Listen to this Post
The vulnerability CVE-2025-62609 in the MLX framework involves a segmentation fault in the `mlx::core::load_gguf()` function when processing malicious GGUF files. MLX, an array framework for machine learning on Apple silicon, uses an external gguflib library to parse GGUF format files. During loading, the function receives pointers from this library to data structures within the file. Without proper validation, these untrusted pointers are dereferenced directly. If a crafted GGUF file contains manipulated pointer values pointing to invalid or unauthorized memory addresses, dereferencing causes a segmentation fault. This occurs because the code assumes pointers from the external library are always valid, lacking bounds checks or null checks. An attacker can exploit this by providing a specially designed GGUF file, leading to abrupt application crash. The issue is triggered during the file loading phase, typically via API calls that read GGUF models. The crash results in denial of service, disrupting ML workflows. The vulnerability is remotely exploitable if the application loads files from network sources, with low attack complexity. The patch in version 0.29.4 adds pointer validation to ensure safe dereferencing.
Platform: MLX array framework
Version: Prior to 0.29.4
Vulnerability: Segmentation fault load_gguf
Severity: MEDIUM CVSS 5.5
Date: 11/21/2025
Prediction: Patched version 0.29.4
What Undercode Say:
Analytics:
bash commands and codes related to the blog
- Install MLX: `pip install mlx`
– Check version: `pip show mlx`
– Load GGUF file: `import mlx.core as mx; mx.load_gguf(‘file.gguf’)`
– Vulnerable code snippet: `mlx::core::load_gguf(const std::string& path)`
– Test crash: `python -c “import mlx.core as mx; mx.load_gguf(‘malicious.gguf’)”`
How Exploit:
Craft malicious GGUF file with invalid pointer values.
Trigger via loading file in MLX.
Causes segmentation fault crash.
Protection from this CVE:
Update to version 0.29.4.
Validate external library pointers.
Use trusted GGUF sources.
Impact:
Application crash denial-of-service.
Disrupts machine learning workflows.
Low confidentiality integrity loss.
🎯Let’s Practice Exploiting & Learn Patching For Free:
Sources:
Reported By: nvd.nist.gov
Extra Source Hub:
Undercode
