ml-engineering EUVD-2026-29498

Update the torch-checkpoint-shrink.py script by adding weights_only=True parameter to all torch.load() calls, specifically at line 57 where the vulnerability exists. Modify the code from torch.load(file_path) to torch.load(file_path, weights_only=True) to restrict deserialization to tensor data only. No formal patched release version exists as this is a script repository-users must manually apply the fix by editing their local copy or pulling updated commits if the maintainer has released a fix. As an immediate compensating control, implement input validation to process checkpoint files only from trusted, cryptographically verified sources such as signed model registries. Deploy the script in sandboxed execution environments (containers with no network access, restricted filesystem permissions, non-privileged user accounts) to limit blast radius of successful exploitation-note this reduces but does not eliminate risk as code execution still occurs within the sandbox context. For production ML pipelines, implement checkpoint integrity verification using cryptographic signatures before processing, and maintain an allowlist of approved checkpoint sources. Audit all PyTorch-using scripts for similar torch.load() calls without weights_only=True. Trade-off: weights_only=True may break compatibility with checkpoints containing custom objects or optimizer states-test thoroughly in development before production deployment.