CVE-2025-14920 is a deserialization vulnerability classified under CWE-502 found in the Hugging Face Transformers library, specifically within the Perceiver model's handling of model files. The vulnerability stems from the lack of proper validation of user-supplied data during the deserialization process, which is a common vector for remote code execution (RCE) attacks. When a maliciously crafted model file is parsed, it can trigger the execution of arbitrary code with the privileges of the user running the application. The attack requires user interaction, such as opening a malicious file or visiting a malicious webpage that triggers the loading of the compromised model. The vulnerability has a CVSS 3.0 base score of 7.8, reflecting high impact on confidentiality, integrity, and availability, with low attack complexity and no privileges required, but user interaction is necessary. This flaw is particularly critical in environments where Hugging Face Transformers are used to load models from untrusted or external sources, as it can lead to full system compromise. No public patches are currently listed, and no known exploits have been observed in the wild, but the risk remains significant due to the widespread use of Hugging Face in AI/ML applications.

For European organizations, the impact of CVE-2025-14920 can be substantial, especially for those leveraging AI and machine learning frameworks in production or research environments. Successful exploitation could lead to unauthorized code execution, data theft, manipulation of AI model outputs, or disruption of AI-driven services. This could compromise sensitive data, intellectual property, and critical decision-making processes reliant on AI models. Industries such as finance, healthcare, automotive, and telecommunications, which increasingly integrate AI technologies, may face operational disruptions and reputational damage. Additionally, organizations using Hugging Face Transformers in cloud or hybrid environments could see lateral movement by attackers if the vulnerability is exploited. The requirement for user interaction somewhat limits automated mass exploitation but does not eliminate targeted attacks, especially via phishing or supply chain vectors. The absence of known exploits currently provides a window for proactive mitigation.

European organizations should implement strict controls around the sourcing and validation of AI model files, ensuring they come from trusted and verified repositories. Employ sandboxing or containerization techniques when loading models to isolate potential malicious code execution. Regularly update Hugging Face Transformers to the latest versions once patches are released. Implement network-level protections to detect and block suspicious traffic related to model file downloads or interactions. Educate users about the risks of opening untrusted files or clicking unknown links that could trigger model loading. Employ application whitelisting and endpoint detection and response (EDR) solutions to monitor for anomalous behavior indicative of exploitation attempts. Consider integrating integrity checks or cryptographic signatures for model files to prevent tampering. Finally, maintain an incident response plan tailored to AI/ML infrastructure compromises.