ml-explore mlx up to 0.29.3 mlx::core::load heap-based overflow

CVSS Meta Temp Score
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CTI Interest Score
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8.0$0-$5k0.00

Summaryinfo

A vulnerability has been found in ml-explore mlx up to 0.29.3 and classified as critical. This vulnerability affects the function mlx::core::load. This manipulation causes heap-based overflow. The identification of this vulnerability is CVE-2025-62608. It is possible to initiate the attack remotely. There is no exploit available. The affected component should be upgraded.

Detailsinfo

A vulnerability was found in ml-explore mlx up to 0.29.3. It has been declared as critical. Affected by this vulnerability is the function mlx::core::load. The manipulation with an unknown input leads to a heap-based overflow vulnerability. The CWE definition for the vulnerability is CWE-122. A heap overflow condition is a buffer overflow, where the buffer that can be overwritten is allocated in the heap portion of memory, generally meaning that the buffer was allocated using a routine such as malloc(). As an impact it is known to affect confidentiality, integrity, and availability. The summary by CVE is:

MLX is an array framework for machine learning on Apple silicon. Prior to version 0.29.4, there is a heap buffer overflow in mlx::core::load() when parsing malicious NumPy .npy files. Attacker-controlled file causes 13-byte out-of-bounds read, leading to crash or information disclosure. This issue has been patched in version 0.29.4.

It is possible to read the advisory at github.com. This vulnerability is known as CVE-2025-62608 since 10/16/2025. The exploitation appears to be easy. The attack can be launched remotely. The exploitation doesn't need any form of authentication. Technical details of the vulnerability are known, but there is no available exploit.

Upgrading to version 0.29.4 eliminates this vulnerability. Applying a patch is able to eliminate this problem. The bugfix is ready for download at github.com. The best possible mitigation is suggested to be upgrading to the latest version.

The vulnerability is also documented in the vulnerability database at EUVD (EUVD-2025-198501). Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.

Productinfo

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Version

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CPE 2.3info

CPE 2.2info

CVSSv4info

VulDB Vector: 🔒
VulDB Reliability: 🔍

CNA CVSS-B Score: 🔒
CNA CVSS-BT Score: 🔒
CNA Vector: 🔒

CVSSv3info

VulDB Meta Base Score: 8.2
VulDB Meta Temp Score: 8.0

VulDB Base Score: 7.3
VulDB Temp Score: 7.0
VulDB Vector: 🔒
VulDB Reliability: 🔍

NVD Base Score: 9.1
NVD Vector: 🔒

CVSSv2info

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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍

Exploitinginfo

Class: Heap-based overflow
CWE: CWE-122 / CWE-119
CAPEC: 🔒
ATT&CK: 🔒

Physical: No
Local: No
Remote: Yes

Availability: 🔒
Status: Not defined

EPSS Score: 🔒
EPSS Percentile: 🔒

Price Prediction: 🔍
Current Price Estimation: 🔒

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Threat Intelligenceinfo

Interest: 🔍
Active Actors: 🔍
Active APT Groups: 🔍

Countermeasuresinfo

Recommended: Upgrade
Status: 🔍

0-Day Time: 🔒

Upgrade: mlx 0.29.4
Patch: github.com

Timelineinfo

10/16/2025 CVE reserved
11/21/2025 +36 days Advisory disclosed
11/21/2025 +0 days VulDB entry created
12/03/2025 +12 days VulDB entry last update

Sourcesinfo

Product: github.com

Advisory: GHSA-w6vg-jg77-2qg6
Status: Confirmed

CVE: CVE-2025-62608 (🔒)
GCVE (CVE): GCVE-0-2025-62608
GCVE (VulDB): GCVE-100-333259
EUVD: 🔒

Entryinfo

Created: 11/21/2025 21:10
Updated: 12/03/2025 07:31
Changes: 11/21/2025 21:10 (69), 11/21/2025 23:00 (1), 11/22/2025 02:33 (1), 12/03/2025 07:31 (11)
Complete: 🔍
Cache ID: 216::103

Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.

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