Bug 2386842 (CVE-2025-5197)

Summary:	CVE-2025-5197 transformers: Transformers ReDoS Vulnerability
Product:	[Other] Security Response	Reporter:	OSIDB Bzimport <bzimport>
Component:	vulnerability	Assignee:	Product Security DevOps Team <prodsec-dev>
Status:	NEW ---	QA Contact:
Severity:	medium	Docs Contact:
Priority:	medium
Version:	unspecified	CC:	alinfoot, anpicker, bbrownin, bparees, dtrifiro, haoli, hasun, hkataria, jajackso, jcammara, jfula, jkoehler, jmitchel, jneedle, jowilson, jwong, kegrant, koliveir, kshier, lphiri, mabashia, nyancey, ometelka, pbraun, ptisnovs, rbryant, shvarugh, simaishi, smcdonal, stcannon, syedriko, teagle, tfister, thavo, ttakamiy, weaton, xdharmai, yguenane
Target Milestone:	---	Keywords:	Security
Target Release:	---
Hardware:	All
OS:	Linux
Whiteboard:
Fixed In Version:		Doc Type:	---
Doc Text:	A flaw was found in transformers. The `convert_tf_weight_name_to_pt_weight_name()` function contains a Regular Expression Denial of Service (ReDoS) vulnerability, where a maliciously crafted input string can cause excessive backtracking during regular expression matching. This flaw allows a network-based attacker to trigger this condition by providing a specially designed string as input. This results in a denial of service due to resource exhaustion.	Story Points:	---
Clone Of:		Environment:
Last Closed:		Type:	---
Regression:	---	Mount Type:	---
Documentation:	---	CRM:
Verified Versions:		Category:	---
oVirt Team:	---	RHEL 7.3 requirements from Atomic Host:
Cloudforms Team:	---	Target Upstream Version:
Embargoed:

Description OSIDB Bzimport 2025-08-06 12:01:19 UTC

A Regular Expression Denial of Service (ReDoS) vulnerability exists in the Hugging Face Transformers library, specifically in the `convert_tf_weight_name_to_pt_weight_name()` function. This function, responsible for converting TensorFlow weight names to PyTorch format, uses a regex pattern `/[^/]*___([^/]*)/` that can be exploited to cause excessive CPU consumption through crafted input strings due to catastrophic backtracking. The vulnerability affects versions up to 4.51.3 and is fixed in version 4.53.0. This issue can lead to service disruption, resource exhaustion, and potential API service vulnerabilities, impacting model conversion processes between TensorFlow and PyTorch formats.