CVE-2025-62609 is a vulnerability identified in the ml-explore mlx framework, an array processing library designed for machine learning workloads on Apple silicon platforms. The vulnerability stems from a NULL pointer dereference (CWE-476) in the mlx::core::load_gguf() function, which is responsible for loading GGUF files—a data format used within the framework. Specifically, when mlx versions prior to 0.29.4 load a maliciously crafted GGUF file, an untrusted pointer originating from the external gguflib library is dereferenced without proper validation or null checks. This leads to a segmentation fault, causing the application to crash. The vulnerability is remotely exploitable without any authentication or user interaction, as the attack vector involves supplying a malicious GGUF file to the vulnerable function. The CVSS v4.0 base score is 5.5 (medium severity), reflecting the limited impact confined to denial of service rather than code execution or data compromise. No known exploits are currently reported in the wild. The issue was reserved in October 2025 and published in November 2025, with a patch available in mlx version 0.29.4 that adds necessary pointer validation to prevent the crash. This vulnerability primarily affects applications and services that utilize the mlx framework for machine learning tasks on Apple silicon hardware and load GGUF files from untrusted sources.

For European organizations, the primary impact of CVE-2025-62609 is the potential for denial of service (DoS) conditions in applications relying on the mlx framework for machine learning on Apple silicon devices. This could disrupt critical ML workloads, data processing pipelines, or AI-driven services, leading to operational downtime and productivity loss. Although the vulnerability does not allow unauthorized data access or code execution, repeated exploitation could degrade service availability and reliability. Organizations in sectors such as technology, research, finance, and healthcare that leverage Apple silicon-based ML solutions may experience interruptions if malicious GGUF files are introduced, either accidentally or through targeted attacks. The absence of authentication or user interaction requirements increases the risk of automated exploitation in environments where GGUF files are ingested from external or untrusted sources. However, the impact is limited to applications using vulnerable mlx versions and loading GGUF files, thus the scope is somewhat contained. Prompt patching is essential to maintain service continuity and prevent exploitation.

European organizations should immediately upgrade all instances of the mlx framework to version 0.29.4 or later, where the NULL pointer dereference vulnerability has been patched. They should audit their ML workflows to identify any components that load GGUF files, especially those accepting files from external or untrusted sources, and implement strict input validation and file integrity checks. Employ sandboxing or containerization for ML applications to contain potential crashes and prevent cascading failures. Monitoring and alerting should be enhanced to detect abnormal application crashes or segmentation faults related to mlx processes. Network-level controls can be used to restrict the sources of GGUF files to trusted repositories or internal systems. Additionally, organizations should review dependency management practices to ensure timely updates of third-party libraries like mlx and gguflib. Finally, incorporating fuzz testing and static analysis in the development lifecycle can help detect similar pointer validation issues proactively.