xgboost
Pass
Audited by Gen Agent Trust Hub on Oct 11, 2026
Risk Level: SAFE
Full Analysis
- [SAFE]: The skill documentation and provided code snippets utilize well-known, established data science libraries including xgboost, scikit-learn, pandas, and numpy. All code examples demonstrate standard usage patterns for training and evaluating machine learning models without suspicious external dependencies or execution methods.
- [SAFE]: The instructions explicitly recommend using the native JSON format for saving and loading models (
model.save_model("model.json")) rather than Python's binary pickle format. This is a recognized security best practice that reduces the risk of arbitrary code execution during model deserialization. - [SAFE]: The code snippets for GPU acceleration (
device="cuda") and early stopping are standard performance features of the library and do not involve unauthorized privilege escalation or network exfiltration patterns.
Audit Metadata