PyTorch Lightning Exposes Users to Remote Code Execution via...
Blog post from Socket
PyTorch Lightning, a popular deep learning framework, has been found to have multiple critical deserialization vulnerabilities that could lead to remote code execution when loading untrusted model files, affecting versions up to 2.4.0. These vulnerabilities, disclosed under VU#252619 and reported by Kasimir Schulz of HiddenLayer, were coordinated by the CERT Coordination Center at Carnegie Mellon University. They involve insecure deserialization pathways within the framework’s checkpointing, distributed training, and I/O components, primarily using `torch.load()` and Python’s `pickle`. The widespread use of PyTorch Lightning in research and production environments increases the risk of these vulnerabilities, potentially allowing attackers to execute malicious code through crafted model files, which could compromise system security and data integrity. Until a patch is released, CERT/CC recommends strict trust boundaries, restricted deserialization modes, sandboxed environments, inspection of serialized files, and auditing of automation tools to mitigate risks. As of the report's publication, Lightning AI has not acknowledged the vulnerabilities or provided patches, and users are advised to monitor official channels for updates.
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