CVE-2026-2393
May 11, 2026, 8:25 p.m.
7.1
High
Description
A Server-Side Request Forgery (SSRF) vulnerability exists in MLflow versions prior to 3.9.0. The `_create_webhook()` function in `mlflow/server/handlers.py` accepts a user-controlled `url` parameter without validation, and the `_send_webhook_request()` function in `mlflow/webhooks/delivery.py` sends HTTP POST requests to this attacker-controlled URL. This allows an authenticated attacker to force the MLflow backend to send HTTP requests to internal services, cloud metadata endpoints, or arbitrary external servers. The lack of input sanitization, URL scheme filtering, or allowlist validation on the webhook URL enables exploitation, potentially leading to cloud credential theft, internal network access, and data exfiltration.
Product(s) Impacted
| Vendor | Product | Versions |
|---|---|---|
| Mlflow |
|
|
Weaknesses
Common security weaknesses mapped to this vulnerability.
CWE-918
Server-Side Request Forgery (SSRF)
The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination.
*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 | <3.9.0 | / | / | / | / | / | / | / |
Tags
CVSS Score
CVSS Data - 3.0
- Attack Vector: NETWORK
- Attack Complexity: LOW
- Privileges Required: LOW
- Scope: UNCHANGED
- Confidentiality Impact: HIGH
- Integrity Impact: LOW
- Availability Impact: NONE
CVSS:3.0/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:N
Timeline
Published: May 11, 2026, 6:16 p.m.
Last Modified: May 11, 2026, 8:25 p.m.
Last Modified: May 11, 2026, 8:25 p.m.
Status : Received
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.