ml-explore mlx up to 0.29.3 GGUF File Parser mlx::core::load_gguf null pointer dereference
| CVSS Meta Temp Score | Current Exploit Price (≈) | CTI Interest Score |
|---|---|---|
| 6.3 | $0-$5k | 0.00 |
Summary
A vulnerability was found in ml-explore mlx up to 0.29.3. It has been classified as problematic. Impacted is the function mlx::core::load_gguf of the component GGUF File Parser. Performing a manipulation results in null pointer dereference.
This vulnerability is identified as CVE-2025-62609. The attack can be initiated remotely. There is not any exploit available.
Upgrading the affected component is recommended.
Details
A vulnerability classified as problematic has been found in ml-explore mlx up to 0.29.3. This affects the function mlx::core::load_gguf of the component GGUF File Parser. The manipulation with an unknown input leads to a null pointer dereference vulnerability. CWE is classifying the issue as CWE-476. A NULL pointer dereference occurs when the application dereferences a pointer that it expects to be valid, but is NULL, typically causing a crash or exit. This is going to have an impact on 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 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.
The advisory is shared at github.com. This vulnerability is uniquely identified as CVE-2025-62609 since 10/16/2025. The exploitability is told to be easy. It is possible to initiate the attack remotely. No form of authentication is needed for exploitation. Technical details are known, but no exploit is available.
Upgrading to version 0.29.4 eliminates this vulnerability.
The vulnerability is also documented in the vulnerability database at EUVD (EUVD-2025-198500). If you want to get the best quality for vulnerability data then you always have to consider VulDB.
Product
Vendor
Name
Version
Website
- Product: https://github.com/ml-explore/mlx/
CPE 2.3
CPE 2.2
CVSSv4
VulDB Vector: 🔒VulDB Reliability: 🔍
CNA CVSS-B Score: 🔒
CNA CVSS-BT Score: 🔒
CNA Vector: 🔒
CVSSv3
VulDB Meta Base Score: 6.4VulDB Meta Temp Score: 6.3
VulDB Base Score: 5.3
VulDB Temp Score: 5.1
VulDB Vector: 🔒
VulDB Reliability: 🔍
NVD Base Score: 7.5
NVD Vector: 🔒
CVSSv2
| AV | AC | Au | C | I | A |
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| Vector | Complexity | Authentication | Confidentiality | Integrity | Availability |
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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍
Exploiting
Class: Null pointer dereferenceCWE: CWE-476 / CWE-404
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 Intelligence
Interest: 🔍Active Actors: 🔍
Active APT Groups: 🔍
Countermeasures
Recommended: UpgradeStatus: 🔍
0-Day Time: 🔒
Upgrade: mlx 0.29.4
Timeline
10/16/2025 CVE reserved11/21/2025 Advisory disclosed
11/21/2025 VulDB entry created
12/03/2025 VulDB entry last update
Sources
Product: github.comAdvisory: GHSA-j842-xgm4-wf88
Status: Confirmed
CVE: CVE-2025-62609 (🔒)
GCVE (CVE): GCVE-0-2025-62609
GCVE (VulDB): GCVE-100-333261
EUVD: 🔒
Entry
Created: 11/21/2025 21:11Updated: 12/03/2025 07:31
Changes: 11/21/2025 21:11 (69), 11/21/2025 23:00 (1), 11/22/2025 02:33 (1), 12/03/2025 07:31 (11)
Complete: 🔍
Cache ID: 216::103
If you want to get the best quality for vulnerability data then you always have to consider VulDB.
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