CVE-2025-62608: Ml-explore MLX Buffer Overflow Vulnerability

CVE-2025-62608 Overview

CVE-2025-62608 is a heap buffer overflow vulnerability in the MLX machine learning framework for Apple silicon. The vulnerability exists in the mlx::core::load() function when parsing maliciously crafted NumPy .npy files. An attacker-controlled file can trigger a 13-byte out-of-bounds read, potentially leading to application crash or information disclosure.

MLX is an array framework designed specifically for machine learning workloads on Apple silicon, making this vulnerability particularly relevant to developers and researchers working with ML models on macOS systems.

Critical Impact

Attackers can craft malicious NumPy files to cause out-of-bounds memory reads, potentially leaking sensitive information or causing denial of service in ML applications.

Affected Products

• ml-explore mlx versions prior to 0.29.4

Discovery Timeline

• 2025-11-21 - CVE-2025-62608 published to NVD
• 2025-12-02 - Last updated in NVD database

Technical Details for CVE-2025-62608

Vulnerability Analysis

This heap buffer overflow vulnerability (CWE-122) occurs within the NumPy file parsing functionality of the MLX framework. When the mlx::core::load() function processes a malformed .npy file, insufficient boundary checking allows for a 13-byte out-of-bounds read operation on heap memory.

The vulnerability is network-exploitable, as malicious .npy files could be distributed through various channels including model repositories, shared datasets, or compromised ML pipelines. When an MLX-based application loads such a file, the out-of-bounds read can either cause the application to crash or potentially disclose sensitive memory contents.

Root Cause

The root cause stems from improper validation of data boundaries during NumPy file header parsing. The mlx::core::load() function fails to adequately verify the length of input data before performing read operations, allowing crafted headers to trigger memory access beyond allocated buffer boundaries.

Attack Vector

The attack requires an attacker to craft a malicious NumPy .npy file with a specially constructed header that exploits the boundary validation weakness. When a victim application using MLX processes this file—whether by loading a model, processing a dataset, or importing array data—the vulnerability is triggered.

The attack flow typically involves:

1. Attacker creates a malicious .npy file with crafted header data
2. The file is delivered to a target system (via download, shared storage, or ML pipeline)
3. An MLX-based application calls mlx::core::load() to process the file
4. The parser performs a 13-byte out-of-bounds read on heap memory
5. This results in either information disclosure or application crash

Detection Methods for CVE-2025-62608

Indicators of Compromise

• Unexpected crashes in MLX-based applications when loading .npy files
• Abnormal memory access patterns in applications utilizing the MLX framework
• Suspicious .npy files with malformed or unusually structured headers
• Application core dumps indicating heap corruption or out-of-bounds access

Detection Strategies

• Monitor application logs for segmentation faults or memory access violations in ML processing pipelines
• Implement file integrity checks on NumPy files before loading into MLX applications
• Use memory sanitizers (ASan, MSan) during development and testing to catch out-of-bounds reads
• Deploy runtime application self-protection (RASP) solutions to detect memory corruption attempts

Monitoring Recommendations

• Enable verbose logging in MLX-based applications to track file loading operations
• Monitor for unusual patterns in .npy file access across ML infrastructure
• Implement anomaly detection for application crashes related to file parsing
• Track and validate sources of NumPy files entering ML pipelines

How to Mitigate CVE-2025-62608

Immediate Actions Required

• Upgrade MLX to version 0.29.4 or later immediately
• Audit any .npy files from untrusted sources before processing
• Restrict file upload and import capabilities to trusted users and sources
• Implement input validation layers before passing files to MLX parsing functions

Patch Information

The vulnerability has been addressed in MLX version 0.29.4. Users should upgrade to this version or later to receive the security fix. For detailed information about the patch, refer to the GitHub Security Advisory GHSA-w6vg-jg77-2qg6 and related pull requests #1 and #2.

Workarounds

• Validate all NumPy files using independent parsing libraries before processing with MLX
• Implement sandboxing for ML workloads that process untrusted data files
• Use application-level memory protection mechanisms to contain potential exploitation
• Consider running MLX applications with reduced privileges to limit information disclosure impact

# Upgrade MLX to patched version
pip install --upgrade mlx>=0.29.4

# Verify installed version
pip show mlx | grep Version