Update README.md

README.md

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+license: mit
+short_description: 'Security admission and adversarial test corpus for local AI '
 ---
+LayerFault
+
+Security admission for local AI models.
+
+LayerFault is an offline-first security scanner and admission layer for AI models, model packages, repositories and runtimes.
+
+This Hugging Face organisation hosts the LayerFault Security Corpus, a growing adversarial test corpus for validating AI model security scanners, admission controls and model-handling pipelines.
+
+These repositories are security test fixtures, not models intended for inference.
+
+
+## LayerFault Security Corpus
+
+The corpus contains deliberately synthetic, malformed and adversarial model repositories designed to reproduce security conditions that defensive tooling should detect.
+
+Coverage includes:
+
+- unsafe Pickle and serialization
+- NumPy object arrays
+- Safetensors structural anomalies
+- PyTorch and TorchScript packages
+- Hugging Face custom loaders and `auto_map`
+- tokenizer and template attacks
+- hidden and Unicode instructions
+- Python, PowerShell, JavaScript and shell capability chains
+- executable and native library payloads
+- archive and path traversal attacks
+- suspicious sidecars
+- dependency and package integrity anomalies
+- malformed model formats
+- provenance and lineage mismatches
+- synthetic weight anomalies and backdoor fixtures
+- dataset poisoning
+- runtime security test cases
+
+The corpus is organised into Hugging Face Collections so individual security domains can be explored and tested independently.
+
+---
+
+## Why LayerFault exists
+
+A modern model repository can contain much more than weights.
+
+```text
+Model Repository
+├── weights
+├── configuration
+├── tokenizer
+├── templates
+├── Python code
+├── native libraries
+├── serialized objects
+├── scripts
+└── dependency metadata
+```
+
+These components can introduce conventional software security risks before a single inference request is made.
+
+LayerFault places an admission boundary between acquiring a model and allowing it into an environment.
+
+```text
+MODEL ACQUISITION
+       │
+       ▼
+   LAYERFAULT
+       │
+       ├── structure
+       ├── serialization
+       ├── package contents
+       ├── integrity
+       ├── provenance
+       ├── policy
+       └── runtime exposure
+       │
+       ▼
+ PASS / WARN / BLOCK
+       │
+       ▼
+    INFERENCE
+```
+
+---
+
+## Security Notice
+
+Repositories published by this organisation may be intentionally adversarial.
+
+Fixtures can contain synthetic examples of executable code, unsafe serialization constructs, malformed metadata, suspicious archive structures, malicious looking scripts or other behaviours that security tooling is expected to detect.
+
+They are intended for:
+
+- defensive security research
+- scanner validation
+- regression testing
+- detector development
+- model admission research
+
+Do not treat corpus repositories as production models.
+
+Do not execute arbitrary fixture contents outside an appropriately isolated testing environment.
+
+---
+
+## Security Boundaries
+
+LayerFault can establish security properties about model artifacts, packages, provenance, runtimes and admission policy.
+
+Static inspection cannot prove that arbitrary learned weights are free from semantic backdoors, hidden triggers or malicious learned behaviour.
+
+LayerFault therefore keeps artifact security, runtime security and behavioural analysis as distinct guarantees.
+
+---
+
+## Corpus Purpose
+
+The LayerFault Security Corpus exists to make model security testing:
+
+- reproducible
+- comparable
+- regression friendly
+- transparent
+- easier to automate
+
+The corpus is designed to evolve alongside new model formats, packaging methods, runtime behaviours and attack techniques.
+
+---
+
+**Know what you are admitting. Know whether it changed. Know who attested to it. Block execution when those guarantees fail.**
 
-Edit this `README.md` markdown file to author your organization card.