CVE-2025-53818

July 15, 2025, 8:15 p.m.

8.9
High

Description

GitHub Kanban MCP Server is a Model Context Protocol (MCP) server for managing GitHub issues in Kanban board format and streamlining LLM task management. Version 0.3.0 of the MCP Server is written in a way that is vulnerable to command injection vulnerability attacks as part of some of its MCP Server tool definition and implementation. The MCP Server exposes the tool `add_comment` which relies on Node.js child process API `exec` to execute the GitHub (`gh`) command, is an unsafe and vulnerable API if concatenated with untrusted user input. As of time of publication, no known patches are available.

Product(s) Impacted

Vendor Product Versions
Github
  • Kanban Mcp Server
  • 0.3.0-0.4.0

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-78
Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')
The product constructs all or part of an OS command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended OS command when it is sent to a downstream component.

CVSS Score

8.9 / 10

CVSS Data - 4.0

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Attack Requirements: NONE
  • Privileges Required: NONE
  • User Interaction: NONE
  • Scope:
  • Confidentiality Impact: HIGH
  • Integrity Impact: HIGH
  • Availability Impact: HIGH
  • Exploit Maturity: PROOF_OF_CONCEPT
  • CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X

    View Vector String

Timeline

Published: July 14, 2025, 9:15 p.m.
Last Modified: July 15, 2025, 8:15 p.m.

Status : Awaiting Analysis

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

More info

Source

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.