CVE-2024-5480 (CVSS 10): Critical RCE Vulnerability in PyTorch Distributed RPC Framework

A critical vulnerability (CVE-2024-5480) has been discovered in PyTorch’s distributed RPC (Remote Procedure Call) framework, exposing machine learning models and sensitive data to potential remote code execution (RCE) attacks. This flaw, identified by security researcher xbalien, carries a maximum severity rating (CVSS 10), underscoring the urgency of patching for organizations utilizing PyTorch in distributed training environments.

Exploiting the Flaw: A Recipe for Disaster

PyTorch’s distributed RPC framework is a powerful tool for scaling machine learning workloads across multiple machines. However, a critical oversight in its design has left it vulnerable to exploitation. The framework fails to adequately verify the authenticity of functions called during RPC communication, allowing malicious actors to execute arbitrary code on the master node orchestrating the training process.

By manipulating RPC calls, attackers can leverage built-in Python functions like eval or load external libraries, effectively gaining complete control over the master node. This can lead to the theft of sensitive AI models, training data, and other confidential information.

Widespread Impact and Urgent Remediation

The CVE-2024-5480 vulnerability affects PyTorch versions up to and including 2.2.2, impacting a significant portion of the PyTorch user base. Organizations and researchers using PyTorch for distributed training, especially in multi-CPU environments, are strongly advised to upgrade to version 2.2.3 or later immediately.

Protecting the AI Ecosystem

This discovery underscores the growing importance of security in the artificial intelligence (AI) landscape. As AI models and training data become increasingly valuable targets, vulnerabilities like this one highlight the need for robust security measures throughout the development and deployment lifecycle.

Organizations must prioritize regular updates, thorough testing, and proactive monitoring to identify and mitigate vulnerabilities before they can be exploited. The responsible disclosure by xbalien and PyTorch’s swift response demonstrate the critical role of collaboration between researchers and developers in securing the AI ecosystem.