CVE-2025-14924 is a deserialization vulnerability classified under CWE-502 found in the Hugging Face Transformers library, specifically in the megatron_gpt2 model checkpoint parsing functionality. The vulnerability stems from the lack of proper validation of user-supplied checkpoint data, which is deserialized without sufficient checks, allowing an attacker to inject malicious serialized objects. When a user interacts with a malicious checkpoint—either by visiting a crafted webpage or opening a malicious file—the deserialization process can lead to arbitrary code execution within the context of the running process. The vulnerability requires user interaction (UI:R) but does not require any privileges or authentication, making it accessible to remote attackers who can trick users into loading malicious data. The CVSS v3.0 score of 7.8 reflects high severity due to the potential for full compromise of confidentiality, integrity, and availability of affected systems. The issue is particularly critical in environments where Hugging Face Transformers are used for AI/ML model deployment, as attackers could execute code to manipulate model behavior, exfiltrate sensitive data, or disrupt services. No patches are currently linked, and no known exploits have been reported in the wild, but the vulnerability was publicly disclosed on December 23, 2025, and assigned by ZDI as ZDI-CAN-27984.

For European organizations, the impact of this vulnerability is significant, especially for those leveraging Hugging Face Transformers in AI research, development, and production environments. Successful exploitation could lead to remote code execution, allowing attackers to compromise machine learning infrastructure, steal intellectual property, manipulate AI model outputs, or disrupt critical AI-driven services. This could affect sectors such as finance, healthcare, automotive, and telecommunications, where AI models are increasingly integrated. The requirement for user interaction limits automated exploitation but does not eliminate risk, as social engineering or malicious content delivery can trigger the vulnerability. Additionally, compromised AI systems could undermine trust in AI outputs and cause regulatory and compliance issues under GDPR and other European data protection laws. The lack of available patches increases exposure time, emphasizing the need for immediate mitigation.

European organizations should implement the following specific mitigations: 1) Avoid loading untrusted or unauthenticated model checkpoints or serialized data within Hugging Face Transformers environments. 2) Employ strict input validation and integrity checks (e.g., cryptographic signatures) on all checkpoint files before deserialization. 3) Use sandboxing or containerization to isolate AI model execution environments, limiting the impact of potential code execution. 4) Monitor user interactions that involve loading external model data and educate users on the risks of opening untrusted files or links. 5) Keep abreast of updates from Hugging Face and apply patches promptly once available. 6) Implement network-level controls to restrict access to internal AI infrastructure and detect anomalous behavior indicative of exploitation attempts. 7) Conduct regular security assessments and penetration testing focused on AI/ML pipelines to identify and remediate deserialization and related vulnerabilities.