GHSA-m74m-f7cr-432x

Summary

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.

Details

The vulnerability exists in PyLoad's download package functionality (/api/addPackage endpoint), which directly passes user-supplied URLs to the download engine without validating the destination. The affected code in src/pyload/webui/app/blueprints/api_blueprint.py:

@bp.route("/addPackage", methods=["POST"], endpoint="add_package")
@login_required
def add_package():
    name = flask.request.form["add_name"]
    links = flask.request.form["add_links"].split("\n")
    # ... validation omitted ...
    api.add_package(name, links, dest)  # No URL validation

The download engine in src/pyload/core/managers/download.py accepts any URL scheme and initiates HTTP requests to arbitrary destinations, including internal network addresses and cloud metadata endpoints.

Proof of Concept

Live Demo Instance: http://143.244.141.81:8000
Credentials: pyload / pyload

• Login into the pyload application
• Navigate to package tab and enter the package name and fill the Link section with the following URL

http://169.254.169.254/metadata/v1.json

<img width="1851" height="786" alt="image" src="https://github.com/user-attachments/assets/18e7aedf-7663-4a57-8f3e-5200be2c958e" />

• Now navigate to Files section and download the link.

<img width="1429" height="870" alt="image" src="https://github.com/user-attachments/assets/9b8b9cd6-afb7-461c-b058-a3cc4f26e2e6" />

• It was observed that we are able to Read the Digital Ocean Metadata

<img width="1872" height="837" alt="image" src="https://github.com/user-attachments/assets/d30d2d74-53e9-46f8-8206-894a275ac831" />

The downloaded v1.json file contains sensitive cloud infrastructure data:

• Droplet ID: Unique identifier for the instance
• Network Configuration: Public/private IP addresses, VPC topology
• Authentication Keys: Cloud provider auth tokens
• SSH Keys: Public keys configured in droplet metadata
• Region and Datacenter: Infrastructure location

Impact

Vulnerability Type: Server-Side Request Forgery (SSRF)
CVSS Score: 7.7 - 9.1 (High to Critical, depending on cloud deployment)

Affected Systems

• All PyLoad installations (version 0.5.0 and potentially earlier)
• Critical Impact on cloud deployments (AWS EC2, DigitalOcean, Google Cloud, Azure) where metadata contains:
  • IAM credentials (AWS)
  • SSH private keys (configured in user-data)
  • API tokens and secrets
  • Database credentials stored in cloud-init

Attack Requirements

• Valid PyLoad user account (any role - ADMIN or USER)
• Network connectivity to PyLoad instance

Security Impact

1. Cloud Metadata Theft: Complete exfiltration of instance metadata
2. Lateral Movement: Discovery and enumeration of internal network services
3. Credential Exposure: Theft of cloud IAM credentials, SSH keys, API tokens
4. Infrastructure Mapping: Network topology, IP addressing, service discovery

Remediation

Implement URL validation in the download engine:

1. Whitelist allowed URL schemes (http/https only)
2. Block requests to private IP ranges (RFC 1918, link-local addresses)
3. Block cloud metadata endpoints (169.254.169.254, metadata.google.internal, etc.)
4. Implement request destination validation before initiating downloads