BREW-RAPID-MLX-CVE-2025-1194 (GHSA-FPWR-67PX-3QHX)
Vulnerability from osv_homebrew – Published: 2026-08-13 17:32 – Updated: 2026-09-10 20:37 – Source website
VLAI
Summary
Transformers Regular Expression Denial of Service (ReDoS) vulnerability
Details
A Regular Expression Denial of Service (ReDoS) vulnerability was identified in the huggingface/transformers library, specifically in the file tokenization_gpt_neox_japanese.py of the GPT-NeoX-Japanese model. The vulnerability occurs in the SubWordJapaneseTokenizer class, where regular expressions process specially crafted inputs. The issue stems from a regex exhibiting exponential complexity under certain conditions, leading to excessive backtracking. This can result in high CPU usage and potential application downtime, effectively creating a Denial of Service (DoS) scenario. The affected version is v4.48.1 (latest).
Severity
4.3 (Medium)
References
{
"affected": [
{
"ecosystem_specific": {
"fix": "bump",
"range_state": "fixed",
"resource": "transformers",
"resource_purl": "pkg:pypi/transformers@5.15.1",
"upstream_fixed_in": "4.50.0"
},
"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 identified in the huggingface/transformers library, specifically in the file `tokenization_gpt_neox_japanese.py` of the GPT-NeoX-Japanese model. The vulnerability occurs in the SubWordJapaneseTokenizer class, where regular expressions process specially crafted inputs. The issue stems from a regex exhibiting exponential complexity under certain conditions, leading to excessive backtracking. This can result in high CPU usage and potential application downtime, effectively creating a Denial of Service (DoS) scenario. The affected version is v4.48.1 (latest).",
"id": "BREW-rapid-mlx-CVE-2025-1194",
"modified": "2026-09-10T20:37:33Z",
"published": "2026-08-13T17:32:07Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-1194"
},
{
"type": "WEB",
"url": "https://github.com/huggingface/transformers/commit/92c5ca9dd70de3ade2af2eb835c96215cc50e815"
},
{
"type": "PACKAGE",
"url": "https://github.com/huggingface/transformers"
},
{
"type": "WEB",
"url": "https://huntr.com/bounties/86f58dcd-683f-4adc-a735-849f51e9abb2"
}
],
"schema_version": "1.7.3",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:L",
"type": "CVSS_V3"
}
],
"summary": "Transformers Regular Expression Denial of Service (ReDoS) vulnerability",
"upstream": [
"GHSA-fpwr-67px-3qhx",
"CVE-2025-1194",
"PYSEC-2026-1984"
]
}
Loading…
Loading…
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.
Loading…
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.
Loading…
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.
Loading…