CVE-2025-15379
March 30, 2026, 1:26 p.m.
10.0
Critical
Description
A command injection vulnerability exists in MLflow's model serving container initialization code, specifically in the `_install_model_dependencies_to_env()` function. When deploying a model with `env_manager=LOCAL`, MLflow reads dependency specifications from the model artifact's `python_env.yaml` file and directly interpolates them into a shell command without sanitization. This allows an attacker to supply a malicious model artifact and achieve arbitrary command execution on systems that deploy the model. The vulnerability affects versions 3.8.0 and is fixed in version 3.8.2.
Product(s) Impacted
| Vendor | Product | Versions |
|---|---|---|
| Mlflow |
|
|
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 | mlflow | mlflow | 3.8.0 | / | / | / | / | / | / | / |
| a | mlflow | mlflow | 3.8.2 | / | / | / | / | / | / | / |
Tags
CVSS Score
CVSS Data - 3.0
- Attack Vector: NETWORK
- Attack Complexity: LOW
- Privileges Required: NONE
- Scope: CHANGED
- Confidentiality Impact: HIGH
- Integrity Impact: HIGH
- Availability Impact: HIGH
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
Timeline
Published: March 30, 2026, 8:16 a.m.
Last Modified: March 30, 2026, 1:26 p.m.
Last Modified: March 30, 2026, 1:26 p.m.
Status : Awaiting Analysis
CVE has been recently published to the CVE List and has been received by the NVD.
More infoSource
*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.