Hangtian Liu (Information Engineering University), Lei Zheng (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Shuitao Gan (Laboratory for Advanced Computing and Intelligence Engineering), Chao Zhang (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Zicong Gao (Information Engineering University), Hongqi Zhang (Henan Key Laboratory of Information Security), Yishun Zeng (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Zhiyuan Jiang (National University of Defense Technology), Jiahai Yang (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University)

Hidden web interfaces, i.e., undisclosed access channels in IoT devices, introduce great security risks and have resulted in severe attacks in recent years. However, the definition of such threats is vague, and few solutions are able to discover them. Due to their hidden nature, traditional bug detection solutions (e.g., taint analysis, fuzzing) are hard to detect them. In this paper, we present a novel solution EAGLEYE to automatically expose hidden web interfaces in IoT devices. By analyzing input requests to public interfaces, we first identify routing tokens within the requests, i.e., those values (e.g., actions or file names) that are referenced and used as index by the firmware code (routing mechanism) to find associated handler functions. Then, we utilize modern large language models to analyze the contexts of such routing tokens and deduce their common pattern, and then infer other candidate values (e.g., other actions or file names) of these tokens. Lastly, we perform a hidden-interface directed black-box fuzzing, which mutates the routing tokens in input requests with these candidate values as the high-quality dictionary. We have implemented a prototype of EAGLEYE and evaluated it on 13 different commercial IoT devices. EAGLEYE successfully found 79 hidden interfaces, 25X more than the state-of-the-art (SOTA) solution IoTScope. Among them, we further discovered 29 unknown vulnerabilities including backdoor, XSS (cross-site scripting), command injection, and information leakage, and have received 7 CVEs.

View More Papers

Revisiting EM-based Estimation for Locally Differentially Private Protocols

Yutong Ye (Institute of software, Chinese Academy of Sciences & Zhongguancun Laboratory, Beijing, PR.China.), Tianhao Wang (University of Virginia), Min Zhang (Institute of Software, Chinese Academy of Sciences), Dengguo Feng (Institute of Software, Chinese Academy of Sciences)

Read More

The Road to Trust: Building Enclaves within Confidential VMs

Wenhao Wang (Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering, CAS), Linke Song (Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering, CAS), Benshan Mei (Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering, CAS), Shuang Liu (Ant Group), Shijun Zhao (Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering,…

Read More

WIP: Towards Privacy Compliance by Design in the Matter...

Yichen Liu (Indiana University Bloomington), Jingwen Yan (Clemson University), Song Liao (Texas Tech University), Long Cheng (Clemson University), Luyi Xing (Indiana University Bloomington)

Read More

NDSS Symposium 2025 Welcome and Opening Remarks

General Chairs: David Balenson, USC Information Sciences Institute and Heng Yin, University of California, Riverside Program Chairs: Christina Pöpper, New York University Abu Dhabi and Hamed Okhravi, MIT Lincoln Laboratory Artifact Evaluation Chairs: Daniele Cono D’Elia, Sapienza University and Mathy Vanhoef, KU Leuven

Read More