Critical PyTorch Vulnerability CVE-2025-32434 Allows Remote Code Execution

This security issue in PyTorch gives an attacker the ability to achieve remote code execution by tricking a system into loading a specially crafted malicious model file, even when developers use the weights_only=True setting in torch.load() which is typically considered a safer option. The following protection guardrails from BlueRock can further prevent the following steps an attacker can take: When the malicious model file is loaded and attempts to execute arbitrary commands through the vulnerable Python torch.load() function, Python OS Command Injection Prevention steps in to block these unauthorized OS commands directly within the Python process, preventing the initial code execution that could lead to system compromise. Should an attacker's initial payload, after bypassing the intended weights_only=True safeguard, attempt to run from an unexpected or temporary location on the system where the malicious model might have been saved, Process Path Exec Allow ensures that only applications from approved, legitimate paths can be executed, thereby stopping malware or unauthorized tools dropped in unusual directories from running. If the PyTorch application is running within a containerized environment, and the attacker, having achieved initial execution, tries to introduce new malicious binaries or scripts not part of the original trusted container image—for instance, to establish persistence or escalate privileges—Container Drift Protection (Binaries & Scripts) would prevent these unauthorized executables from running, maintaining the integrity of the container. Following a successful command execution, if the attacker attempts to establish a persistent, interactive connection back to their own server by initiating a reverse shell from the compromised machine to gain direct control, Reverse Shell Protection actively detects and blocks this common post-exploitation technique. Furthermore, if the attacker's executed code attempts to exfiltrate stolen data, such as sensitive model parameters or proprietary datasets, or tries to move laterally by connecting to other systems within the network (e.g., to access cloud metadata services for credentials or other internal AI development servers), Process Socket Deny restricts the compromised process from making unauthorized network connections, effectively containing the threat and preventing data breaches or further network compromise.

• T1059: Command and Scripting Interpreter: The article describes a vulnerability in the PyTorch framework, specifically in the torch.load() function when used with the parameter weights_only=True. This allows an attacker to execute arbitrary commands on the target machine, which is indicative of Remote Code Execution. The attacker can craft a model file designed to exploit this flaw, thereby achieving execution of commands remotely on the victim's system. This aligns with the MITRE ATT&CK technique for Command and Scripting Interpreter (T1059), as it involves executing commands on a system.
• T1204: User Execution: The article mentions that malicious actors can exploit the vulnerability by crafting a model file designed to exploit the flaw in the torch.load() function. This involves modifying or creating files to achieve execution of arbitrary commands. This aligns with the MITRE ATT&CK technique for User Execution (T1204), as it requires the user to load a malicious model file which then executes the attacker's code.
• T1570: Lateral Tool Transfer: The vulnerability allows for remote command execution, which can lead to data breaches, system compromise, or lateral movement in cloud-hosted AI environments. This suggests that an attacker could use this flaw to move laterally within a network or cloud environment after initial compromise. This aligns with the MITRE ATT&CK technique for Lateral Movement (T1570), as it involves moving through a network after gaining initial access.

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