Ollama, a popular open-source platform used for running large language models locally. Tracked as CVE-2026-5757, this critical flaw exists in Ollama’s model quantization engine. If exploited, it allows an unauthenticated attacker to steal sensitive server data by simply uploading a maliciously crafted AI model file. How […]

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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 Exploit Ollama Model Uploads to Leak Server Data

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Extracted Entities

Attack Types (1)

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