pyLoad: Lack of Input Size Validation Leads to Denial of Service (DoS) and Process Termination

Description:

The API rpc function in api_blueprint.py handles multipart/form-data uploads by reading the whole content of the uploaded file into memory with file.read(). This occurs before the data is sent to the underlying function. Since there is no size limit set at this point, a large file upload can exhaust the server's available memory which led to process termination.

VulnerableCode & Path:

https://github.com/pyload/pyload/blob/8e447958b8a66c5899775e725a8b90bce6643004/src/pyload/webui/app/blueprints/api_blueprint.py#L73

Steps to Reproduce:

1. Log in to pyLoad (or use an API key, here i used api to communicate).
2. Prepare a large file (e.g., 10GB) .

truncate -s 10G large_file.bin

3. Send a multipart request to an API function that accepts a file, such as check_online_status_container:

curl -X POST "http://localhost:8000/api/rpc" \
     -H "X-API-Key: YOUR_API_KEY" \
     -F "func=check_online_status_container" \
     -F "container=@large_file.bin"

4. Monitor the server's memory usage. The process will attempt to allocate memory for the entire file and last the process will be killed by the kernel. <img width="1902" height="610" alt="dos-process-kill-poc" src="https://github.com/user-attachments/assets/2b9bfd0f-5b9f-48dd-983f-f97dfcc358cc" />

Impact

• Denial of Service (DoS): The pyLoad process will be killed by the Operating System's Out-Of-Memory (OOM) killer, or the entire system may become unresponsive due to swap thrashing or memory exhaustion.
• Service Instability: Any active downloads or tasks will be interrupted.

Mitigations

• Implement File Size Limits: Enforce a maximum size for uploaded files in the web server configuration (e.g., Nginx client_max_body_size).