Hackers Exploit Ollama Model Uploads to Leak Server Data

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 the Exploit Works

To improve performance and efficiency, Ollama uses quantization, which reduces the numerical precision of an AI model.

However, researchers discovered an out-of-bounds memory vulnerability in the engine’s handling of GPT-Generated Unified Format (GGUF) files.

When an attacker uploads a specially designed GGUF file and triggers the quantization process, they can force the server to read beyond its safe memory limits.

This dangerous exploit is made possible by three combined factors:

• The engine blindly trusts the file metadata provided by the user without checking if it actually matches the provided data size.
• The software uses an unsafe memory operation in Go to create a data slice that extends far into the application’s heap.
• The system accidentally writes leaked memory to a new model layer, allowing the attacker to push the stolen data to an external server via Ollama’s registry API.

Because this vulnerability grants unauthorized access to the server’s core heap, the consequences can be devastating for organizations that host these models. Attackers can silently read and extract highly sensitive data that is temporarily stored in the system’s memory during normal operations.

This unwanted exposure can quickly lead to the theft of API keys, private user data, or proprietary intellectual property.

Furthermore, malicious actors could leverage this unauthorized access to gain broader control over the server, compromise the underlying network, and establish stealthy persistence without triggering standard security alarms.

The vulnerability was initially discovered by security researcher Jeremy Brown, who used AI-assisted vulnerability research methods to uncover it.

As of late April 2026, the CERT Coordination Center has been unable to reach the vendor, meaning no official patch is currently available.

Organizations and developers running Ollama must take immediate manual steps to secure their AI deployments from potential attacks.

To reduce the risk of exploitation, administrators should implement the following security measures:

• Restrict or turn off model upload functionality on all exposed servers immediately.
• Limit all Ollama deployments to local, isolated, or heavily trusted network environments.
• Only accept, download, and run AI models from verified and highly trusted sources.
• Apply strict network validation controls to prevent unauthorized external connections and data exfiltration.

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