CVE-2025-14926 is a remote code execution vulnerability classified under CWE-94 (Improper Control of Generation of Code) found in the Hugging Face Transformers library, version 4.57.0. The flaw exists in the convert_config function, which processes user-supplied configuration strings without adequate validation before executing them as Python code. This lack of sanitization allows an attacker to craft a malicious checkpoint configuration that, when converted by the vulnerable function, executes arbitrary code in the context of the current user. Exploitation requires user interaction, specifically the victim must initiate the conversion of the malicious checkpoint. The vulnerability has a CVSS 3.0 base score of 7.8, indicating high severity, with attack vector local (AV:L), low attack complexity (AC:L), no privileges required (PR:N), and user interaction required (UI:R). The impact on confidentiality, integrity, and availability is high, as arbitrary code execution can lead to data theft, system compromise, or denial of service. No patches or exploit code are currently publicly available, but the vulnerability was assigned and published by ZDI (ZDI-CAN-28251). This vulnerability is particularly critical for environments where Hugging Face Transformers is used to manage or deploy machine learning models, as malicious checkpoints could be introduced through supply chain attacks or insider threats.

For European organizations, the impact of CVE-2025-14926 is significant due to the widespread adoption of Hugging Face Transformers in AI research, development, and deployment. Successful exploitation could lead to unauthorized access to sensitive data, manipulation or destruction of machine learning models, and potential lateral movement within networks. This could disrupt AI-driven services, compromise intellectual property, and damage organizational reputation. Sectors such as finance, healthcare, automotive, and government, which increasingly rely on AI technologies, are particularly at risk. The requirement for user interaction limits mass exploitation but does not eliminate targeted attacks, especially in environments where model conversion is routine. The absence of known exploits in the wild provides a window for proactive mitigation, but the high severity score underscores the urgency of addressing this vulnerability.

1. Avoid converting or loading checkpoint files from untrusted or unknown sources to prevent triggering the vulnerability. 2. Implement strict input validation and sanitization for any user-supplied configuration data before processing. 3. Use sandboxing or containerization techniques to isolate the execution environment of model conversion processes, limiting potential damage from code execution. 4. Monitor and audit logs for unusual activities related to model conversion or checkpoint processing. 5. Stay updated with Hugging Face releases and apply patches promptly once available. 6. Educate users and developers about the risks of processing untrusted model files and enforce policies restricting such actions. 7. Consider employing application whitelisting or endpoint protection solutions that can detect and block suspicious code execution patterns related to this vulnerability.