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
Huntr Form Copy
Target
Keras Native (.keras)
Title
ModelScan Keras V3 scanner misses TorchModuleWrapper unsafe deserialization surface in .keras files
Hugging Face PoC
https://huggingface.co/fsabiu/keras-modelscan-torchmodulewrapper-coverage-gap
Description
Use the full local draft:
01-mfv-model-file-vulnerabilities/report-drafts/F-MFV-001-modelscan-torchmodulewrapper-gap.md
Short Impact Statement
ModelScan 0.8.8 returns a clean scan for a Keras V3 .keras file containing TorchModuleWrapper, while Keras 3.14.0 blocks the same class in safe_mode=True because it can deserialize a torch.nn.Module through torch.load(). The same ModelScan setup correctly flags a benign Lambda positive control, so this is a targeted scanner coverage gap rather than a broken scanner installation.
Upload Checklist
- Upload all files in this directory to a public Hugging Face repo.
- Confirm Hugging Face SHA256 matches
SHA256SUMS.txt. - Paste repo URL into the Huntr form.
- Submit as scanner coverage gap / scanner bypass.
- Do not present as a new Keras runtime RCE.