Zhibo Zhang (Fudan University), Lei Zhang (Fudan University), Zhangyue Zhang (Fudan University), Geng Hong (Fudan University), Yuan Zhang (Fudan University), Min Yang (Fudan University)

underline{D}edicated underline{U}RL underline{s}hortening underline{s}ervices (DUSSs) are designed to transform textit{trusted} long URLs into the shortened links.
Since DUSSs are widely used in famous corporations to better serve their large number of users (especially mobile users), cyber criminals attempt to exploit DUSS to transform their malicious links and abuse the inherited implicit trust, which is defined as textit{Misdirection Attack} in this paper.
However, little effort has been made to systematically understand such attacks. To fulfill the research gap, we present the first systematic study of the textit{Misdirection Attack} in abusing DUSS to demystify its attack surface, exploitable scope, and security impacts in the real world.

Our study reveals that real-world DUSSs commonly rely on custom URL checks, yet they exhibit unreliable security assumptions regarding web domains and lack adherence to security standards.
We design and implement a novel tool, Ditto, for empirically studying vulnerable DUSSs from a mobile perspective.
Our large-scale study reveals that a quarter of the DUSSs are susceptible to textit{Misdirection Attack}.
More importantly, we find that DUSSs hold implicit trust from both their users and domain-based checkers, extending the consequences of the attack to stealthy phishing and code injection on users' mobile phones.
We have responsibly reported all of our findings to corporations of the affected DUSS and helped them fix their vulnerabilities.

View More Papers

I know what you MEME! Understanding and Detecting Harmful...

Yong Zhuang (Wuhan University), Keyan Guo (University at Buffalo), Juan Wang (Wuhan University), Yiheng Jing (Wuhan University), Xiaoyang Xu (Wuhan University), Wenzhe Yi (Wuhan University), Mengda Yang (Wuhan University), Bo Zhao (Wuhan University), Hongxin Hu (University at Buffalo)

Read More

VoiceRadar: Voice Deepfake Detection using Micro-Frequency and Compositional Analysis

Kavita Kumari (Technical University of Darmstadt), Maryam Abbasihafshejani (University of Texas at San Antonio), Alessandro Pegoraro (Technical University of Darmstadt), Phillip Rieger (Technical University of Darmstadt), Kamyar Arshi (Technical University of Darmstadt), Murtuza Jadliwala (University of Texas at San Antonio), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

Read More

UI-CTX: Understanding UI Behaviors with Code Contexts for Mobile...

Jiawei Li (Beihang University & National University of Singapore), Jiahao Liu (National University of Singapore), Jian Mao (Beihang University), Jun Zeng (National University of Singapore), Zhenkai Liang (National University of Singapore)

Read More

Delay-allowed Differentially Private Data Stream Release

Xiaochen Li (University of Virginia), Zhan Qin (Zhejiang University), Kui Ren (Zhejiang University), Chen Gong (University of Virginia), Shuya Feng (University of Connecticut), Yuan Hong (University of Connecticut), Tianhao Wang (University of Virginia)

Read More