CVE-2026-40089

April 9, 2026, 8:16 p.m.

9.9
Critical

Description

Sonicverse is a Self-hosted Docker Compose stack for live radio streaming. The Sonicverse Radio Audio Streaming Stack dashboard contains a Server-Side Request Forgery (SSRF) vulnerability in its API client (apps/dashboard/lib/api.ts). Installations created using the provided install.sh script (including the one‑liner bash <(curl -fsSL https://sonicverse.short.gy/install-audiostack)) are affected. In these deployments, the dashboard accepts user-controlled URLs and passes them directly to a server-side HTTP client without sufficient validation. An authenticated operator can abuse this to make arbitrary HTTP requests from the dashboard backend to internal or external systems. This vulnerability is fixed with commit cb1ddbacafcb441549fe87d3eeabdb6a085325e4.

Product(s) Impacted

Vendor Product Versions
Sonicverse
  • Sonicverse
  • *

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-918
Server-Side Request Forgery (SSRF)
The web server receives a URL or similar request from an upstream component and retrieves the contents of this URL, but it does not sufficiently ensure that the request is being sent to the expected destination.

*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 sonicverse sonicverse / / / / / / / /

CVSS Score

9.9 / 10

CVSS Data - 3.1

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Privileges Required: LOW
  • Scope: CHANGED
  • Confidentiality Impact: HIGH
  • Integrity Impact: HIGH
  • Availability Impact: LOW
  • CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:L

    View Vector String

Timeline

Published: April 9, 2026, 8:16 p.m.
Last Modified: April 9, 2026, 8:16 p.m.

Status : Received

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.