A vulnerability, which was classified as problematic, has been found in PyTorch 2.6.0. This issue affects the function torch.cuda.memory.cachingallocatordelete of the file c10/cuda/CUDACachingAllocator.cpp. The manipulation leads to memory corruption. An attack has to be approached locally. The exploit has been disclosed to the public and may be used.

A vulnerability classified as problematic has been found in PyTorch 2.6.0. Affected is the function torch.jit.jitmodulefromflatbuffer. The manipulation leads to memory corruption. Local access is required to approach this attack. The exploit has been disclosed to the public and may be used.

A vulnerability classified as critical has been found in PyTorch 2.6.0. This affects the function torch.jit.script. The manipulation leads to memory corruption. It is possible to launch the attack on the local host. The exploit has been disclosed to the public and may be used.

Withdrawn Advisory This advisory has been withdrawn because it describes known functionality of PyTorch. This link is maintained to preserve external references.

Original Description A deserialization vulnerability exists in the Pytorch RPC framework (torch.distributed.rpc) in pytorch/pytorch versions <=2.3.1. The vulnerability arises from the lack of security verification during the deserialization process of PythonUDF objects in pytorch/torch/distributed/rpc/internal.py. This flaw allows an attacker to execute arbitrary code remotely by sending a malicious serialized PythonUDF object, leading to remote code execution (RCE) on the master node.

In PyTorch <=2.4.1, the RemoteModule has Deserialization RCE. NOTE: this is disputed by multiple parties because this is intended behavior in PyTorch distributed computing.