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:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://JohnYossarian1961/keras-nested-lambda-poc") - Notebooks
- Google Colab
- Kaggle
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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.
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