Python library to scan ML models for vulnerabilities
Hi! I’ve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, …) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production. It is still a beta and I would love to hear your feedback if you have the time to try it out. We have quite a few tutorials in the docs with ready-made colab notebooks to…
In plain words
This Python library automatically scans machine learning models to identify vulnerabilities including underperforming data subsets, prediction overconfidence, and robustness issues. It works with major ML frameworks like scikit-learn, PyTorch, and XGBoost, and integrates with Giskard AI's quality assurance platform for pre-production testing. Designed for data scientists and ML engineers, it offers tutorials and ready-made Jupyter notebooks to help users get started testing their models.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi! I’ve been working on this automatic scanner for ML models to detect issues like underperforming data slices, overconfidence in predictions, robustness problems, and others. It supports all main Python ML frameworks (sklearn, torch, xgboost, …) and integrates with the quality assurance solution we are building at Giskard AI (https://giskard.ai) to systematically test models before putting them in production. It is still a beta and I would love to hear your feedback if you have the time to try it out. We have quite a few tutorials in the docs with ready-made colab notebooks to make it easy to get started. If you are interested in the code: https://github.com/Giskard-AI/giskard/tree/main/python-clien...
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