{"ID":2829421,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.15781","arxiv_id":"2512.15781","title":"Detecting Malicious Entra OAuth Apps with LLM-Based Permission Risk Scoring","abstract":"This project presents a unified detection framework that constructs a complete corpus of Microsoft Graph permissions, generates consistent LLM-based risk scores, and integrates them into a real-time detection engine to identify malicious OAuth consent activity.","short_abstract":"This project presents a unified detection framework that constructs a complete corpus of Microsoft Graph permissions, generates consistent LLM-based risk scores, and integrates them into a real-time detection engine to identify malicious OAuth consent activity.","url_abs":"https://arxiv.org/abs/2512.15781","url_pdf":"https://arxiv.org/pdf/2512.15781v1","authors":"[\"Ashim Mahara\"]","published":"2025-12-14T23:05:50Z","proceeding":"cs.CR","tasks":"[\"cs.CR\"]","methods":"[\"Large Language Model\"]","has_code":false}