CVE-2025-5197
Aug. 6, 2025, 8:23 p.m.
5.3
Medium
Description
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.
Product(s) Impacted
| Vendor | Product | Versions |
|---|---|---|
| Huggingface |
|
|
Weaknesses
Common security weaknesses mapped to this vulnerability.
CWE-1333
Inefficient Regular Expression Complexity
The product uses a regular expression with an inefficient, possibly exponential worst-case computational complexity that consumes excessive CPU cycles.
*CPE(s)
Affected systems and software identified for this CVE.
| Type | Vendor | Product | Version | Update | Edition | Language | Software Edition | Target Software | Target Hardware | Other Information |
|---|---|---|---|---|---|---|---|---|---|---|
| a | huggingface | transformers | <4.53.0 | / | / | / | / | / | / | / |
Tags
CVSS Score
CVSS Data - 3.0
- Attack Vector: NETWORK
- Attack Complexity: LOW
- Privileges Required: NONE
- Scope: UNCHANGED
- Confidentiality Impact: NONE
- Integrity Impact: NONE
- Availability Impact: LOW
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L
Timeline
Published: Aug. 6, 2025, 12:15 p.m.
Last Modified: Aug. 6, 2025, 8:23 p.m.
Last Modified: Aug. 6, 2025, 8:23 p.m.
Status : Awaiting Analysis
CVE has been recently published to the CVE List and has been received by the NVD.
More infoSource
security@huntr.dev
*Disclaimer: Some vulnerabilities do not have an associated CPE. To enhance the data, we use AI to infer CPEs based on CVE details. This is an automated process and might not always be accurate.