Unauthenticated Remote Information Disclosure in Ollama Model Quantization Engine

Executive Summary

CVE-2026-5757 is an unauthenticated remote information disclosure vulnerability in Ollama's model quantization engine.

An attacker can exploit this vulnerability by uploading a specially crafted GGUF file to the model upload interface, which lacks proper bounds checking.

Due to the quantization engine trusting tensor metadata from the GGUF file header without validation and using Go's unsafe.Slice, the attacker can cause out-of-bounds memory access into the server's heap memory.

This allows the attacker to read and exfiltrate sensitive data from the server's heap memory.

The leaked data is then processed and written into a new model layer, which can be pushed to an attacker-controlled server via Ollama's registry API, effectively exfiltrating the memory contents.

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Detection Guidance

This vulnerability involves an attacker uploading a specially crafted GGUF file to the model upload interface to read and exfiltrate heap memory. Detection would focus on monitoring and analyzing uploads to the model quantization engine, especially GGUF files.

Suggested detection approaches include:

• Monitoring network traffic for unusual or unauthorized uploads to the model upload interface.
• Inspecting logs of the model quantization engine for suspicious GGUF file uploads or unexpected model layer creations.
• Using file integrity monitoring or scanning tools to detect anomalous GGUF files.

Specific commands are not provided in the available resources.

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Impact Analysis

This vulnerability can lead to exposure of sensitive data stored in the server's heap memory.

An attacker could use the disclosed information to further compromise the system.

It also enables stealthy persistence, allowing attackers to maintain unauthorized access without detection.

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Compliance Impact

The vulnerability allows unauthenticated remote attackers to read and exfiltrate heap memory from the server, potentially exposing sensitive data. This exposure of sensitive data could lead to non-compliance with data protection regulations such as GDPR and HIPAA, which require the protection of personal and sensitive information against unauthorized access and disclosure.

Since the vulnerability can result in sensitive data exposure and further system compromise, organizations using the affected software may face increased risk of violating these standards if the vulnerability is exploited.

Interim mitigations such as restricting model upload functionality, limiting deployments to trusted environments, and accepting models only from verified sources are recommended to reduce the risk and help maintain compliance.

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Mitigation Strategies

Immediate mitigation steps include restricting or disabling access to the model upload functionality, especially in untrusted environments.

• Limit deployments of the Ollama model quantization engine to trusted networks only.
• If model uploads are necessary, ensure that only trusted sources are allowed to upload models.
• Implement additional validation controls on uploaded GGUF files to prevent out-of-bounds memory access.

As of the report, no patch is available and the vendor has not been reached for coordination.

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