CVE-2025-14921 is a deserialization vulnerability classified under CWE-502 affecting the Hugging Face Transformers library, specifically the Transformer-XL model. The vulnerability stems from inadequate validation of user-supplied data during the parsing of model files, which allows an attacker to craft malicious serialized data that, when deserialized by the library, can execute arbitrary code remotely. The flaw requires user interaction, such as opening a malicious file or visiting a malicious webpage that triggers the deserialization process. 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), user interaction required (UI:R), unchanged scope (S:U), and high impact on confidentiality, integrity, and availability (C:H/I:H/A:H). This means that while the attacker must convince a user to perform an action, no prior access or elevated privileges are needed, and successful exploitation can lead to full system compromise under the current user context. The vulnerability is particularly critical in environments where Hugging Face Transformers are used to load or process untrusted model files or data. No patches or exploits in the wild are currently reported, but the risk remains significant given the widespread use of Hugging Face in AI and ML applications. The vulnerability was assigned by ZDI (ZDI-CAN-25424) and published on December 23, 2025.

For European organizations, the impact of CVE-2025-14921 can be severe, especially for those leveraging AI and machine learning frameworks that incorporate Hugging Face Transformers. Successful exploitation could lead to remote code execution, allowing attackers to compromise sensitive data, manipulate AI model outputs, or disrupt AI-driven services. This could affect sectors such as finance, healthcare, automotive, and research institutions that rely heavily on AI models for decision-making and automation. Confidentiality breaches could expose proprietary models or personal data, while integrity violations could lead to corrupted AI outputs, undermining trust in automated systems. Availability impacts could result in denial of service or ransomware deployment. Given the user interaction requirement, phishing or social engineering campaigns could be used to trigger exploitation, increasing the risk in environments with less stringent user awareness training. The lack of known exploits in the wild provides a window for proactive defense, but the high severity score underscores the urgency of mitigation.

To mitigate CVE-2025-14921, European organizations should implement the following specific measures: 1) Restrict the sources of model files to trusted repositories and verify integrity using cryptographic hashes or signatures before loading them into Hugging Face Transformers. 2) Employ sandboxing or containerization to isolate the execution environment of AI models, limiting the impact of potential code execution. 3) Enhance user awareness training to reduce the risk of social engineering attacks that could lead to user interaction with malicious files or links. 4) Monitor and log deserialization activities and model loading processes to detect anomalous behavior indicative of exploitation attempts. 5) Apply strict access controls and least privilege principles to the environments running Hugging Face Transformers to minimize damage if exploitation occurs. 6) Stay updated with vendor advisories and apply patches or updates as soon as they become available. 7) Consider implementing application whitelisting to prevent unauthorized code execution. 8) Use network segmentation to isolate AI/ML infrastructure from critical business systems. These targeted actions go beyond generic advice and address the specific attack vector and exploitation conditions of this vulnerability.