Instructions to use fsabiu/keras-modelscan-torchmodulewrapper-coverage-gap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use fsabiu/keras-modelscan-torchmodulewrapper-coverage-gap with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://fsabiu/keras-modelscan-torchmodulewrapper-coverage-gap") - Notebooks
- Google Colab
- Kaggle
Update Huntr form notes with public PoC URL
Browse files- HUNTR_FORM.md +3 -10
HUNTR_FORM.md
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## Hugging Face PoC
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Recommended repo name:
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```text
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keras-modelscan-torchmodulewrapper-coverage-gap
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```
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## Description
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## Upload Checklist
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- [ ] Paste repo URL into the Huntr form.
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- [ ] Submit as scanner coverage gap / scanner bypass.
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- [ ] Do not present as a new Keras runtime RCE.
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## Hugging Face PoC
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https://huggingface.co/fsabiu/keras-modelscan-torchmodulewrapper-coverage-gap
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## Description
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## Upload Checklist
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- [x] Upload all files in this directory to a public Hugging Face repo.
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- [x] Confirm Hugging Face SHA256 matches `SHA256SUMS.txt`.
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- [ ] Paste repo URL into the Huntr form.
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- [ ] Submit as scanner coverage gap / scanner bypass.
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- [ ] Do not present as a new Keras runtime RCE.
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