CVE-2025-53909
July 17, 2025, 9:15 p.m.
9.1
Critical
Description
mailcow: dockerized is an open source groupware/email suite based on docker. A Server-Side Template Injection (SSTI) vulnerability exists in versions prior to 2025-07 in the notification template system used by mailcow for sending quota and quarantine alerts. The template rendering engine allows template expressions that may be abused to execute code in certain contexts. The issue requires admin-level access to mailcow UI to configure templates, which are automatically rendered during normal system operation. Version 2025-07 contains a patch for the issue.
Product(s) Impacted
| Vendor | Product | Versions |
|---|---|---|
| Mailcow |
|
|
Weaknesses
Common security weaknesses mapped to this vulnerability.
CWE-1336
Improper Neutralization of Special Elements Used in a Template Engine
The product uses a template engine to insert or process externally-influenced input, but it does not neutralize or incorrectly neutralizes special elements or syntax that can be interpreted as template expressions or other code directives when processed by the engine.
*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 | mailcow | mailcow | 2025-* | / | / | / | / | / | / | / |
Tags
CVSS Score
CVSS Data - 3.1
- Attack Vector: NETWORK
- Attack Complexity: LOW
- Privileges Required: HIGH
- Scope: CHANGED
- Confidentiality Impact: HIGH
- Integrity Impact: HIGH
- Availability Impact: HIGH
CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:C/C:H/I:H/A:H
Timeline
Published: July 17, 2025, 2:15 p.m.
Last Modified: July 17, 2025, 9:15 p.m.
Last Modified: July 17, 2025, 9:15 p.m.
Status : Awaiting Analysis
CVE has been recently published to the CVE List and has been received by the NVD.
More infoSource
security-advisories@github.com
*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.