Lost in Permissions: Exploring the Microsoft 365 App Ecosystem
Vincenzo Longo, Alberto Verna, Nikhil Jha, Marco Mellia
Abstract
The Microsoft 365 (M365) ecosystem hosts thousands of third-party applications that integrate with enterprise tenants via fine-grained OAuth permissions, potentially granting access to sensitive organisational resources such as emails, files, calendars, chats, and user directories. Despite the security implications of these permission grants, the M365 ecosystem has not been systematically studied. We present the first privacy- and security-oriented measurement of M365 third-party applications. By combining public marketplace APIs with automated tenant-side deployment, we crawl over 8,000 applications. We find that only 1,069 of them expose both descriptions and permission sets, with significant inconsistencies in transparency across official distribution channels. Next, we leverage a topic-aware anomaly detection framework to assess whether requested permissions align with declared functionality. We cluster applications via Neural Topic Modelling and apply unsupervised anomaly detection within each topic to identify deviations from peer permission profiles. LLM-assisted analysis of the most anomalous cases and blind manual inspection reveal a correlation between anomalous permission profiles and the risk associated with the requested permissions. We find that many applications request overly broad tenant-wide scopes (e.g., directory-wide read/write access), violating least-privilege principles and increasing the organisational attack surface. Our pipeline provides tenant administrators with actionable insights by identifying anomalous applications and the permissions that most contribute to their anomalousness. Overall, our findings expose systemic opacity and structural immaturity in the M365 app ecosystem, where permission disclosure is inconsistent and over-privileged access is common.
Create a lesson
Related papers
Analog Pin Directionality as an Exfiltration Attack Surface in Mixed-Signal ICs
Ramana Ranganatham, Chirag Adiga, Michael Zuzak et al.
Characterizing Network Centralization and Observability in the Remote MCP Ecosystem
Muhammad Abdullah Sohail
When Agents Look Like Beacons: NIDS Evasion by Model Context Protocol Traffic
Muhammad Abdullah Sohail
Hamming Ideals and Grobner Bases for ISD-like Syndrome Decoding
Roberto La Scala, Marco Marchesin, Sharwan K. Tiwari
ASLEval: Measuring Privacy Exposure Displacement in LLM Agent Sessions
Guosen Wu, Huizhen Huang, Guoxiong Long et al.
CASHEWS: Source Preprocessor for LLM-based Malicious Package Detection
Jean-Charles Noirot Ferrand, David Adei, Anders Møller et al.