CVE-2025-14921: 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 25 Oct 2026, 19:02 UTC
- EPSS
- 0.3% chance of exploitation in 30 days – higher than 25% 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 2026-10-25.
What to do
AI-suggested
1. Immediately restrict the sources of Transformer-XL model files to trusted repositories and verified providers to minimize exposure to malicious files. 2. Implement strict validation and integrity checks (e.g., cryptographic signatures or hashes) on all model files before loading them into the Transformers library. 3. Educate users and developers about the risks of opening untrusted model files or visiting unverified URLs that could trigger model loading. 4. Monitor official Hugging Face channels for patches or updates addressing this vulnerability and apply them promptly once available. 5. Employ runtime application self-protection (RASP) or endpoint detection and response (EDR) solutions to detect anomalous behaviors indicative of exploitation attempts. 6. Isolate AI workloads in sandboxed or containerized environments to limit the impact of potential code execution. 7. Review and harden user privileges to minimize the damage from code execution under user context. 8. Conduct regular security audits of AI pipelines and dependencies to identify and remediate similar deserialization risks.
Description
CVE-2025-14921 is a high-severity remote code execution vulnerability in the Hugging Face Transformers library, specifically affecting the Transformer-XL model deserialization process. The flaw arises from improper validation of user-supplied model files, enabling deserialization of untrusted data. Exploitation requires user interaction, such as visiting a malicious webpage or opening a crafted file, allowing attackers to execute arbitrary code with the privileges of the current user. The vulnerability impacts confidentiality, integrity, and availability of affected systems. No known exploits are currently reported in the wild. European organizations using vulnerable versions of Hugging Face Transformers are at risk, especially those integrating Transformer-XL models in their AI workflows. Mitigation involves strict validation of model files, restricting model sources, and applying updates once patches become available. Countries with significant AI development and adoption, such as Germany, France, and the UK, are more likely to be affected due to higher usage of these tools.