GHSA-vr7h-p6mm-wpmh

GHSA-vr7h-p6mm-wpmh MEDIUM
Published August 22, 2025

### Summary Using torch.jit.unsupported_tensor_ops.execWrapper function, which is a pytorch library function to execute remote pickle file. ### Details The attack payload executes in the following...

Full analysis pending. Showing NVD description excerpt.

Affected Systems

Package Ecosystem Vulnerable Range Patched
picklescan pip <= 0.0.27 0.0.28

Do you use picklescan? You're affected.

Severity & Risk

CVSS 3.1
N/A
EPSS
N/A
KEV Status
Not in KEV
Sophistication
N/A

Recommended Action

Patch available

Update picklescan to version 0.0.28

Compliance Impact

Compliance analysis pending. Sign in for full compliance mapping when available.

Technical Details

NVD Description

### Summary Using torch.jit.unsupported_tensor_ops.execWrapper function, which is a pytorch library function to execute remote pickle file. ### Details The attack payload executes in the following steps: First, the attacker craft the payload by calling to torch.jit.unsupported_tensor_ops.execWrapper function in reduce method Then when the victim after checking whether the pickle file is safe by using Picklescan library and this library doesn't dectect any dangerous functions, decide to pickle.load() this malicious pickle file, thus lead to remote code execution. ### PoC ``` import torch.jit.unsupported_tensor_ops as unsupported_tensor_ops class EvilTorchJitUnsupportedTensorOpsExecWrapper: def __reduce__(self): code = '__import__("os").system("whoami")' glob = {} loc = {} return unsupported_tensor_ops.execWrapper, (code, glob, loc) ``` ### Impact Who is impacted? Any organization or individual relying on picklescan to detect malicious pickle files inside PyTorch models. What is the impact? Attackers can embed malicious code in pickle file that remains undetected but executes when the pickle file is loaded. Supply Chain Attack: Attackers can distribute infected pickle files across ML models, APIs, or saved Python objects. ### Corresponding https://github.com/FredericDT https://github.com/Qhaoduoyu

Timeline

Published
August 22, 2025
Last Modified
August 22, 2025
First Seen
March 24, 2026