Yizhe Shi (Fudan University), Zhemin Yang (Fudan University), Dingyi Liu (Fudan University), Kangwei Zhong (Fudan University), Jiarun Dai (Fudan University), Min Yang (Fudan University)

In the app-in-app ecosystem, super-apps provide mini-app developers access to various sensitive cloud services, such as cloud database and cloud storage. These services enable mini-app developers to efficiently store and manage mini-app data in the super-app server. To protect these sensitive resources, super-apps implement an identity management mechanism, allowing mini-app developers to verify user identity and ensure that only authorized and trusted users can access specific resources. However, flaws exist in the implementation of resource management by mini-app developers, which can expose sensitive resources to attackers.

In this paper, we conduct the first systematic study of the insecure cloud resource management in the app-in-app ecosystem. We design and implement a tool, ICREMiner, that combines static analysis and dynamic probing to assess the security implications on 22,695 real-world mini-apps that access app-in-app cloud services in four super-app platforms. The results of our study reveal that 2,815 mini-apps (12.40%) are affected by the insecure resource management, involving 8,062 insecure cloud operations. We have identified that some mini-apps of prominent corporations are also vulnerable to these risks. Additionally, we conduct an in-depth analysis of the significant security hazards that can be caused by the vulnerability, such as allowing attackers to steal sensitive user information and pay for free. In response, we have engaged in responsible vulnerability disclosure to the super-app platforms and corresponding mini-app developers. We also provide several mitigation strategies to help them resolve the vulnerabilities.

View More Papers

ropbot: Reimaging Code Reuse Attack Synthesis

Kyle Zeng (Arizona State University), Moritz Schloegel (CISPA Helmholtz Center for Information Security), Christopher Salls (UC Santa Barbara), Adam Doupé (Arizona State University), Ruoyu Wang (Arizona State University), Yan Shoshitaishvili (Arizona State University), Tiffany Bao (Arizona State University)

Read More

CoLD: Collaborative Label Denoising Framework for Network Intrusion Detection

Shuo Yang (The University of Hong Kong), Xinran Zheng (University College London), Jinze Li (The University of Hong Kong), Jinfeng Xu (The University of Hong Kong), Edith C. H. Ngai (The University of Hong Kong)

Read More

Pogofuzz: Profile-Guided Optimization for Fuzzing (Registered Report)

Tobias Holl (Ruhr University Bochum), Leon Weiß (Ruhr University Bochum), Kevin Borgolte (Ruhr University Bochum)

Read More