Instructions to use hacnho/keras-set-floatx-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use hacnho/keras-set-floatx-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://hacnho/keras-set-floatx-poc") - Notebooks
- Google Colab
- Kaggle
Keras set_floatx dead-node proof of concept
This repository contains a bounded .keras proof of concept for the existing
Keras dead Functional-node / NON_MODELING_APIS trust-boundary family.
Primary artifact:
dead_switch_keras_config_set_floatx.keras
It calls:
keras.config.set_floatx("float64")
during keras.saving.load_model(..., safe_mode=True).
Expected effect
The malicious archive loads successfully, but later benign helper APIs drift:
floatx:float32 -> float64keras.ops.ones(...).dtype:float32 -> float64keras.random.uniform(...).dtype:float32 -> float64
A simple Dense example may remain float32, so this is a selective downstream
helper-API drift rather than a universal model-output mutation.
Files
dead_switch_keras_config_set_floatx.kerasverify_set_floatx_remote.pyrequirements.txtSHA256SUMS.txt
Reproduce
python3 -m venv /tmp/keras-set-floatx-poc
. /tmp/keras-set-floatx-poc/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
python verify_set_floatx_remote.py
Expected result:
load_result = ok:Functionalbefore.floatx = float32after.floatx = float64before.ops_ones_dtype = float32after.ops_ones_dtype = float64before.random_uniform_dtype = float32after.random_uniform_dtype = float64
Safety note
This is a bounded same-family strengthening artifact:
- no ACE claim
- no network or file-write side effect required to observe the drift
- intended as evidence for an existing Keras MFV root-cause family, not a new fresh report
- Downloads last month
- 29
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support