CVE-2025-6921
Sept. 24, 2025, 6:11 p.m.
5.3
Medium
Description
The huggingface/transformers library, versions prior to 4.53.0, is vulnerable to Regular Expression Denial of Service (ReDoS) in the AdamWeightDecay optimizer. The vulnerability arises from the _do_use_weight_decay method, which processes user-controlled regular expressions in the include_in_weight_decay and exclude_from_weight_decay lists. Malicious regular expressions can cause catastrophic backtracking during the re.search call, leading to 100% CPU utilization and a denial of service. This issue can be exploited by attackers who can control the patterns in these lists, potentially causing the machine learning task to hang and rendering services unresponsive.
Product(s) Impacted
| Vendor | Product | Versions |
|---|---|---|
| Huggingface |
|
|
Weaknesses
Common security weaknesses mapped to this vulnerability.
CWE-400
Uncontrolled Resource Consumption
The product does not properly control the allocation and maintenance of a limited resource, thereby enabling an actor to influence the amount of resources consumed, eventually leading to the exhaustion of available resources.
*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: Sept. 23, 2025, 2:15 p.m.
Last Modified: Sept. 24, 2025, 6:11 p.m.
Last Modified: Sept. 24, 2025, 6:11 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.