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
File size: 1,139 Bytes
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## 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:
```text
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
- [x] Upload all files in this directory to a public Hugging Face repo.
- [x] 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.
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