pySCG: Avoid pickle in most cases or at least warn

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#4 18 comentarios 1 reacción 0 asignados Ver en GitHub

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Evaluación

Dificultad
2/5
Tiempo estimado
1-3 horas
Aptitud para principiantes
54/100
Tipo de issue
Documentación
Claridad
Bastante claro
Estado de actividad
Tranquilo
Stack tecnológico
python
Área
documentation, security

Línea de trabajo

Start by finding the guide section that discusses pickle and compare its guidance with the linked Python documentation and the issue’s examples of safer alternatives. Add a clear warning about unpickling untrusted data and list practical alternatives or protections; done when readers can identify the risk and see safer options. The issue does not name a specific file or test.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

The Python3 pickle documentation says the following:

The pickle module is not secure. Only unpickle data you trust. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling. Never unpickle data that could have come from an untrusted source, or that could have been tampered with. Consider signing data with hmac if you need to ensure that it has not been tampered with. Safer serialization formats such as json may be more appropriate if you are processing untrusted data. See Comparison with json.

However, many researchers and developers are quite clueless about the problem. Paws in the Pickle Jar: Risk & Vulnerability in the Model-sharing Ecosystem reports that, "Our analysis shows that more than 80% of the evaluated machine learning models in the ecosystem consist of pickle-serialized code, which is vulnerable to code injection / arbitrary code execution risks... The pickle module serializes the underlying source code into a byte stream that can’t be statically interpreted back to its true intention and functionality. This requires the user to implicitly trust the contents of the model file uploaded by the author. For this reason, HuggingFace encourages safe tensors as a replacement for PyTorch models, and Splunk supports import of ONNX-formatted models".

Providing a clear note about the problem, and listing several solutions, would be a big help.

Lenguaje dominante
Python
Estrellas
2
Forks
2
Merge medio
11 min
PR fusionados (30 d)
2

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

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