Chongqing Lei (Southeast University), Zhen Ling (Southeast University), Yue Zhang (Jinan University), Kai Dong (Southeast University), Kaizheng Liu (Southeast University), Junzhou Luo (Southeast University), Xinwen Fu (University of Massachusetts Lowell)

Android accessibility service was designed to assist individuals with disabilities in using Android devices. However, it has been exploited by attackers to steal user passwords due to design shortcomings. Google has implemented various countermeasures to make it difficult for these types of attacks to be successful on modern Android devices. In this paper, we present a new type of side channel attack called content queries (CONQUER) that can bypass these defenses. We discovered that Android does not prevent the content of passwords from being queried by the accessibility service, allowing malware with this service enabled to enumerate the combinations of content to brute force the password. While this attack seems simple to execute, there are several challenges that must be addressed in order to successfully launch it against real-world apps. These include the use of lazy query to differentiate targeted password strings, active query to determine the right timing for the attack, and timing- and state-based side channels to infer case-sensitive passwords. Our evaluation results demonstrate that the CONQUER attack is effective at stealing passwords, with an average one-time success rate of 64.91%. This attack also poses a threat to all Android versions from 4.1 to 12, and can be used against tens of thousands of apps. In addition, we analyzed the root cause of the CONQUER attack and discussed several countermeasures to mitigate the potential security risks it poses.

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] => 66 ) ) ) [post__not_in] => Array ( [0] => 13188 ) )

BARS: Local Robustness Certification for Deep Learning based Traffic...

Kai Wang (Tsinghua University), Zhiliang Wang (Tsinghua University), Dongqi Han (Tsinghua University), Wenqi Chen (Tsinghua University), Jiahai Yang (Tsinghua University), Xingang Shi (Tsinghua University), Xia Yin (Tsinghua University)

Read More

Bridging the Privacy Gap: Enhanced User Consent Mechanisms on...

Carl Magnus Bruhner (Linkoping University), David Hasselquist (Linkoping University, Sectra Communications), Niklas Carlsson (Linkoping University)

Read More

“I didn't click”: What users say when reporting phishing

Nikolas Pilavakis, Adam Jenkins, Nadin Kokciyan, Kami Vaniea (University of Edinburgh)

Read More

podft: On Accelerating Dynamic Taint Analysis with Precise Path...

Zhiyou Tian (Xidian University), Cong Sun (Xidian University), Dongrui Zeng (Palo Alto Networks), Gang Tan (Pennsylvania State University)

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)