CVE-2025-46338

April 29, 2025, 1:52 p.m.

6.9
Medium

Description

Audiobookshelf is a self-hosted audiobook and podcast server. Prior to version 2.21.0, an improper input handling vulnerability in the `/api/upload` endpoint allows an attacker to perform a reflected cross-site scripting (XSS) attack by submitting malicious payloads in the `libraryId` field. The unsanitized input is reflected in the server’s error message, enabling arbitrary JavaScript execution in a victim's browser. This issue has been patched in version 2.21.0.

Product(s) Impacted

Vendor Product Versions
Audiobookshelf
  • Audiobookshelf
  • <2.21.0

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-79
Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
The product does not neutralize or incorrectly neutralizes user-controllable input before it is placed in output that is used as a web page that is served to other users.

*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 audiobookshelf audiobookshelf <2.21.0 / / / / / / /

CVSS Score

6.9 / 10

CVSS Data - 4.0

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Attack Requirements: NONE
  • Privileges Required: NONE
  • User Interaction: NONE
  • Scope:
  • Confidentiality Impact: NONE
  • Integrity Impact: NONE
  • Availability Impact: NONE
  • Exploit Maturity: NOT_DEFINED
  • CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:N/SC:L/SI:L/SA:N/E:X/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: April 29, 2025, 5:15 a.m.
Last Modified: April 29, 2025, 1:52 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.