LMDeploy has Remote Code Execution by Pickle Deserialization via handle_zmq_recv in lmdeploy/lmdeploy/pytorch/disagg/conn/engine_conn.py

LMDeploy's PyTorch DistServe/PD-disaggregation control plane used recv_pyobj() to deserialize messages received through a ZeroMQ PULL socket. PyZMQ implements recv_pyobj() using Python pickle deserialization, which can execute arbitrary code while reconstructing an object.

The peer address used by the receiver was supplied through the POST /distserve/p2p_connect HTTP endpoint. An attacker who could reach an affected DistServe API server could cause the server to connect to an attacker-controlled ZeroMQ endpoint and deserialize a crafted pickle payload.

API-key authentication is not enabled unless the operator explicitly configures it. As a result, affected DistServe deployments without API keys allowed unauthenticated remote code execution with the privileges of the LMDeploy serving process.

This issue affects the PyTorch backend when PD-disaggregation/DistServe is enabled. Ordinary deployments that do not use the affected disaggregated-serving path do not expose this data flow.

Affected components

• HTTP entry point: lmdeploy/serve/openai/endpoints/distserve.py, POST /distserve/p2p_connect
• Attacker-controlled peer address: DistServeConnectionRequest.remote_engine_endpoint_info.zmq_address
• Vulnerable receiver: lmdeploy/pytorch/disagg/conn/engine_conn.py, EngineP2PConnection.handle_zmq_recv()
• Unsafe operation: recv_pyobj(), which performs pickle deserialization

Vulnerable data flow

1. A caller submits a DistServe P2P connection request containing a ZeroMQ address.
2. The LMDeploy engine connects its ZeroMQ PULL socket to that address.
3. handle_zmq_recv() receives messages using recv_pyobj().
4. A malicious peer sends a crafted pickle object.
5. Python code executes during deserialization, before LMDeploy can perform any type or field validation.

A type check performed after recv_pyobj() cannot mitigate this issue because pickle payload execution occurs during deserialization.

Impact

Successful exploitation allows arbitrary code execution as the LMDeploy serving process. This can expose model weights, prompts, credentials, attached storage, cluster-network services, and host or GPU resources. An attacker may also modify or terminate the serving process.

Affected versions

Affected versions:

• lmdeploy >= 0.9.2, < 0.16.0

The vulnerable P2P receiver was introduced in commit b0b705f7.

Remediation

The issue was fixed by replacing the pickle-based ZeroMQ protocol with JSON serialization:

• send_pyobj() was replaced with send_json().
• recv_pyobj() was replaced with recv_json().
• Received objects are validated using the DistServeCacheFreeRequest Pydantic schema before use.
• Invalid or off-schema messages are rejected without terminating the receive loop.

Fix commit:

https://github.com/InternLM/lmdeploy/commit/f05b4ad8bf2e2d84101a1d63b3c44fadd99223b2

The fix was released in LMDeploy 0.16.0.

Workarounds

Users who cannot upgrade immediately should:

• Prevent untrusted clients from reaching /distserve/* endpoints.
• Restrict the DistServe HTTP and ZeroMQ control planes to trusted cluster networks.
• Configure API-key authentication.
• Block arbitrary outbound ZeroMQ connections from serving nodes.

These measures reduce exposure but do not make pickle deserialization safe. Upgrading to LMDeploy 0.16.0 or later is recommended.