LF Security Corpus: Model Format Mutation Matrix
Collection
Safetensors, GGUF, ONNX, TFLite, Keras and OpenVINO structure mutations. • 18 items • Updated
format-keras-custom-lambda
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-CH-FMTX-0015
Purpose
Format keras custom lambda.
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.
Challenge classification
- Severity: critical
- Difficulty: adversarial
- Expected admission decision: BLOCK
- Control type: positive
- Attack surface: model-format-structure
- Techniques: keras
- Transformations: none
Ground-truth oracle IDs
LF-ORACLE-FMTX-0015
These oracle IDs describe synthetic ground truth. They do not claim that a matching Layerfault detector already exists. A challenge may intentionally expose a scanner blind spot and remain unmapped until the detector is implemented.
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# !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/format-keras-custom-lambda")