CVE-2024-6800

Sept. 30, 2024, 7:14 p.m.

9.8
Critical

Description

An XML signature wrapping vulnerability was present in GitHub Enterprise Server (GHES) when using SAML authentication with specific identity providers utilizing publicly exposed signed federation metadata XML. This vulnerability allowed an attacker with direct network access to GitHub Enterprise Server to forge a SAML response to provision and/or gain access to a user with site administrator privileges. Exploitation of this vulnerability would allow unauthorized access to the instance without requiring prior authentication. This vulnerability affected all versions of GitHub Enterprise Server prior to 3.14 and was fixed in versions 3.13.3, 3.12.8, 3.11.14, and 3.10.16. This vulnerability was reported via the GitHub Bug Bounty program.

Product(s) Impacted

Vendor Product Versions
Github
  • Enterprise Server
  • *

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-347
Improper Verification of Cryptographic Signature
The product does not verify, or incorrectly verifies, the cryptographic signature for data.

*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 github enterprise_server / / / / / / / /
a github enterprise_server / / / / / / / /
a github enterprise_server / / / / / / / /
a github enterprise_server / / / / / / / /

CVSS Score

9.8 / 10

CVSS Data - 3.1

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Privileges Required: NONE
  • Scope: UNCHANGED
  • Confidentiality Impact: HIGH
  • Integrity Impact: HIGH
  • Availability Impact: HIGH
  • CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

    View Vector String

Timeline

Published: Aug. 20, 2024, 8:15 p.m.
Last Modified: Sept. 30, 2024, 7:14 p.m.

Status : Analyzed

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

More info

Source

product-cna@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.