Instructions to use LayerFault/keras-custom-object with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use LayerFault/keras-custom-object 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://LayerFault/keras-custom-object") - Notebooks
- Google Colab
- Kaggle
Download README.md from LayerFault/keras-custom-object: direct link, hf CLI and curl.
- Browser
- Download file 1.67 kB
-
https://huggingface.co/LayerFault/keras-custom-object/resolve/main/README.md
- Command line
-
hf download hf://LayerFault/keras-custom-object/README.md
-
curl -L -o README.md https://huggingface.co/LayerFault/keras-custom-object/resolve/main/README.md
license: apache-2.0
tags:
- layerfault
- security-research
- model-security
- synthetic
- adversarial-testing
extra_gated_prompt: >-
This repository is a synthetic security-test artifact from the Layerfault
corpus. It intentionally contains adversarial characteristics (e.g. suspicious
pickle opcodes, executable-format smuggling, prompt-injection strings)
designed to exercise security scanner detection rules. It is **not** a usable
ML model and must never be loaded or executed outside an isolated
scanner-testing environment. By accepting, you confirm you understand this
repository is a test fixture, not production model weights.
extra_gated_button_content: I understand this is a security test fixture and accept the risk
gated: auto
keras-custom-object
SECURITY TEST ARTIFACT: DO NOT USE AS A PRODUCTION MODEL
This repository is part of the Layerfault synthetic security corpus. It is deliberately constructed to contain security-relevant characteristics for scanner testing.
Corpus ID: LF-CORPUS-KERAS-0001
Purpose
Minimal Keras ZIP containing a custom/Lambda-like object reference.
Direct expected Layerfault rules
LF-KERAS-CUSTOM-OBJECT
Candidate rules
These are deliberately plausible targets that remain marked as candidates until the exact Layerfault build used for certification confirms them.
- None
Negative-control rules
These should remain silent for this corpus item.
- None
Safety
The corpus uses fake secrets, loopback/.invalid network destinations, harmless marker output,
and synthetic model behavior only. It is intended for static scanning and isolated security testing.