Lost in Permissions: Exploring the Microsoft 365 App Ecosystem
- URL: http://arxiv.org/abs/2608.02336v1
- Date: Mon, 03 Aug 2026 14:54:23 GMT
- Title: Lost in Permissions: Exploring the Microsoft 365 App Ecosystem
- Abstract summary: We present the first privacy- and security-oriented measurement of M365 third-party applications.<n>We find that only 1,069 of them expose both descriptions and permission sets, with significant inconsistencies in transparency.<n>Overall, our findings expose systemic opacity and structural immaturity in the M365 app ecosystem.
- Score: 0.6719751155411074
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- How Agents Ask for Permission: User Permissions for AI Agents, from Interfaces to Enforcement [55.966820779528376]
As AI agents gain prevalence, users are increasingly exposed to the risks such systems entail.<n> Prompt injection attacks, as well as hallucination, can cause agents to leak private information to third parties.<n>As autonomous systems, agents present the more active danger of performing sensitive tasks, such as bank transactions, without the user's intent or authorization.
arXiv Detail & Related papers (2026-07-15T11:36:22Z) - Overprivilege Analysis of Security Policies in Serverless Cloud Applications [1.3068208613904335]
We analyze a curated dataset of 689 AWS Lambda applications comprised of 1,293 functions.<n>47.7% of applications carry excess permissions with a significant privilege reduction potential of 99.65%.<n>18.8% of applications hold unnecessary Privilege Escalation capabilities, and 12 applications had Defense Evasion permissions they did not need.
arXiv Detail & Related papers (2026-07-03T02:22:14Z) - Understanding and Mitigating Prompt Leaking Attacks in Real-World LLM-Based Applications [57.33208520608409]
This paper presents a systematic study of prompt leaking in large language model (LLM)-based applications.<n>We find that over 80% of deployments leak system prompts under realistic adversarial queries.<n>We propose AREA, a practical defense that re-anchors the model's attention using an optimizable soft prompt.
arXiv Detail & Related papers (2026-06-17T04:20:00Z) - FIKA-Bench: From Fine-grained Recognition to Fine-Grained Knowledge Acquisition [54.31138496553705]
Fine-grained recognition in everyday life is often not a closed-book classification problem.<n>Existing benchmarks primarily evaluate visually recognition, leaving this active external knowledge acquisition ability underexplored.<n>We study fine-grained knowledge acquisition, where a system must seek, verify, and use external evidence to answer open-ended fine-grained recognition questions.
arXiv Detail & Related papers (2026-05-13T08:49:51Z) - Why Does the LLM Stop Computing: An Empirical Study of User-Reported Failures in Open-Source LLMs [50.075587392477935]
We conduct the first large-scale empirical study of 705 real-world failures from the open-source DeepSeek, Llama, and Qwen ecosystems.<n>Our analysis reveals a paradigm shift: white-box orchestration relocates the reliability bottleneck from model algorithmic defects to the systemic fragility of the deployment stack.
arXiv Detail & Related papers (2026-01-20T06:42:56Z) - Demystifying Progressive Web Application Permission Systems [5.879181029987908]
Progressive Web Applications (PWAs) blend the advantages of web and native apps, offering features like offline access, push notifications, and installability.<n>Their permission management remains poorly defined and inconsistently implemented across platforms and browsers.<n>Our work highlights the urgent need for a unified, robust permission model for PWAs.
arXiv Detail & Related papers (2025-09-16T22:03:47Z) - From Hugging Face to GitHub: Tracing License Drift in the Open-Source AI Ecosystem [12.206378714907075]
Hidden license conflicts in the open-source AI ecosystem pose serious legal and ethical risks.<n>We present the first end-to-end audit of licenses for datasets and models on Hugging Face.
arXiv Detail & Related papers (2025-09-11T21:46:20Z) - A Comprehensive Analysis of Evolving Permission Usage in Android Apps: Trends, Threats, and Ecosystem Insights [9.172402449557264]
Despite official Android platform documentation on proper permission usage, there are still many cases of permission abuse.<n>This study provides a comprehensive analysis of the Android permission landscape.<n>By distinguishing between benign and malicious applications, we uncover developers' evolving strategies.
arXiv Detail & Related papers (2025-08-04T02:54:10Z) - OrgAccess: A Benchmark for Role Based Access Control in Organization Scale LLMs [7.999158988904784]
Large Language Models (LLMs) serve as unified knowledge repositories and intelligent assistants in enterprise settings.<n> evaluating this crucial capability is inherently difficult due to the proprietary and sensitive nature of real-world corporate data and access control policies.<n>We introduce a synthetic yet representative textbfOrgAccess benchmark consisting of 40 distinct types of permissions commonly relevant across different organizational roles and levels.
arXiv Detail & Related papers (2025-05-25T14:30:15Z) - Which Agent Causes Task Failures and When? On Automated Failure Attribution of LLM Multi-Agent Systems [50.29939179830491]
Failure attribution in LLM multi-agent systems remains underexplored and labor-intensive.<n>We develop and evaluate three automated failure attribution methods, summarizing their corresponding pros and cons.<n>The best method achieves 53.5% accuracy in identifying failure-responsible agents but only 14.2% in pinpointing failure steps.
arXiv Detail & Related papers (2025-04-30T23:09:44Z) - Seeker: Towards Exception Safety Code Generation with Intermediate Language Agents Framework [58.36391985790157]
In real world software development, improper or missing exception handling can severely impact the robustness and reliability of code.<n>We explore the use of large language models (LLMs) to improve exception handling in code.<n>We propose Seeker, a multi-agent framework inspired by expert developer strategies for exception handling.
arXiv Detail & Related papers (2024-12-16T12:35:29Z) - Towards Exception Safety Code Generation with Intermediate Representation Agents Framework [54.03528377384397]
Large Language Models (LLMs) often struggle with robust exception handling in generated code, leading to fragile programs that are prone to runtime errors.<n>We propose Seeker, a novel multi-agent framework that enforces exception safety in LLM generated code through an Intermediate Representation (IR) approach.<n>Seeker decomposes exception handling into five specialized agents: Scanner, Detector, Predator, Ranker, and Handler.
arXiv Detail & Related papers (2024-10-09T14:45:45Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.