CVE-2024-6386

Sept. 27, 2024, 1:25 p.m.

8.8
High

Description

The WPML plugin for WordPress is vulnerable to Remote Code Execution in all versions up to, and including, 4.6.12 via the Twig Server-Side Template Injection. This is due to missing input validation and sanitization on the render function. This makes it possible for authenticated attackers, with Contributor-level access and above, to execute code on the server.

Product(s) Impacted

Vendor Product Versions
Wpml
  • Wpml
  • *

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.
CWE-94
Improper Control of Generation of Code ('Code Injection')
The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.

*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 wpml wpml / / / / / wordpress / /

CVSS Score

8.8 / 10

CVSS Data - 3.1

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Privileges Required: LOW
  • Scope: UNCHANGED
  • Confidentiality Impact: HIGH
  • Integrity Impact: HIGH
  • Availability Impact: HIGH
  • CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H

    View Vector String

Timeline

Published: Aug. 21, 2024, 9:15 p.m.
Last Modified: Sept. 27, 2024, 1:25 p.m.

Status : Analyzed

CVE has been recently published to the CVE List and has been received by the NVD.

More info

Source

security@wordfence.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.