This work presents the first privacy- and security-oriented measurement of M365 third-party applications, and finds that only 1,069 of them expose both descriptions and permission sets, with significant inconsistencies in transparency across official distribution channels.
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.
On October 17th, 2025, Google announced the retirement of most Privacy Sandbox APIs, concluding nearly five years of experimentation with its alternative to privacy-invasive data collection on the Web. Designed to balance privacy with advertising functionality and cross-site tracking, the initiative faced repeated redesigns and limited ecosystem support. In this work, we present the first longitudinal, consent-aware measurement of the Privacy Sandbox's deployment across the Web. Using a custom call listener and weekly crawls of the top-10,000 websites, we monitor the usage of all major APIs in the months preceding their retirement. Adoption had already stagnated well before Google's announcement: most APIs were used by only a handful of actors, whose activity declined steadily throughout our study. Even the APIs that Google plans to maintain show no sign of growth. The sole exception is Cookies Having Independent Partitioned State (CHIPS). Overall, the demise of the Privacy Sandbox leaves unresolved the challenge of enabling privacy-preserving interest-based advertising.
Rachid Youssef Grib, Alberto Verna, Nikhil Jha et al.· 0 citations
Mobile app reviews are a rich, continuously renewing source of how users experience privacy and security, yet existing taxonomies of these concerns are hand-crafted and cannot keep pace with the evolving nature of the data. Automating taxonomy construction is the natural response, but scalability is the core challenge: current LLM- and clustering-based methods are developed for scientific corpora of a few thousand documents and do not extend to app review collections numbering in the hundreds of thousands. We address this gap in two ways. First, we filter app reviews for privacy- and security-related content, yielding a comprehensive corpus of over 600K reviews. Second, we introduce TaxoScale, a pipeline that handles taxonomy construction at this scale by extending an expert-defined taxonomy via Recursive Hierarchical Clustering and LLM-based node naming. TaxoScale outperforms strong automatic-taxonomy baselines on path, level, coverage, and novelty metrics, and discovers novel branches absent from prior taxonomies.
Moghis Fereidouni, Vinaik Chhetri, Umar Farooq et al.· 0 citations
As large language model (LLM) platforms evolve rapidly, third-party plugin systems extend model capabilities but also introduce challenges for infrastructure governance and security. When a platform replaces an older third-party plugin ecosystem, backend services from the old ecosystem may remain reachable even after the platform no longer exposes those integrations. In this work, we focus on the early ChatGPT plugin ecosystem, which is in the original plugin store before the transition to the GPT Store. We probe domain reachability, residual configuration files, and API endpoint responses. We also use historical API records to identify backend services that standard probes miss. Our results show that 72.9% of plugin domains remain reachable, while only 6.2% of plugins retain fully functional backend APIs. Among these reachable backends, many do not declare authentication and expose numerous API endpoints that perform stateful operations. Historical probing further reveals 123 hidden backend services that remain publicly reachable but no longer appear in current configurations. These findings show that platform evolution reduces backend visibility but does not eliminate network reachability. This gap reveals a governance blind spot during the evolution of LLM platform extension mechanisms.
The first large-scale, cross-platform study of plugins from five major web application marketplaces, covering domains from office productivity to software development, indicates that AI-assisted plugins face a range of emerging issues that negatively impact user experience and fail to comply with established AI ethics principles.
Liuhuo Wan, Zicong Liu, Chuan Yan et al.· Proceedings of the ACM on So...· 0 citations
Ensuring compliance with privacy regulations, such as GDPR and CCPA, has become a critical challenge for mobile applications. These regulations mandate that apps provide accessible paths for users to exercise rights, such as account deletion and data export. However, these paths are often buried deep within complex UI hierarchies, making manual auditing unscalable. In this paper, we introduce RightsGen, a framework that leverages Large Language Models (LLMs) to automate the discovery and documentation of user rights declarations in Android applications. Unlike traditional random UI fuzzers, RightsGen utilizes the semantic reasoning capabilities of LLMs to navigate dynamic UI states. It employs a path-oriented mechanism and a GUI Agent acting as a rights tester guided by structured rights tuples. In our evaluation across 144 popular apps, RightsGen demonstrated high efficiency in dynamic testing, achieving a recall rate of 92.5% in recognizing user rights paths and 87.3% in extracting rights tuples. These results highlight the effectiveness of RightsGen in automating the generation of user rights declarations.
Chengjun Li, Tao Liu, Yi Wu et al.· SIGSOFT FSE Companion· 0 citations
Application Programming Interfaces (APIs) are essential in software development, enabling web services, mobile apps, and microservices. However, their widespread use introduces significant security risks, highlighting the importance of API security. This paper presents HTTP REST API Learning (HRAL), a novel unsupervised anomaly detection approach that models the structure and behavior of API endpoints directly from network traffic, without relying on predefined rules or documentation. HRAL enables robust detection of malicious activity by understanding how APIs behave and flagging deviations as potential threats. We evaluate HRAL across varying levels of OpenAPI documentation detail and compare it with existing techniques. HRAL achieves strong performance, with an average recall of 82.07% and an F1-score of 87.24%, significantly outperforming alternatives when API documentation is limited. Moreover, our results approach the effectiveness of full API document definitions. When combined with signature-based rules such as the OWASP ModSecurity CRS, our system achieves 100% detection. These results highlight HRAL's effectiveness in real-world, partially documented API environments and its potential as a foundational layer for modern API security solutions.