CVE-2026-8795

June 9, 2026, 1:49 p.m.

7.8
High

Description

A YAML injection vulnerability exists in the Windows.Collectors.Remapping artifact of Rapid7 Velociraptor before version 0.76.6. The hostname field in client_info.json inside a collection ZIP is inserted into a YAML template via Go's text/template without escaping. An attacker providing a crafted collection ZIP can leverage literal double quotes and newlines in the hostname to break out of the YAML quoted string and inject a new mount remapping entry. When an analyst applies the generated remapping file with --remap, arbitrary VQL executes on their machine with NullACLManager (all permissions granted, unsandboxed).

Product(s) Impacted

Vendor Product Versions
Rapid7
  • Velociraptor
  • <0.76.6

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-74
Improper Neutralization of Special Elements in Output Used by a Downstream Component ('Injection')
The product constructs all or part of a command, data structure, or record using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify how it is parsed or interpreted when it is sent to a downstream component.

*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 <0.76.6 / / / / / / /

CVSS Score

7.8 / 10

CVSS Data - 3.1

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

    View Vector String

Timeline

Published: June 9, 2026, 1:16 a.m.
Last Modified: June 9, 2026, 1:49 p.m.

Status : Awaiting Analysis

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

More info

*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.