aiqbench analysis
This tutorial provides a reproducible workflow for analyzing the ClawHub Security Signals dataset using Hugging Face Parquet files to evaluate scanner overlap and disagreement. By combining textual SKILL.md metadata with numerical signals, the guide demonstrates how to train a logistic regression model to predict security verdicts. The approach offers a practical, Colab-ready pipeline for researchers and engineers looking to quantify the reliability of AI security scanning tools.
ClawHub Security Signals: A Coding Guide to End-to-End Security Signal Analysis and Verdict Classification on the AI Skills Dataset
In this tutorial, we explore the ClawHub Security Signals dataset to see how scanners assess AI skills. We load the data from the Hugging Face Parquet conversion and inspect verdicts, scanner outputs, and severity labels. We measure how VirusTotal, static analysis, and SkillSpector overlap and disagree using Jaccard scores and Cohen's kappa. Finally, we combine SKILL.md text with scanner signals to train a logistic regression model for ClawScan verdicts. The post ClawHub Security Signals: A Coding Guide to End-to-End Security Signal Analysis and Verdict Classification on the AI Skills Dataset appeared first on MarkTechPost.
In this tutorial, we use the ClawHub Security Signals dataset to examine how different security scanners assess AI skills and related files. We load the dataset directly from the Hugging Face Parquet conversion to avoid compatibility issues with newer dataset metadata, then inspect the main columns, verdict distribution, scanner outputs, and severity labels. After exploring scanner disagreement…
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