CVE-2026-33992

March 27, 2026, 11:17 p.m.

9.3
Critical

Description

pyLoad is a free and open-source download manager written in Python. Prior to version 0.5.0b3.dev97, PyLoad's download engine accepts arbitrary URLs without validation, enabling Server-Side Request Forgery (SSRF) attacks. An authenticated attacker can exploit this to access internal network services and exfiltrate cloud provider metadata. On DigitalOcean droplets, this exposes sensitive infrastructure data including droplet ID, network configuration, region, authentication keys, and SSH keys configured in user-data/cloud-init. Version 0.5.0b3.dev97 contains a patch.

Product(s) Impacted

Vendor Product Versions
Pyload
  • Pyload
  • <0.5.0b3.dev97

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 pyload pyload <0.5.0b3.dev97 / / / / / / /

CVSS Score

9.3 / 10

CVSS Data - 4.0

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Attack Requirements: NONE
  • Privileges Required: LOW
  • User Interaction: NONE
  • Scope:
  • Confidentiality Impact: HIGH
  • Integrity Impact: HIGH
  • Availability Impact: NONE
  • Exploit Maturity: NOT_DEFINED
  • CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:N/SC:H/SI:H/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: March 27, 2026, 11:17 p.m.
Last Modified: March 27, 2026, 11:17 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.