Cem Topcuoglu (Northeastern University), Kaan Onarlioglu (Akamai Technologies), Bahruz Jabiyev (Northeastern University), Engin Kirda (Northeastern University)

Web server fingerprinting is a common activity in vulnerability management and security testing, with network scanners offering the capability for over two decades. All known fingerprinting techniques are designed for probing a single, isolated web server. However, the modern Internet is made up of complex layered architectures, where chains of CDNs, reverse proxies, and cloud services front origin servers. That renders existing fingerprinting tools and techniques utterly ineffective.

We present the first methodology that can fingerprint servers in a multi-layer architecture, by leveraging the HTTP processing discrepancies between layers. This technique is capable of detecting both the server technologies involved and their correct ordering. It is theoretically extendable to any number of layers, any server technology, deployed in any order, but of course within practical constraints. We then address those practical considerations and present a concrete implementation of the scheme in a tool called Untangle, empirically demonstrating its ability to fingerprint 3-layer architectures with high accuracy.

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PANDORA: Jailbreak GPTs by Retrieval Augmented Generation Poisoning

Gelei Deng, Yi Liu (Nanyang Technological University), Yuekang Li (The University of New South Wales), Wang Kailong(Huazhong University of Science and Technology), Tianwei Zhang, Yang Liu (Nanyang Technological University)

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EnclaveFuzz: Finding Vulnerabilities in SGX Applications

Liheng Chen (Institute of Information Engineering, Chinese Academy of Sciences; School of Cyber Security, University of Chinese Academy of Sciences; Institute for Network Science and Cyberspace of Tsinghua University), Zheming Li (Institute for Network Science and Cyberspace of Tsinghua University), Zheyu Ma (Institute for Network Science and Cyberspace of Tsinghua University), Yuan Li (Tsinghua University),…

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Make your IoT environments robust against adversarial machine learning...

Hamed Haddadpajouh (University of Guelph), Ali Dehghantanha (University of Guelph)

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