Instructions to use wulonchia/modelscan-nested-lambda-bypass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use wulonchia/modelscan-nested-lambda-bypass with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://wulonchia/modelscan-nested-lambda-bypass") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| # ModelScan Nested Keras Lambda Layer Detection Bypass — PoC | |
| This repository contains a proof-of-concept `.keras` model file for a detection | |
| bypass in [protectai/modelscan](https://github.com/protectai/modelscan) (v0.8.6). | |
| ## File | |
| - `nested_lambda_poc.keras` — a Keras model whose malicious `Lambda` layer is | |
| nested inside an inner `Sequential` sub-model. | |
| ## Behavior | |
| - ModelScan 0.8.6 only inspects the top-level `config.layers` list, so it reports | |
| **"No issues found"** for this file. | |
| - An identical Lambda at the top level is correctly flagged as MEDIUM severity. | |
| - The nested Lambda still executes arbitrary code at `keras.models.load_model()` time. | |
| This PoC is shared privately (gated, manual review) for vulnerability disclosure | |
| purposes only. | |