VU#518910: Ollama GGUF Quantization Remote Memory Leak
Overview
Ollama’s model quantization engine contains a vulnerability that allows an attacker with access to the model upload interface to read and potentially exfiltrate heap memory from the server. This issue may lead to unintended behavior, including unauthorized access to sensitive data and, in some cases, broader system compromise.
Description
Ollama is an open-source tool designed to run large language models (LLMs) locally on personal systems, including macOS, Windows, and Linux. Ollama supports model quantization, an optimization technique that reduces the numerical precision used in models to improve performance and efficiency.
An out-of-bounds heap read/write vulnerability has been identified in Ollama’s model processing engine. By uploading a specially crafted GPT-Generated Unified Format (GGUF) file and triggering the quantization process, an attacker can cause the server to read beyond intended memory boundaries and write the leaked data into a new model layer.
CVE-2026-5757: Unauthenticated remote information disclosure vulnerability in Ollama’s model quantization engine allows an attacker to read and exfiltrate the server’s heap memory, potentially leading to sensitive data exposure, further compromise, and stealthy persistence.
The vulnerability is caused by three combined factors:
- No Bounds Checking: The quantization engine trusts tensor metadata (like element count) from the user-supplied GGUF file header without verifying it against the actual size of the provided data.
- Unsafe Memory Access: Go’s
unsafe.Sliceis used to create a memory slice based on the attacker-controlled element count, which can extend far beyond the legitimate data buffer and into the application’s heap. - Data Exfiltration Path: The out-of-bounds heap data is inadvertently processed and written into a new model layer. Ollama’s registry API can then be used to “push” this layer to an attacker-controlled server, effectively exfiltrating the leaked memory.
Impact
An attacker with access to the model upload interface can exploit this vulnerability to read from or write to heap memory. This may result in exposure of sensitive data, data exfiltration, and potentially full system compromise.
Solution
Unfortunately, we were unable to reach the vendor to coordinate this vulnerability, and a patch is not yet available to address this vulnerability. The underlying issue should be addressed by implementing proper bounds checking to ensure that tensor metadata is validated against the actual size of the provided data before any memory operations are performed.
As an interim mitigation, access to the model upload functionality should be restricted or disabled, particularly in environments exposed to untrusted users or networks. Deployments should be limited to local or otherwise trusted network environments where possible. If model uploads are required for operational reasons, only models from trusted and verifiable sources should be accepted, and appropriate validation controls should be applied to reduce risk.
Acknowledgements
Thanks to the reporter Jeremy Brown, who detected the vulnerability through AI-assisted vulnerability research. This document was written by Timur Snoke.
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