Zeyu Lei (Purdue University), Yuhong Nan (Purdue University), Yanick Fratantonio (Eurecom & Cisco Talos), Antonio Bianchi (Purdue University)

SMS messages containing One-Time Passwords (OTPs) are a widely used mechanism for performing authentication in mobile applications. In fact, many popular apps use OTPs received via SMS as the only authentication factor, entirely replacing password-based authentication schemes. Although SMS OTP authentication mechanisms provide significant convenience to end-users, they also have significant security implications. In this paper, we study these mobile apps' authentication schemes based on SMS OTPs, and, in particular, we perform a systematic study on the threats posed by ``local attacks,'' a scenario in which an attacker has control over an unprivileged third-party app on the victim's device.

This study was carried out using a combination of reverse engineering, formal verification, user studies, and large-scale automated analysis. Our work not only revealed vulnerabilities in third-party apps, but it also uncovered several new design and implementation flaws in core APIs implemented by the mobile operating systems themselves. For instance, we found two official Android APIs to be vulnerable by design, i.e., APIs that inevitably lead to the implementation of insecure authentication schemes, even when used according to their documentation. Moreover, we found that other APIs are prone to be used unsafely by apps' developers.

Our large-scale study found 36 apps, sharing hundreds of millions of installations, that misuse these APIs, allowing a malicious local attacker to completely hijack their accounts. Such vulnerable apps include Telegram and KakaoTalk, some of the most popular messaging apps worldwide. Finally, we proposed a new and safer mechanism to perform SMS-based authentication, and we prove its safety using formal verification.

View More Papers

Demo #4: Attacking Tesla Model X’s Autopilot Using Compromised...

Ben Nassi (Ben-Gurion University of the Negev), Yisroel Mirsky (Ben-Gurion University of the Negev, Georgia Tech), Dudi Nassi, Raz Ben Netanel (Ben-Gurion University of the Negev), Oleg Drokin (Independent Researcher), and Yuval Elovici (Ben-Gurion University of the Negev) Best Demo Award Winner ($300 cash prize)!

Read More

Differential Training: A Generic Framework to Reduce Label Noises...

Jiayun Xu (Singapore Management University), Yingjiu Li (University of Oregon), Robert H. Deng (Singapore Management University)

Read More

Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses...

Virat Shejwalkar (UMass Amherst), Amir Houmansadr (UMass Amherst)

Read More

Differentially Private Health Tokens for Estimating COVID-19 Risk

David Butler, Chris Hicks, James Bell, Carsten Maple, and Jon Crowcroft (The Alan Turing Institute)

Read More