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CVE-2025-62609

7.5 · HIGH

MLX is an array framework for machine learning on Apple silicon.

Published Nov 21, 2025CWE-476EPSS 0.38%

Overview

CVE-2025-62609 is a high-severity vulnerability. It was published on November 21, 2025 and has a CVSS 3.1 base score of 7.5 (HIGH).

This vulnerability has a CVSS 3.1 base score of 7.5, rated HIGH. It can be exploited remotely over the network. No authentication or special privileges are required for exploitation.

Technical description

MLX is an array framework for machine learning on Apple silicon. Prior to version 0.29.4, there is a segmentation fault in mlx::core::load_gguf() when loading malicious GGUF files. Untrusted pointer from external gguflib library is dereferenced without validation, causing application crash. This issue has been patched in version 0.29.4.

Remediation

Check the references section for vendor advisories, patches, and mitigation guidance. If immediate patching is not possible, review the CVSS vector to understand the attack surface and apply compensating controls such as network segmentation or access restrictions.

Frequently asked questions

What is CVE-2025-62609?

CVE-2025-62609 is a high-severity vulnerability. It was published on November 21, 2025 and has a CVSS 3.1 base score of 7.5 (HIGH).

How severe is CVE-2025-62609?

This vulnerability has a CVSS 3.1 base score of 7.5, rated HIGH. It can be exploited remotely over the network. No authentication or special privileges are required for exploitation.

How do I fix or remediate CVE-2025-62609?

Check the references section for vendor advisories, patches, and mitigation guidance. If immediate patching is not possible, review the CVSS vector to understand the attack surface and apply compensating controls such as network segmentation or access restrictions.

How can CyberStrike help with CVE-2025-62609?

CyberStrike is an open-source, AI-augmented pentesting engine. Rather than flag a version number, it tests whether an issue like CVE-2025-62609 is actually reachable in your own applications and APIs, then verifies each finding before reporting it. That tells you if the weakness is a real, exploitable risk in your environment or a false positive you can safely deprioritize.

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