CVE-2025-14728

Dec. 29, 2025, 7:15 p.m.

6.8
Medium

Description

Rapid7 Velociraptor versions before 0.75.6 contain a directory traversal issue on Linux servers that allows a rogue client to upload a file which is written outside the datastore directory. Velociraptor is normally only allowed to write in the datastore directory. The issue occurs due to insufficient sanitization of directory names which end with a ".", only encoding the final "." AS "%2E". Although files can be written to incorrect locations, the containing directory must end with "%2E". This limits the impact of this vulnerability, and prevents it from overwriting critical files.

Product(s) Impacted

Vendor Product Versions
Rapid7
  • Velociraptor
  • *

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-22
Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')
The product uses external input to construct a pathname that is intended to identify a file or directory that is located underneath a restricted parent directory, but the product does not properly neutralize special elements within the pathname that can cause the pathname to resolve to a location that is outside of the restricted directory.

*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 rapid7 velociraptor / / / / / / / /

CVSS Score

6.8 / 10

CVSS Data - 3.1

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

    View Vector String

Timeline

Published: Dec. 29, 2025, 7:15 p.m.
Last Modified: Dec. 29, 2025, 7:15 p.m.

Status : Received

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

More info

Source

cve@rapid7.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.