Instructions to use JohnYossarian1961/keras-nested-lambda-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use JohnYossarian1961/keras-nested-lambda-poc with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://JohnYossarian1961/keras-nested-lambda-poc") - Notebooks
- Google Colab
- Kaggle
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Download README.md from JohnYossarian1961/keras-nested-lambda-poc: direct link, hf CLI and curl.
- Browser
- Download file 702 Bytes
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https://huggingface.co/JohnYossarian1961/keras-nested-lambda-poc/resolve/main/README.md
- Command line
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hf download hf://JohnYossarian1961/keras-nested-lambda-poc/README.md
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curl -L -o README.md https://huggingface.co/JohnYossarian1961/keras-nested-lambda-poc/resolve/main/README.md
702 Bytes
metadata
tags:
- security
- proof-of-concept
license: mit
Keras Nested Lambda PoC — ModelScan Scanner Bypass
This repository contains a proof-of-concept demonstrating that ModelScan's fails to detect Lambda layers nested inside sub-models in Keras files.
Vulnerability
Target: ModelScan (protectai/modelscan)
Scanner:
Severity: Medium
The scanner only inspects the top-level array in . If a Lambda layer is nested inside a sub-model (a model-as-a-layer pattern), the scanner never recurses and the Lambda goes undetected.
PoC Structure
Reproduction
Root Cause
only iterates one level:
A recursive scan of nested model configs would close this gap.