CVE-2025-14929: Hugging Face Transformers · Deserialization of Untrusted Data
Affects Hugging Face TransformersSource CVE Database V5
Not known to be exploited · CVSS 7.8 High.
Data as of 26 Oct 2026, 06:26 UTC
- EPSS
- 0.3% chance of exploitation in 30 days – higher than 30% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.0/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HExploitation evidence
Disclosed: evidence found.
CVE published 23 Dec 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-26.
What to do
AI-suggested
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.
Description
CVE-2025-14929 is a high-severity vulnerability in Hugging Face Transformers affecting the X-CLIP checkpoint conversion process. It arises from improper validation of user-supplied data during deserialization, enabling remote code execution if a user opens a malicious file or visits a crafted webpage. Exploitation requires user interaction but no prior authentication. The vulnerability impacts confidentiality, integrity, and availability by allowing arbitrary code execution within the affected process context. No known exploits are currently in the wild. European organizations using vulnerable versions of Hugging Face Transformers, especially in AI/ML workflows, face significant risk. Mitigation involves avoiding untrusted checkpoint files, applying patches when available, and implementing strict input validation and sandboxing. Countries with strong AI sectors and extensive use of Hugging Face tools, such as Germany, France, the UK, and the Netherlands, are most likely affected.