Feature Extraction
Transformers
static_canary
ctf
model-scanning
supply-chain-canary
static-analysis
custom_code
Instructions to use WWTCyberLab/static-canary-pt-loader-risk with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WWTCyberLab/static-canary-pt-loader-risk with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="WWTCyberLab/static-canary-pt-loader-risk", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("WWTCyberLab/static-canary-pt-loader-risk", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
WWTCyberLab/static-canary-pt-loader-risk
This is an inert CTF canary model repository for Cisco SecureAI model scanning demos. It is intentionally designed to look risky to static model scanners while avoiding destructive behavior.
Expected scanner themes:
model_CTF{model_scanner_high_pickle_evidence}.ptcontains a Python protocol-2 pickle reducer for__builtin__.set. Loading it constructs only a normal set of static strings, includingCTF_MODEL_CANARY.static-canary-pt-loader-risk*.safetensorsfiles are valid safetensors fixtures with static metadata, suspicious tensor names, anomalous FP32 markers, and deterministic high-entropy-looking weight bytes to exercise obfuscation heuristics.config.jsonincludesauto_mapmetadata so scanners can exercisetrust_remote_codereview paths.modeling_static_canary.pyimports modules commonly flagged by loader-risk scanners, but import and model construction have no subprocess, filesystem, or network side effects.
Runtime canary events should be emitted by the lab harness, not hidden inside the model artifact.
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