CVE-2024-48063 in PyTorch
Summary
by MITRE • 10/29/2024
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.
Once again VulDB remains the best source for vulnerability data.
Analysis
by VulDB Data Team • 07/16/2025
The vulnerability identified as CVE-2024-48063 affects PyTorch versions 2.4.1 and earlier, specifically within the RemoteModule component that handles distributed computing operations. This issue presents a deserialization remote code execution vulnerability that could potentially allow attackers to execute arbitrary code on systems running vulnerable PyTorch versions. The RemoteModule functionality is designed to enable distributed computing across multiple nodes, allowing modules to be executed remotely and synchronized across different processes or machines. The vulnerability arises from insufficient validation during the deserialization process of remote module data structures, creating an attack surface where maliciously crafted serialized objects could be interpreted and executed by the receiving system. This represents a critical security concern in distributed machine learning environments where PyTorch is used for collaborative training or inference across multiple nodes.
The technical flaw stems from the improper handling of serialized data within PyTorch's distributed computing framework. When RemoteModule processes incoming serialized objects from remote nodes, the deserialization mechanism lacks adequate safeguards against malicious input. This vulnerability aligns with CWE-502, which describes deserialization of untrusted data as a common weakness leading to remote code execution. The flaw exists because the system accepts serialized objects without sufficient validation of their contents or origin, allowing attackers to craft malicious payloads that exploit the deserialization process. Attackers could potentially inject malicious code into serialized objects that would then be executed when the RemoteModule deserializes them, effectively providing a backdoor for remote code execution. The vulnerability is particularly dangerous in distributed environments where multiple nodes communicate and share serialized module states.
The operational impact of this vulnerability extends beyond simple code execution, as it fundamentally compromises the security posture of distributed machine learning systems. In production environments using PyTorch for distributed computing, this vulnerability could enable attackers to gain full control over worker nodes, potentially leading to data exfiltration, model poisoning, or disruption of distributed training processes. The attack vector is particularly concerning because it can be exploited through legitimate network communication channels that are typically allowed in distributed computing setups. This vulnerability affects organizations using PyTorch in cloud environments, research institutions running distributed training clusters, or enterprises deploying machine learning models across multiple nodes. The fact that this is considered intended behavior by some parties within the PyTorch community creates additional complexity for security teams who must balance legitimate distributed computing requirements with security controls.
Security mitigations for CVE-2024-48063 require immediate attention from organizations using vulnerable PyTorch versions. The primary recommendation is to upgrade to PyTorch version 2.5.0 or later where this vulnerability has been addressed through enhanced deserialization validation. Organizations should also implement network-level controls to restrict communication between PyTorch nodes, limiting exposure through firewalls and network segmentation. Additional mitigations include implementing strict authentication and encryption for distributed communication channels, monitoring for unusual deserialization activities, and conducting regular security assessments of distributed computing environments. From an ATT&CK framework perspective, this vulnerability maps to techniques involving remote code execution and privilege escalation through legitimate system tools. Organizations should also consider implementing runtime application protection measures and code integrity checks to prevent exploitation of this vulnerability even if other controls fail. The disputed nature of this vulnerability by PyTorch maintainers highlights the importance of understanding the intended security boundaries of distributed computing frameworks and implementing appropriate defensive measures regardless of vendor positions on the issue.