Xianbo Wang (The Chinese University of Hong Kong), Shangcheng Shi (The Chinese University of Hong Kong), Yikang Chen (The Chinese University of Hong Kong), Wing Cheong Lau (The Chinese University of Hong Kong)

Nowadays, most mobile devices are equipped with various hardware interfaces such as touchscreen, fingerprint scanner, camera and microphone to capture inputs from the user.
Many mobile apps use these physical interfaces to receive user-input for authentication/authorization operations including one-click login, fingerprint-based payment approval, and face/voice unlocking.
In this paper, we investigate the so-called PHYjacking attack where a victim is misled by a zero-permission malicious app to feed physical inputs to different hardware interfaces on a mobile device to result in unintended authorization.
We analyze the protection mechanisms in Android for different types of physical input interfaces and introduce new techniques to bypass them.
Specifically, we identify weaknesses in the existing protection schemes for the related system APIs and observe common pitfalls when apps implement physical-input-based authorization.
Worse still, we discover a race-condition bug in Android that can be exploited even when app-based mitigations are properly implemented.
Based on these findings, we introduce fingerprint-jacking and facejacking techniques and demonstrate their impact on real apps.
We also discuss the feasibility of launching similar attacks against NFC and microphone inputs, as well as effective tapjacking attacks against Single Sign-On apps.
We have designed a static analyzer to examine 3000+ real-world apps and find 44% of them contain PHYjacking-related implementation flaws.
We demonstrate the practicality and potential impact of PHYjacking via proof-of-concept implementations which enable unauthorized money transfer on a payment app with over 800 million users, user-privacy leak from a social media app with over 400 million users and escalating app permissions in Android 11.

View More Papers

MIRROR: Model Inversion for Deep LearningNetwork with High Fidelity

Shengwei An (Purdue University), Guanhong Tao (Purdue University), Qiuling Xu (Purdue University), Yingqi Liu (Purdue University), Guangyu Shen (Purdue University); Yuan Yao (Nanjing University), Jingwei Xu (Nanjing University), Xiangyu Zhang (Purdue University)

Read More

Speeding Dumbo: Pushing Asynchronous BFT Closer to Practice

Bingyong Guo (Institute of Software, Chinese Academy of Sciences), Yuan Lu (Institute of Software Chinese Academy of Sciences), Zhenliang Lu (The University of Sydney), Qiang Tang (The University of Sydney), jing xu (Institute of Software, Chinese Academy of Sciences), Zhenfeng Zhang (Institute of Software, Chinese Academy of Sciences)

Read More

EqualNet: A Secure and Practical Defense for Long-term Network...

Jinwoo Kim (KAIST), Eduard Marin (Telefonica Research (Spain)), Mauro Conti (University of Padua), Seungwon Shin (KAIST)

Read More

Chosen-Instruction Attack Against Commercial Code Virtualization Obfuscators

Shijia Li (College of Computer Science, NanKai University and the Tianjin Key Laboratory of Network and Data Security Technology), Chunfu Jia (College of Computer Science, NanKai University and the Tianjin Key Laboratory of Network and Data Security Technology), Pengda Qiu (College of Computer Science, NanKai University and the Tianjin Key Laboratory of Network and Data…

Read More