CVE-2025-14924 is a remote code execution vulnerability classified under CWE-502 (Deserialization of Untrusted Data) found in the Hugging Face Transformers library, specifically in the megatron_gpt2 model checkpoint parsing functionality. The vulnerability stems from the library's failure to properly validate user-supplied data when deserializing model checkpoints, which can be crafted maliciously to execute arbitrary code within the context of the running process. An attacker must convince a user to interact with a malicious checkpoint file or visit a malicious page that triggers the deserialization process. The vulnerability does not require any privileges or authentication but does require user interaction, making social engineering a likely attack vector. The CVSS v3.0 score is 7.8, indicating high severity, with impacts on confidentiality, integrity, and availability. The attack vector is local (AV:L), meaning the attacker needs to have local access or the user must perform an action locally, but the attack complexity is low and no privileges are required. No patches have been linked yet, and no known exploits are reported in the wild as of the publication date. This vulnerability is critical for environments where Hugging Face Transformers are used to load or parse model checkpoints, especially in AI development and deployment pipelines where untrusted data sources may be present.

For European organizations, this vulnerability poses a significant risk, especially those involved in AI research, development, and deployment using Hugging Face Transformers. Successful exploitation could lead to arbitrary code execution, resulting in full system compromise, data theft, or disruption of AI services. Confidentiality of sensitive data processed by AI models could be breached, integrity of AI workflows compromised, and availability of AI services disrupted. Industries such as finance, healthcare, automotive, and government agencies leveraging AI technologies are particularly vulnerable. The requirement for user interaction means phishing or social engineering campaigns could be used to trigger exploitation. Given the increasing adoption of AI frameworks in Europe, the vulnerability could be leveraged to target intellectual property or critical infrastructure. The lack of known exploits currently provides a window for proactive mitigation, but the high severity score underscores the urgency of addressing this issue.

1. Avoid loading or parsing model checkpoints from untrusted or unauthenticated sources. 2. Implement strict input validation and sanitization for any checkpoint files before deserialization. 3. Use sandboxing or containerization to isolate the execution environment of AI model loading processes to limit potential damage from exploitation. 4. Monitor and restrict user interactions that involve opening files or visiting URLs that could trigger deserialization. 5. Employ endpoint protection solutions capable of detecting anomalous behavior related to code execution in AI frameworks. 6. Stay updated with Hugging Face releases and apply patches promptly once available. 7. Educate users on the risks of opening untrusted files or links related to AI models. 8. Consider disabling or restricting the use of megatron_gpt2 or other vulnerable components until a patch is released. 9. Conduct regular security assessments of AI pipelines to identify and remediate deserialization risks. 10. Implement network segmentation to limit lateral movement if exploitation occurs.