EUVD-2004-1928

Malware in sbrugna...

EUVD-2009-3835

Malware in sbrugna...

EUVD-2025-29125

Malicious code in bioql PyPI...

EUVD-2022-51936

Malicious code in bioql PyPI...

EUVD-2025-7855

Malicious code in bioql PyPI...

GHSA-RCV9-QM8P-9P6J Hugging Face Transformers library has Regular Expression Denial of Service

A Regular Expression Denial of Service ReDoS vulnerability was discovered in the Hugging Face Transformers library, specifically within the normalizenumbers 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 fro...

Hugging Face Transformers library has Regular Expression Denial of Service

A Regular Expression Denial of Service ReDoS vulnerability was discovered in the Hugging Face Transformers library, specifically within the normalizenumbers 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 fro...

Regular Expression Denial of Service (ReDoS)

Overview transformers is a State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow Affected versions of this package are vulnerable to Regular Expression Denial of Service ReDoS via the normalizenumbers function of the EnglishNormalizer class. An attacker can cause excessive CPU...

CVE-2025-6051

A Regular Expression Denial of Service ReDoS vulnerability was discovered in the Hugging Face Transformers library, specifically within the normalizenumbers 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 fro...

CVE-2025-6051

CVE-2025-6051 is a ReDoS in Hugging Face Transformers’ EnglishNormalizer.normalize_numbers(), affecting versions up to 4.52.4 and fixed in 4.53.0. The issue arises from numeric string handling, enabling crafted inputs with long digit sequences to cause excessive CPU usage, impacting text-to-speec...

CVE-2025-6051 Regular Expression Denial of Service (ReDoS) in huggingface/transformers

A Regular Expression Denial of Service ReDoS vulnerability was discovered in the Hugging Face Transformers library, specifically within the normalizenumbers 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 fro...

CVE-2025-6051 Regular Expression Denial of Service (ReDoS) in huggingface/transformers

A Regular Expression Denial of Service ReDoS vulnerability was discovered in the Hugging Face Transformers library, specifically within the normalizenumbers 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 fro...

CVE-2025-6051 Regular Expression Denial of Service (ReDoS) in huggingface/transformers

A Regular Expression Denial of Service ReDoS vulnerability was discovered in the Hugging Face Transformers library, specifically within the normalizenumbers 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 fro...

Malicious code in english-time (npm)

The package english-time was found to contain malicious code...

MAL-2025-19656 Malicious code in english-time (npm)

The package english-time was found to contain malicious code...

Regular expression Denial of Service - ReDoS

Description A regular expression denial of service ReDoS vulnerability has been identified in the Hugging Face Transformers library's CLVP number normalizer. The vulnerability exists in the normalizenumbers method of the EnglishNormalizer class, which converts numeric strings to their English wor...

PT-2025-37422

Name of the Vulnerable Software and Affected Versions Hugging Face Transformers versions up to 4.52.4 Description A Regular Expression Denial of Service ReDoS vulnerability exists in the normalize numbers method of the EnglishNormalizer class. This issue arises from the method's handling of numer...

USB: a Comprehensive and Unified Safety Evaluation Benchmark for Multimodal Large Language Models

Despite their remarkable achievements and widespread adoption, Multimodal Large Language Models MLLMs have revealed significant security vulnerabilities, highlighting the urgent need for robust safety evaluation benchmarks. Existing MLLM safety benchmarks, however, fall short in terms of data...

CVE-2022-4604

A vulnerability classified as problematic was found in wp-english-wp-admin Plugin up to 1.5.1. Affected by this vulnerability is the function registerendpoints of the file english-wp-admin.php. The manipulation leads to cross-site request forgery. The attack can be launched remotely. Upgrading to...

Spill the Beans: Exploiting CPU Cache Side-Channels to Leak Tokens from Large Language Models

Side-channel attacks on shared hardware resources increasingly threaten confidentiality, especially with the rise of Large Language Models LLMs. In this work, we introduce Spill The Beans, a novel application of cache side-channels to leak tokens generated by an LLM. By co-locating an attack...