CVE-2025-14924: 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. 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.
Description
Hugging Face Transformers megatron_gpt2 Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vulnerability allows remote attackers to execute arbitrary code on affected installations of Hugging Face Transformers. User interaction is required to exploit this vulnerability in that the target must visit a malicious page or open a malicious file. The specific flaw exists within the parsing of checkpoints. The issue results from the lack of proper validation of user-supplied data, which can result in deserialization of untrusted data. An attacker can leverage this vulnerability to execute code in the context of the current process. Was ZDI-CAN-27984.