CVE-2025-14920: 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, 14:05 UTC
- EPSS
- 0.3% chance of exploitation in 30 days – higher than 29% 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 26 Oct 2026.
What to do
AI-suggested
- Immediately restrict the loading of model files to trusted, verified sources only, avoiding any untrusted or external model files.
- Implement strict input validation and sanitization for all model files before deserialization, employing allowlists or schema validation where possible.
- Isolate the execution environment for model loading, such as using sandboxing or containerization, to limit the impact of potential code execution.
- Educate users about the risks of opening untrusted model files or visiting suspicious websites that could trigger malicious model loading.
- Monitor and audit model loading activities and system behavior for anomalies indicative of exploitation attempts.
- Apply security updates and patches from Hugging Face promptly once they become available.
- Consider disabling or limiting features that automatically load or parse model files from external sources.
- Employ endpoint protection solutions capable of detecting suspicious deserialization or code execution behaviors.
- Review and harden access controls around AI/ML infrastructure to minimize the privileges of users and processes handling model files.
- Establish incident response plans specific to AI/ML infrastructure compromise scenarios.
Description
CVE-2025-14920 is a high-severity remote code execution vulnerability in the Hugging Face Transformers library, specifically affecting the Perceiver model deserialization process. The flaw arises from improper validation of user-supplied model files, allowing deserialization of untrusted data. Exploitation requires user interaction, such as opening a malicious file or visiting a crafted webpage. Successful exploitation enables attackers to execute arbitrary code with the privileges of the current user. Although no known exploits are reported in the wild, the vulnerability poses significant risks to confidentiality, integrity, and availability. The vulnerability has a CVSS score of 7.8, reflecting its high impact and relatively low complexity to exploit. European organizations using Hugging Face Transformers in AI/ML workflows, especially those processing untrusted or external model files, are at risk. Mitigation involves strict validation of model inputs, restricting model file sources, and applying patches once available. Countries with strong AI development sectors and extensive use of open-source ML frameworks, such as Germany, France, and the UK, are most likely affected.