Zeyu Lei (Purdue University), Güliz Seray Tuncay (Google), Beatrice Carissa Williem (Purdue University), Z. Berkay Celik (Purdue University), Antonio Bianchi (Purdue University)

Storage on Android has evolved significantly over the years, with each new Android version introducing changes aimed at enhancing usability, security, and privacy. While these updates typically help with restricting app access to storage through various mechanisms, they may occasionally introduce new complexities and vulnerabilities. A prime example is the introduction of scoped storage in Android 10, which fundamentally changed how apps interact with files. While intended to enhance user privacy by limiting broad access to shared storage, scoped storage has also presented developers with new challenges and potential vulnerabilities to address. However, despite its significance for user privacy and app functionality, no systematic studies have been performed to study Android's scoped storage at depth from a security perspective.

In this paper, we present the first systematic security analysis of the scoped storage mechanism. To this end, we design and implement a testing tool, named ScopeVerif, that relies on differential analysis to uncover security issues and implementation inconsistencies in Android's storage. Specifically, ScopeVerif takes a list of security properties and checks if there are any file operations that violate any security properties defined in the official Android documentation. Additionally, we conduct a comprehensive analysis across different Android versions as well as a cross-OEM analysis to identify discrepancies in different implementations and their security implications.

Our study identifies both known and unknown issues of scoped storage. Our cross-version analysis highlights undocumented changes as well as partially fixed security loopholes across versions. Additionally, we discovered several vulnerabilities in scoped storage implementations by different OEMs. These vulnerabilities stem from deviations from the documented and correct behavior, which potentially poses security risks. The affected OEMs and Google have acknowledged our findings and offered us bug bounties in response.

View More Papers

coucouArray ( [post_type] => ndss-paper [post_status] => publish [posts_per_page] => 4 [orderby] => rand [tax_query] => Array ( [0] => Array ( [taxonomy] => category [field] => id [terms] => Array ( [0] => 118 ) ) ) [post__not_in] => Array ( [0] => 20005 ) )

Starshields for iOS: Navigating the Security Cosmos in Satellite...

Jiska Classen (Hasso Plattner Institute, University of Potsdam), Alexander Heinrich (TU Darmstadt, Germany), Fabian Portner (TU Darmstadt, Germany), Felix Rohrbach (TU Darmstadt, Germany), Matthias Hollick (TU Darmstadt, Germany)

Read More

TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning Agents

Chen Gong (University of Vriginia), Kecen Li (Chinese Academy of Sciences), Jin Yao (University of Virginia), Tianhao Wang (University of Virginia)

Read More

Secret Spilling Drive: Leaking User Behavior through SSD Contention

Jonas Juffinger (Graz University of Technology), Fabian Rauscher (Graz University of Technology), Giuseppe La Manna (Amazon), Daniel Gruss (Graz University of Technology)

Read More

Mixnets on a Tightrope: Quantifying the Leakage of Mix...

Sebastian Meiser, Debajyoti Das, Moritz Kirschte, Esfandiar Mohammadi, Aniket Kate

Read More

Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)