5.3

CVSS Score

3.0

-

CVSS Score

Basic Information

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CVE-2025-6051

CVE-2025-6051: 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 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.

(GitHub Advisory)

Miggo Vulnerability Database

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CVE-2025-6051

CVE-2025-6051:

5.3

CVSS Score

3.0

-

CVSS Score

Basic Information

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Technical Details

Package Name	Ecosystem	Vulnerable Versions	First Patched Version
transformers	pip	< 4.53.0	4.53.0

Technical Details

Vulnerability Intelligence
Miggo AI

Root Cause Analysis

The vulnerability analysis began by examining the provided commit URLs, which are directly linked to the security advisory. The get_commit_infos tool was used to retrieve the code changes from commits 54a02160eb030da9be18231c77791f2eb3a52216 and ba8eaba9865618253f997784aa565b96206426f0. Both commits modify the file src/transformers/models/clvp/number_normalizer.py.

The core of the vulnerability, as described, is a Regular Expression Denial of Service (ReDoS) within the normalize_numbers method of the EnglishNormalizer class. The commit patches confirm this. The diffs show the removal of several re.sub calls that used inefficient and vulnerable regular expressions. For example, patterns like r"([0-9][0-9\\,]+[0-9])" exhibit what is known as "catastrophic backtracking" when fed a long string of digits without commas. The regex engine's attempts to find a match grow exponentially with the input length, causing the CPU to spike and the application to become unresponsive.

The patch mitigates this by replacing the vulnerable regex patterns with more performant and secure alternatives, such as using possessive quantifiers (++) where appropriate to prevent backtracking, and simplifying the expressions to be less ambiguous (e.g., [0-9,]*[0-9] instead of [0-9\\,]*[0-9]+).

Based on this direct evidence from the security patch, the EnglishNormalizer.normalize_numbers function is confidently identified as the vulnerable function. During exploitation, a profiler would show this function consuming a significant amount of CPU time as it processes the malicious input through the vulnerable regular expressions.

Vulnerable functions

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Miggo AI

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WAF Rule

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Reasoning

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