ClawHub 보안 신호: AI 스킬 데이터셋 분석 및 판정 분류 가이드
ClawHub Security Signals: A Coding Guide to End-to-End Security Signal Analysis and Verdict Classification on the AI Skills Dataset
- 매체
- MarkTechPost
- 게시
- 중요도
- 2/5
- 화제 지수
- —
한국어 요약
ClawHub Security Signals 데이터셋을 활용한 AI 스킬 보안 평가 튜토리얼이 공개됐다. VirusTotal, 정적 분석, SkillSpector 등 여러 스캐너의 판정 결과를 비교 분석하고, 텍스트 정보와 보안 신호를 결합해 로지스틱 회귀 모델로 ClawScan 판정을 분류한다.
원문 발췌 · English
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.