CVE-2025-50461
Aug. 19, 2025, 8:15 p.m.
6.5
Medium
Description
A deserialization vulnerability exists in Volcengine's verl 3.0.0, specifically in the scripts/model_merger.py script when using the "fsdp" backend. The script calls torch.load() with weights_only=False on user-supplied .pt files, allowing attackers to execute arbitrary code if a maliciously crafted model file is loaded. An attacker can exploit this by convincing a victim to download and place a malicious model file in a local directory with a specific filename pattern. This vulnerability may lead to arbitrary code execution with the privileges of the user running the script.
Product(s) Impacted
| Vendor | Product | Versions |
|---|---|---|
| Volcengine |
|
|
Weaknesses
Common security weaknesses mapped to this vulnerability.
CWE-77
Improper Neutralization of Special Elements used in a Command ('Command Injection')
The product constructs all or part of a command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended command when it is sent to a downstream component.
*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 | volcengine | verl | 3.0.0 | / | / | / | / | / | / | / |
References
Tags
CVSS Score
CVSS Data - 3.1
- Attack Vector: NETWORK
- Attack Complexity: LOW
- Privileges Required: NONE
- Scope: UNCHANGED
- Confidentiality Impact: LOW
- Integrity Impact: LOW
- Availability Impact: NONE
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:N
Timeline
Published: Aug. 19, 2025, 2:15 p.m.
Last Modified: Aug. 19, 2025, 8:15 p.m.
Last Modified: Aug. 19, 2025, 8:15 p.m.
Status : Received
CVE has been recently published to the CVE List and has been received by the NVD.
More infoSource
cve@mitre.org
*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.