Data exposure via ZeroMQ on multi-node vLLM deployment

Impact

In a multi-node vLLM deployment, vLLM uses ZeroMQ for some multi-node communication purposes. The primary vLLM host opens an XPUB ZeroMQ socket and binds it to ALL interfaces. While the socket is always opened for a multi-node deployment, it is only used when doing tensor parallelism across multiple hosts.

Any client with network access to this host can connect to this XPUB socket unless its port is blocked by a firewall. Once connected, these arbitrary clients will receive all of the same data broadcasted to all of the secondary vLLM hosts. This data is internal vLLM state information that is not useful to an attacker.

By potentially connecting to this socket many times and not reading data published to them, an attacker can also cause a denial of service by slowing down or potentially blocking the publisher.

Detailed Analysis

The XPUB socket in question is created here:

https://github.com/vllm-project/vllm/blob/c21b99b91241409c2fdf9f3f8c542e8748b317be/vllm/distributed/device_communicators/shm_broadcast.py#L236-L237

Data is published over this socket via MessageQueue.enqueue() which is called by MessageQueue.broadcast_object():

https://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/distributed/device_communicators/shm_broadcast.py#L452-L453

https://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/distributed/device_communicators/shm_broadcast.py#L475-L478

The MessageQueue.broadcast_object() method is called by the GroupCoordinator.broadcast_object() method in parallel_state.py:

https://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/distributed/parallel_state.py#L364-L366

The broadcast over ZeroMQ is only done if the GroupCoordinator was created with use_message_queue_broadcaster set to True:

https://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/distributed/parallel_state.py#L216-L219

The only case where GroupCoordinator is created with use_message_queue_broadcaster is the coordinator for the tensor parallelism group:

https://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/distributed/parallel_state.py#L931-L936

To determine what data is broadcasted to the tensor parallism group, we must continue tracing. GroupCoordinator.broadcast_object() is called by GroupCoordinator.broadcoast_tensor_dict():

https://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/distributed/parallel_state.py#L489

which is called by broadcast_tensor_dict() in communication_op.py:

https://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/distributed/communication_op.py#L29-L34

If we look at _get_driver_input_and_broadcast() in the V0 worker_base.py, we'll see how this tensor dict is formed:

https://github.com/vllm-project/vllm/blob/790b79750b596043036b9fcbee885827fdd2ef3d/vllm/worker/worker_base.py#L332-L352

but the data actually sent over ZeroMQ is the metadata_list portion that is split from this tensor_dict. The tensor parts are sent via torch.distributed and only metadata about those tensors is sent via ZeroMQ.

https://github.com/vllm-project/vllm/blob/54a66e5fee4a1ea62f1e4c79a078b20668e408c6/vllm/distributed/parallel_state.py#L61-L83

Patches

• https://github.com/vllm-project/vllm/pull/17197

Workarounds

Prior to the fix, your options include:
1. Do not expose the vLLM host to a network where any untrusted connections may reach the host.
2. Ensure that only the other vLLM hosts are able to connect to the TCP port used for the XPUB socket. Note that port used is random.

References

• Relevant code first introduced in https://github.com/vllm-project/vllm/pull/6183