CVE-2026-58659

PyTorch Lightning through 2.6.5, fixed in commit d710d68, contains a remote code execution vulnerability in the loadstate function that imports and executes attacker-controlled module names from checkpoint instantiator hyperparameters. Attackers can craft malicious checkpoint files that bypass...

CVE-2026-58659

PyTorch Lightning through 2.6.5, fixed in commit d710d68, contains a remote code execution vulnerability in the loadstate function that imports and executes attacker-controlled module names from checkpoint instantiator hyperparameters. Attackers can craft malicious checkpoint files that bypass...

EUVD-2026-44751

PyTorch Lightning through 2.6.5, fixed in commit d710d68, contains a remote code execution vulnerability in the loadstate function that imports and executes attacker-controlled module names from checkpoint instantiator hyperparameters. Attackers can craft malicious checkpoint files that bypass...

CVE-2026-31221

PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability CWE-502 in the checkpoint loading mechanism. The LightningModule.loadfromcheckpoint method, which is commonly used to load saved model states, internally calls torch.load without setting the...

Deserialization of Untrusted Data

Overview pytorch-lightning is a lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate. Affected versions of this package are vulnerable to Deserialization of Untrusted Data in the LightningModule.loadfromcheckpoint function. Any workflow that calls this functio...

Pytorch-Lightning 安全漏洞

PyTorch-Lightning is an open-source lightweight PyTorch wrapper developed by Lightning AI in the United States. It is used for high-performance AI research. Versions of PyTorch-Lightning prior to 2.6.0 contain security vulnerabilities. These vulnerabilities stem from the...

CVE-2026-31221

PyTorch-Lightning versions 2.6.0 and earlier contain an insecure deserialization vulnerability (CWE-502) in the checkpoint loading mechanism. The LightningModule.load_from_checkpoint() (and related checkpoint loading paths) call torch.load() without weights_only=True, allowing deserialization of ...