CVE-2026-24747: RCE in PyTorch Tensor Computation

The impact of this vulnerability is significant. Successful exploitation allows an attacker to execute arbitrary code on the system running the PyTorch application. This could lead to complete system compromise, data theft, or denial of service. The weights_only=True loading option, intended for efficient model deployment, introduces a deserialization vulnerability that can be exploited to overwrite memory regions. Attackers could potentially inject malicious code into the process, gaining control over the application and potentially the underlying system. The ease of crafting malicious checkpoint files, coupled with the widespread use of PyTorch in machine learning applications, increases the potential attack surface.

1. Reserved2026-01-26
2. Published2026-01-27
3. Modified2026-02-26
4. EPSS updated2026-03-23

The primary mitigation for CVE-2026-24747 is to upgrade to PyTorch version 2.10.0 or later, which contains the fix. If upgrading is not immediately feasible, consider restricting access to the torch.load function and carefully validating the source of any .pth files before loading them. Implement strict input validation and sanitization to prevent the loading of untrusted checkpoint files. While a WAF is unlikely to directly address this vulnerability, it can help prevent the upload of malicious .pth files. There are no specific Sigma or YARA rules readily available for this vulnerability, but monitoring for unusual memory access patterns during .pth file loading could be a potential detection strategy.