CVE-2025-14929 is a deserialization of untrusted data vulnerability (CWE-502) found in the Hugging Face Transformers library, specifically within the X-CLIP checkpoint conversion functionality. The vulnerability stems from inadequate validation of user-supplied checkpoint data during parsing, which allows an attacker to craft malicious checkpoint files that, when deserialized by the vulnerable library, execute arbitrary code in the context of the running process. Exploitation 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, reflecting high severity due to its impact on confidentiality, integrity, and availability, combined with low attack complexity and no need for privileges. The flaw enables remote code execution (RCE), which can lead to full system compromise depending on the privileges of the affected process. Although no known exploits are currently reported in the wild, the widespread adoption of Hugging Face Transformers in AI and machine learning pipelines makes this vulnerability particularly concerning. The lack of patches at the time of reporting necessitates immediate mitigation steps to prevent exploitation. The vulnerability was assigned and published by the Zero Day Initiative (ZDI) under the identifier ZDI-CAN-28308.

For European organizations, the impact of CVE-2025-14929 is significant, especially for those leveraging Hugging Face Transformers in AI, natural language processing, and machine learning workflows. Successful exploitation can lead to arbitrary code execution, potentially resulting in data breaches, intellectual property theft, disruption of AI services, and lateral movement within networks. Confidentiality is at risk as attackers can access sensitive model data and training datasets. Integrity is compromised since attackers can alter model checkpoints or outputs, affecting AI decision-making processes. Availability can be disrupted if attackers execute destructive payloads or ransomware. Given the increasing reliance on AI technologies across sectors such as finance, healthcare, automotive, and government in Europe, this vulnerability poses a critical risk to operational continuity and data security. The requirement for user interaction somewhat limits mass exploitation but targeted attacks against AI researchers, data scientists, or automated pipelines remain a serious concern.

European organizations should immediately audit their use of Hugging Face Transformers, particularly versions matching the affected commit d1c6310d6a02481d48d81607cba7840be04580d1 or similar. Until official patches are released, organizations must avoid loading or converting checkpoint files from untrusted or unauthenticated sources. Implement strict input validation and sandboxing around any deserialization processes involving model checkpoints to contain potential exploitation. Employ network segmentation and endpoint detection to monitor for suspicious activity related to AI toolchains. Educate users and developers about the risks of opening untrusted files or clicking unknown links that could trigger deserialization. Consider using containerization or virtual machines to isolate AI workloads. Stay updated with vendor advisories and apply patches promptly once available. Additionally, review and harden access controls around AI infrastructure to minimize the impact of potential compromises.