CVE-2025-6051: huggingface/transformers · ReDoS
Affects huggingface/transformersSource CVE Database V5
Not known to be exploited · CVSS 5.3 Medium · fixed in 4.53.0 : update huggingface/transformers when convenient.
Data as of 25 Oct 2026, 19:02 UTC
- EPSS
- 0.3% chance of exploitation in 30 days – higher than 31% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
- Patch
- Available – fixed in 4.53.0
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:LExploitation evidence
Disclosed: evidence found.
CVE published 14 Sep 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
Affected software
| Product | Affected | Fixed in |
|---|---|---|
| huggingface huggingface/transformers | Affected
| Fixed in– |
AI-suggested
European organizations should immediately upgrade any deployments of the Hugging Face Transformers library to version 4.53.0 or later, where the vulnerability is fixed. If upgrading is not immediately feasible, implement input validation and sanitization to detect and reject unusually long numeric sequences before they reach the vulnerable `normalize_numbers()` method. Rate limiting and anomaly detection on API endpoints processing text normalization can help mitigate exploitation attempts. Monitoring CPU usage and setting resource limits on services using the library can prevent system-wide impact. Additionally, organizations should review their NLP pipelines to identify any indirect dependencies on the vulnerable library and ensure they are updated accordingly. Incorporating fuzz testing and regular expression complexity analysis into the development lifecycle can help detect similar issues proactively. Finally, maintain awareness of vendor advisories and community updates regarding this and related vulnerabilities.
Description
A Regular Expression Denial of Service (ReDoS) vulnerability was discovered in the Hugging Face Transformers library, specifically within the `normalize_numbers()` method of the `EnglishNormalizer` class. This vulnerability affects versions up to 4.52.4 and is fixed in version 4.53.0. The issue arises from the method's handling of numeric strings, which can be exploited using crafted input strings containing long sequences of digits, leading to excessive CPU consumption. This vulnerability impacts text-to-speech and number normalization tasks, potentially causing service disruption, resource exhaustion, and API vulnerabilities.