CVE-2026-44723

May 26, 2026, 8:24 p.m.

5.0
Medium

Description

Vowpal Wabbit is a machine learning system. The workflow .github/workflows/python_checks.yml embeds ${{ github.event.pull_request.title }} directly inside double-quoted bash strings in four separate steps across four jobs, each passing it as a CLI argument to the Python test script run_tests_model_gen_and_load.py. The shell interprets the expanded string before invoking Python, allowing an attacker to break out of the quotes and execute arbitrary commands on the runner. The pull_request trigger fires on PRs targeting any branch (branches: ['*']), with no additional access gate. This vulnerability is fixed by the 998e390e80a7e8192d7849b7784bc113dbd190ad commit.

Product(s) Impacted

Vendor Product Versions
Vowpal Wabbit
  • Vowpal Wabbit
  • *

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.

*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 vowpal_wabbit vowpal_wabbit / / / / / / / /

CVSS Score

5.0 / 10

CVSS Data - 3.1

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

    View Vector String

Timeline

Published: May 26, 2026, 5:16 p.m.
Last Modified: May 26, 2026, 8:24 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.