Hugging Face Transformers Transformers 4.52.4
Approved changes feed: RSS · Atom
cpe:2.3:a:huggingface:transformers:4.52.4:*:*:*:*:*:*:*
part: a version: 4.52.4 update: *
| Vendor | Huggingface (99e96d05-83c7-5fa6-87a0-b60fade6cd99) |
|---|---|
| Product | Transformers (b4ce00f1-117d-532e-8476-9985a60eef6e) |
| Edition | * |
| Language | * |
| Software edition | * |
| Target software | * |
| Target hardware | * |
| Other | * |
| Notes | Imported from NVD CPE 2.0 feed |
PURL mappings
| PURL | Source | Last updated |
|---|---|---|
pkg:github/huggingface/transformers |
purl2cpe | 2026-06-01 10:17:04.507192 |
pkg:pypi/transformers |
purl2cpe | 2026-06-01 10:17:04.507194 |
Vulnerability references
| Identifier | cpeApplicability | Submitted | db.gcve.eu details | Rationale |
|---|---|---|---|---|
CVE:CVE-2025-6638 |
vulnerable | 2026-06-08 07:43:15.746529 |
Regular Expression Denial of Service (ReDoS) in huggingface/transformers
MEDIUM (5.3)
A Regular Expression Denial of Service (ReDoS) vulnerability was discovered in the Hugging Face Transformers library, specifically affecting the MarianTokenizer's `remove_language_code()` method. This vulnerability is present in version 4.52.4 and has been fixed in version 4.53.0. The issue arises from inefficient regex processing, which can be exploited by crafted input strings containing malformed language code patterns, leading to excessive CPU consumption and potential denial of service.
Published: 2025-09-12T10:46:07.934Z
Updated: 2025-09-12T11:52:53.854Z |
Imported from gcve-enriched-dumps CVE data |
CVE:CVE-2025-6051 |
vulnerable | 2026-06-08 07:43:14.186095 |
Regular Expression Denial of Service (ReDoS) in huggingface/transformers
MEDIUM (5.3)
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.
Published: 2025-09-14T17:03:02.953Z
Updated: 2025-09-15T15:59:54.371Z |
Imported from gcve-enriched-dumps CVE data |
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