BREW-RAPID-MLX-CVE-2025-3933 (GHSA-37MW-44QP-F5JM)
Vulnerability from osv_homebrew – Published: 2026-08-13 17:32 – Updated: 2026-09-10 20:37 – Source website
VLAI
Summary
Transformers is vulnerable to ReDoS attack through its DonutProcessor class
Details
A Regular Expression Denial of Service (ReDoS) vulnerability was discovered in the Hugging Face Transformers library, specifically within the DonutProcessor class's token2json() method. This vulnerability affects versions 4.51.3 and earlier, and is fixed in version 4.52.1. The issue arises from the regex pattern <s_(.*?)> which can be exploited to cause excessive CPU consumption through crafted input strings due to catastrophic backtracking. This vulnerability can lead to service disruption, resource exhaustion, and potential API service vulnerabilities, impacting document processing tasks using the Donut model.
Severity
5.3 (Medium)
{
"affected": [
{
"ecosystem_specific": {
"fix": "bump",
"range_state": "fixed",
"resource": "transformers",
"resource_purl": "pkg:pypi/transformers@5.15.1",
"upstream_fixed_in": "4.52.1"
},
"package": {
"ecosystem": "Homebrew",
"name": "rapid-mlx",
"purl": "pkg:brew/rapid-mlx"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.12.11"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"database_specific": {
"confidence": "high",
"source": "matched",
"strategy": "registry",
"upstream_evidence": [
{
"ecosystem": "PyPI",
"key": "pkg:pypi/transformers@5.15.1",
"name": "transformers",
"resource": "transformers",
"strategy": "registry",
"subject_version": "5.15.1"
}
]
},
"details": "A Regular Expression Denial of Service (ReDoS) vulnerability was discovered in the Hugging Face Transformers library, specifically within the DonutProcessor class\u0027s `token2json()` method. This vulnerability affects versions 4.51.3 and earlier, and is fixed in version 4.52.1. The issue arises from the regex pattern `\u003cs_(.*?)\u003e` which can be exploited to cause excessive CPU consumption through crafted input strings due to catastrophic backtracking. This vulnerability can lead to service disruption, resource exhaustion, and potential API service vulnerabilities, impacting document processing tasks using the Donut model.",
"id": "BREW-rapid-mlx-CVE-2025-3933",
"modified": "2026-09-10T20:37:33Z",
"published": "2026-08-13T17:32:07Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-3933"
},
{
"type": "WEB",
"url": "https://github.com/huggingface/transformers/pull/37788"
},
{
"type": "WEB",
"url": "https://github.com/huggingface/transformers/commit/ebbe9b12dd75b69f92100d684c47f923ee262a93"
},
{
"type": "PACKAGE",
"url": "https://github.com/huggingface/transformers"
},
{
"type": "WEB",
"url": "https://huntr.com/bounties/25282953-5827-4384-bb6f-5790d275721b"
}
],
"schema_version": "1.7.3",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L",
"type": "CVSS_V3"
}
],
"summary": "Transformers is vulnerable to ReDoS attack through its DonutProcessor class",
"upstream": [
"GHSA-37mw-44qp-f5jm",
"CVE-2025-3933",
"PYSEC-2026-1977"
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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
| Author | Source | Type | Date | Other |
|---|
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.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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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.
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