Instructions to use LayerFault/keras-clean-control with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use LayerFault/keras-clean-control with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://LayerFault/keras-clean-control") - Notebooks
- Google Colab
- Kaggle
Publish Layerfault synthetic corpus LF-CORPUS-KERAS-0002
Browse files- LAYERFAULT_CORPUS.json +21 -0
- README.md +55 -0
- SHA256SUMS.json +17 -0
- model.keras +0 -0
LAYERFAULT_CORPUS.json
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{
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"candidate_rules": [],
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"corpus_id": "LF-CORPUS-KERAS-0002",
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"description": "Minimal structurally valid Keras archive without custom objects.",
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"expected_rules": [
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"LF-KERAS-STRUCT-VALID"
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],
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"generator": "formats.keras_clean",
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"ground_truth": "synthetic-generated",
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"must_not_rules": [
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"LF-KERAS-CUSTOM-OBJECT"
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],
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"repo_name": "keras-clean-control",
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"repo_type": "model",
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"safety": "inert/canary-only",
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"schema": 1,
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"validation": {
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"kind": "inspect",
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"target": "model.keras"
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}
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}
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README.md
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---
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license: apache-2.0
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tags:
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- layerfault
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- security-research
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- model-security
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- synthetic
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- adversarial-testing
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extra_gated_prompt: >-
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This repository is a synthetic security-test artifact from the Layerfault
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corpus. It intentionally contains adversarial characteristics (e.g.
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suspicious pickle opcodes, executable-format smuggling, prompt-injection
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strings) designed to exercise security scanner detection rules. It is
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**not** a usable ML model and must never be loaded or executed outside an
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isolated scanner-testing environment. By accepting, you confirm you
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understand this repository is a test fixture, not production model
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weights.
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extra_gated_button_content: I understand this is a security test fixture and accept the risk
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gated: auto
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---
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# keras-clean-control
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> **SECURITY TEST ARTIFACT — DO NOT USE AS A PRODUCTION MODEL**
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This repository is part of the Layerfault synthetic security corpus.
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It is deliberately constructed to contain security-relevant characteristics for scanner testing.
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**Corpus ID:** `LF-CORPUS-KERAS-0002`
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## Purpose
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Minimal structurally valid Keras archive without custom objects.
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## Direct expected Layerfault rules
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- `LF-KERAS-STRUCT-VALID`
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## Candidate rules
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These are deliberately plausible targets that remain marked as candidates until the exact
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Layerfault build used for certification confirms them.
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- None
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## Negative-control rules
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These should remain silent for this corpus item.
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- `LF-KERAS-CUSTOM-OBJECT`
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## Safety
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The corpus uses fake secrets, loopback/`.invalid` network destinations, harmless marker output,
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and synthetic model behavior only. It is intended for static scanning and isolated security testing.
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SHA256SUMS.json
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[
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{
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"path": "LAYERFAULT_CORPUS.json",
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"sha256": "5d120b5de98f211bc7f2af8959aa7ef6b20e004b8995124acd57313140839f26",
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"size": 526
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},
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{
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"path": "README.md",
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"sha256": "44082675aea222386e75fb042255a12e20767537bf0eb87a0f271c3e1b9d08d2",
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"size": 1691
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},
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{
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"path": "model.keras",
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"sha256": "02d201bdcbaf9157a3ee647815c89cd374cd2b71e6173bca61f36570c47d62b0",
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"size": 331
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}
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]
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model.keras
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Binary file (331 Bytes). View file
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