FKIE_CVE-2025-6051

Vulnerability from fkie_nvd - Published: 2025-09-14 17:15 - Updated: 2026-09-30 23:10
Severity
5.3 (Medium) - CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L
Summary
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.
References
security@huntr.devhttps://github.com/huggingface/transformers/commit/ba8eaba9865618253f997784aa565b96206426f0Patch
security@huntr.devhttps://huntr.com/bounties/af929523-7b59-418a-bf55-301830b2ac9dExploit, Issue Tracking, Patch, Third Party Advisory
Impacted products
Vendor Product Version
huggingface transformers 4.52.4
To clipboard 

{
  "affected": [
    {
      "affectedData": [
        {
          "product": "huggingface/transformers",
          "vendor": "huggingface",
          "versions": [
            {
              "lessThan": "4.53.0",
              "status": "affected",
              "version": "unspecified",
              "versionType": "custom"
            }
          ]
        }
      ],
      "source": "security@huntr.dev"
    }
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  "configurations": [
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      "nodes": [
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          "cpeMatch": [
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              "criteria": "cpe:2.3:a:huggingface:transformers:4.52.4:*:*:*:*:*:*:*",
              "matchCriteriaId": "BA362206-8A09-4249-A522-427C8793D241",
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          "operator": "OR"
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  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "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\u0027s 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."
    },
    {
      "lang": "es",
      "value": "Se descubri\u00f3 una vulnerabilidad de denegaci\u00f3n de servicio por expresi\u00f3n regular (ReDoS) en la biblioteca Hugging Face Transformers, espec\u00edficamente dentro del m\u00e9todo \u0027normalize_numbers()\u0027 de la clase \u0027EnglishNormalizer\u0027. Esta vulnerabilidad afecta a las versiones hasta la 4.52.4 y est\u00e1 corregida en la versi\u00f3n 4.53.0. El problema surge del manejo de cadenas num\u00e9ricas por parte del m\u00e9todo, que puede ser explotado utilizando cadenas de entrada manipuladas que contengan largas secuencias de d\u00edgitos, lo que lleva a un consumo excesivo de CPU. Esta vulnerabilidad impacta en las tareas de texto a voz y normalizaci\u00f3n de n\u00fameros, potencialmente causando interrupci\u00f3n del servicio, agotamiento de recursos y vulnerabilidades de API."
    }
  ],
  "id": "CVE-2025-6051",
  "lastModified": "2026-09-30T23:10:00.237",
  "metrics": {
    "cvssMetricV30": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "LOW",
          "baseScore": 5.3,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "NONE",
          "integrityImpact": "NONE",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L",
          "version": "3.0"
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        "impactScore": 1.4,
        "source": "security@huntr.dev",
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    "ssvcV203": [
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          "role": "CISA Coordinator",
          "timestamp": "2025-09-15T15:59:46.280744Z",
          "version": "2.0.3"
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  "published": "2025-09-14T17:15:34.210",
  "references": [
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      "tags": [
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      "url": "https://github.com/huggingface/transformers/commit/ba8eaba9865618253f997784aa565b96206426f0"
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  "vulnStatus": "Analyzed",
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          "lang": "en",
          "value": "CWE-1333"
        }
      ],
      "source": "security@huntr.dev",
      "type": "Secondary"
    }
  ]
}



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Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

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Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.



Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.