picklescan before 1.0.3 contains a scanning bypass vulnerability in the scan_pytorch function that allows attackers to embed malicious magic numbers via dynamic eval using the __reduce__ trick. Attackers can craft malicious PyTorch payloads that evade picklescan detection while remaining executable, enabling arbitrary code execution when loaded with torch.load().

Affected: PyPI: picklescan

### Summary This is a scanning bypass to `scan_pytorch` function in `picklescan`. As we can see in the implementation of [get_magic_number()](https://github.com/mmaitre314/picklescan/blob/2a8383cfeb4158567f9770d86597300c9e508d0f/src/picklescan/torch.py#L76C5-L84) that uses `pickletools.genops(data)` to get the `magic_number` with the condition `opcode.name` includes `INT` or `LONG`, but the PyTorch's implemtation simply uses [pickle_module.load()](https://github.com/pytorch/pytorch/blob/134179474539648ba7dee1317959529fbd0e7f89/torch/serialization.py#L1797) to get this `magic_number`. For this implementation difference, we then can embed the `magic_code` into the `PyTorch` file via dynamic `eval` on the `\_\_reduce\_\_` trick, which can make the `pickletools.genops(data)` cannot get the `magic_code` in `INT` or `LONG` type, but the `pickle_module.load()` can still return the same `magic_code`, eading to a bypass.

### PoC #### Attack Step 1 we can edit the source code of the function [\_legacy\_save()](https://github.com/pytorch/pytorch/blob/134179474539648ba7dee1317959529fbd0e7f89/torch/serialization.py#L1120) as follows: ```Python class payload: def __reduce__(self): return (eval, ('MAGIC_NUMBER',))

pickle_module.dump(payload(), f, protocol=pickle_protocol) ``` #### Attack Step 2 with the modified version of `PyTorch`, we run the following PoC to generate the `payload.pt`: ```Python import torch

class payload: def __reduce__(self):…

Affected: PyPI: picklescan

### Summary This is a scanning bypass to `scan_pytorch` function in `picklescan`. As we can see in the implementation of [get_magic_number()](https://github.com/mmaitre314/picklescan/blob/2a8383cfeb4158567f9770d86597300c9e508d0f/src/picklescan/torch.py#L76C5-L84) that uses `pickletools.genops(data)` to get the `magic_number` with the condition `opcode.name` includes `INT` or `LONG`, but the PyTorch's implemtation simply uses [pickle_module.load()](https://github.com/pytorch/pytorch/blob/134179474539648ba7dee1317959529fbd0e7f89/torch/serialization.py#L1797) to get this `magic_number`. For this implementation difference, we then can embed the `magic_code` into the `PyTorch` file via dynamic `eval` on the `\_\_reduce\_\_` trick, which can make the `pickletools.genops(data)` cannot get the `magic_code` in `INT` or `LONG` type, but the `pickle_module.load()` can still return the same `magic_code`, eading to a bypass.

### PoC #### Attack Step 1 we can edit the source code of the function [\_legacy\_save()](https://github.com/pytorch/pytorch/blob/134179474539648ba7dee1317959529fbd0e7f89/torch/serialization.py#L1120) as follows: ```Python class payload: def __reduce__(self): return (eval, ('MAGIC_NUMBER',))

pickle_module.dump(payload(), f, protocol=pickle_protocol) ``` #### Attack Step 2 with the modified version of `PyTorch`, we run the following PoC to generate the `payload.pt`: ```Python import torch

class payload: def __reduce__(self):…