CVE-2025-6051
MediumCVSS 5.3Exploitation Probability (EPSS)
Low risk30th percentile - higher than 30% of all known CVEs
Summary
A ReDoS vulnerability was discovered in the Hugging Face Transformers library in the normalize_numbers() method of the EnglishNormalizer class. It affects versions up to 4.52.4 and is fixed in 4.53.0. Crafted input with long digit sequences can cause excessive CPU consumption.
Risk Assessment
An attacker can cause service disruption, resource exhaustion, and DoS vulnerabilities, especially in text-to-speech and number normalization tasks.
Recommendation
Update the Transformers library to version 4.53.0 or later. Restrict access to input-processing functions.
Other vulnerabilities in Hugging Face Transformers
See all- CVE-2026-80047High
A vulnerability in Hugging Face Transformers (versions 4.57.0 to 5.16.1) allows remote Python files to be written to local disk without user consent when using GenerativePreTrainedModel.load_custom_generate(). The function fetches and caches a remote module file before performing the required trust_remote_code consent check, inverting the security model enforced by other code-loading paths. As a result, attacker-controlled Python code from custom_generate/generate.py is copied into the user's ~/.cache/huggingface/modules directory even if the user declines the trust prompt.
- CVE-2026-75104Medium
Hugging Face Transformers fails to validate shard filenames in checkpoint index files, allowing attackers to read arbitrary files outside the model directory. Malicious index files with parent-directory references or absolute paths can lead to file disclosure and filesystem reconnaissance.
- CVE-2026-9856High
Hugging Face Transformers up to version 5.8.0.dev0 has an arbitrary file write vulnerability via path traversal in save_pretrained(). Keys from chat_template are used as filenames without validation.
Original NVD description (English source)
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.
Vulnerability data from NVD (NIST) · CISA KEV · EPSS