Описание
A vulnerability in PyTorch's torch.distributed.rpc framework, specifically in versions prior to 2.2.2, allows for remote code execution (RCE). The framework, which is used in distributed training scenarios, does not properly verify the functions being called during RPC (Remote Procedure Call) operations. This oversight permits attackers to execute arbitrary commands by leveraging built-in Python functions such as eval during multi-cpu RPC communication. The vulnerability arises from the lack of restriction on function calls when a worker node serializes and sends a PythonUDF (User Defined Function) to the master node, which then deserializes and executes the function without validation. This flaw can be exploited to compromise master nodes initiating distributed training, potentially leading to the theft of sensitive AI-related data.
Отчет
Current investigation indicates that the packages are only present in Fedora and Fedora Rawhide within the Red Hat community ecosystem. No versions of Red Hat Enterprise Linux (RHEL) are affected as they do not ship packages related to PyTorch framework.
Дополнительная информация
Статус:
10 Critical
CVSS3
Связанные уязвимости
Rejected reason: This CVE ID has been rejected or withdrawn by its CVE Numbering Authority.
Rejected reason: This CVE ID has been rejected or withdrawn by its CVE Numbering Authority.
A vulnerability in the PyTorch's torch.distributed.rpc framework, specifically in versions prior to 2.2.2, allows for remote code execution (RCE). The framework, which is used in distributed training scenarios, does not properly verify the functions being called during RPC (Remote Procedure Call) operations. This oversight permits attackers to execute arbitrary commands by leveraging built-in Python functions such as eval during multi-cpu RPC communication. The vulnerability arises from the lack of restriction on function calls when a worker node serializes and sends a PythonUDF (User Defined Function) to the master node, which then deserializes and executes the function without validation. This flaw can be exploited to compromise master nodes initiating distributed training, potentially leading to the theft of sensitive AI-related data.
Уязвимость компонента удалённого вызова процедур RPC фреймворка машинного обучения PyTorch, позволяющая нарушителю выполнить произвольный код
10 Critical
CVSS3