Xin Zhang (Fudan University), Xiaohan Zhang (Fudan University), Zhichen Liu (Fudan University), Bo Zhao (Fudan University), Zhemin Yang (Fudan University), Min Yang (Fudan University)

Fingerprint-based authentication (FpAuth) is increasingly utilized by Android apps, particularly in highly sensitive scenarios such as account login and payment, as it can provide a convenient method for verifying user identity. However, the correct and secure use of Android fingerprint APIs (FpAPIs) in real-world mobile apps remains a challenge due to their complex and evolving nature.

This paper presents the first systematic empirical analysis of FpAPI misuses in Android apps from the perspective of the FpAuth lifecycle. First, we develop specialized tools to identify and analyze apps employing FpAPIs, examining their characteristics. Then we define the threat models and categorize four prevalent types of FpAPI misuses through a detailed lifecycle analysis in practical settings. Finally, we develop tools to automatically detect these misuse types in 1,333 apps that use FpAuth and find alarming results: 97.15% of them are vulnerable to at least one type of misuse, with 18.83% susceptible to all identified misuse types. The consequences of such misuses are significant, including unauthorized data access, account compromise, and even financial loss, impacting a broad user base. We have responsibly reported these vulnerabilities, resulting in the issuance of 184 CVE IDs and 19 China National Vulnerability Database (CNVD) IDs, as well as acknowledgment from 15 vendors. We hope this work can raise awareness and emphasize the importance of proper usage of FpAPIs.

View More Papers

The Philosopher’s Stone: Trojaning Plugins of Large Language Models

Tian Dong (Shanghai Jiao Tong University), Minhui Xue (CSIRO's Data61), Guoxing Chen (Shanghai Jiao Tong University), Rayne Holland (CSIRO's Data61), Yan Meng (Shanghai Jiao Tong University), Shaofeng Li (Southeast University), Zhen Liu (Shanghai Jiao Tong University), Haojin Zhu (Shanghai Jiao Tong University)

Read More

How Different Tokenization Algorithms Impact LLMs and Transformer Models...

Ahmed Mostafa, Raisul Arefin Nahid, Samuel Mulder (Auburn University)

Read More

Do We Really Need to Design New Byzantine-robust Aggregation...

Minghong Fang (University of Louisville), Seyedsina Nabavirazavi (Florida International University), Zhuqing Liu (University of North Texas), Wei Sun (Wichita State University), Sundararaja Iyengar (Florida International University), Haibo Yang (Rochester Institute of Technology)

Read More

Secure Transformer Inference Made Non-interactive

Jiawen Zhang (Zhejiang University), Xinpeng Yang (Zhejiang University), Lipeng He (University of Waterloo), Kejia Chen (Zhejiang University), Wen-jie Lu (Zhejiang University), Yinghao Wang (Zhejiang University), Xiaoyang Hou (Zhejiang University), Jian Liu (Zhejiang University), Kui Ren (Zhejiang University), Xiaohu Yang (Zhejiang University)

Read More