Artificial intelligence systems worldwide depend critically on frameworks like PyTorch for functionality and efficiency. Recently, a major vulnerability has been discovered in PyTorch, specifically in the torch.load() function. This vulnerability, identified as CVE-2025-32434, allows attackers to execute arbitrary code on systems that load AI models. This flaw affects all PyTorch versions up to 2.5.1. The PyTorch team has already released a fixed version, 2.6.0, rectifying this dangerous loophole.
Unveiling the Vulnerability in PyTorch
The vulnerability in PyTorch stems from a flaw in the torch.load() function, which is used for loading serialized AI models. Security researcher Ji’an Zhou revealed that the weights_only=True setting, previously considered a defense mechanism, can be bypassed, allowing attackers to load malicious models. This setting was believed to restrict loading to only model weights, preventing unsafe code execution. Unfortunately, this core assumption in PyTorch’s documentation has been proven incorrect, leaving systems using this framework exposed to potential threats.
Given the widespread use of torch.load(), especially in inference pipelines, federated learning systems, and model hubs, the implications are vast. Attackers could exploit this vulnerability by injecting tampered models into public repositories or supply chains, resulting in arbitrary code execution on victim systems. The ease of exploitation and the potential for full system compromise have led experts to label this vulnerability as critical. Organizations reliant on PyTorch must recognize the severity of this issue and take immediate action to safeguard their systems.
Mitigation and Future Prevention
To address this immediate threat, users are urged to upgrade to PyTorch version 2.6.0 without delay. This version includes a crucial patch that fixes the vulnerability. Additionally, it is imperative to audit existing models to ensure they originate from trusted sources. Regularly monitoring PyTorch’s GitHub Security page for updates and patches can help prevent future security lapses. The PyTorch team has acknowledged the vulnerability, emphasizing the necessity of these updates to secure model pipelines and protect against potential attacks, which could lead to data theft, service disruption, or resource hijacking.
Securing AI infrastructure requires continuous vigilance and scrutiny. Even trusted safeguards, as evidenced by the newfound flaw in the weights_only=True setting, can be compromised. With PyTorch’s widespread adoption by various entities, from startups to technological giants like Meta and Microsoft, it is paramount to remain diligent. This vulnerability serves as a potent reminder of the importance of continuously updating and verifying AI models to defend against evolving threats.
Ensuring Secure AI Practices
Artificial intelligence (AI) systems worldwide rely heavily on frameworks such as PyTorch for their performance and efficiency. Recently, a critical vulnerability was discovered in PyTorch, specifically within the torch.load() function. This security flaw, designated as CVE-2025-32434, permits attackers to execute arbitrary code on systems that load AI models using this function. This vulnerability impacts all versions of PyTorch up to and including 2.5.1. However, the PyTorch development team has addressed and rectified this issue by releasing an updated version, 2.6.0, which fixes this severe security loophole. To ensure system security, users of PyTorch are strongly advised to upgrade to version 2.6.0 without delay. This swift action will help mitigate the risk posed by this vulnerability, protect against potential exploits, and ensure the continued reliable functioning and safety of AI systems that depend on PyTorch.
