Yuxiang Yang (Tsinghua University), Xuewei Feng (Tsinghua University), Qi Li (Tsinghua University), Kun Sun (George Mason University), Ziqiang Wang (Southeast University), Ke Xu (Tsinghua University)

In this paper, we uncover a new side-channel vulnerability in the widely used NAT port preservation strategy and an insufficient reverse path validation strategy of Wi-Fi routers, which allows an off-path attacker to infer if there is one victim client in the same network communicating with another host on the Internet using TCP. After detecting the presence of TCP connections between the victim client and the server, the attacker can evict the original NAT mapping and reconstruct a new mapping at the router by sending fake TCP packets due to the routers' vulnerability of disabling TCP window tracking strategy, which has been faithfully implemented in most of the routers for years. In this way, the attacker can intercept TCP packets from the server and obtain the current sequence and acknowledgment numbers, which in turn allows the attacker to forcibly close the connection, poison the traffic in plain text, or reroute the server's incoming packets to the attacker.

We test 67 widely used routers from 30 vendors and discover that 52 of them are affected by this attack. Also, we conduct an extensive measurement study on 93 real-world Wi-Fi networks. The experimental results show that 75 of these evaluated Wi-Fi networks (81%) are fully vulnerable to our attack. Our case study shows that it takes about 17.5, 19.4, and 54.5 seconds on average to terminate an SSH connection, download private files from FTP servers, and inject fake HTTP response packets with success rates of 87.4%, 82.6%, and 76.1%. We responsibly disclose the vulnerability and suggest mitigation strategies to all affected vendors and have received positive feedback, including acknowledgments, CVEs, rewards, and adoption of our suggestions.

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Huaiyu Yan (Southeast University), Zhen Ling (Southeast University), Haobo Li (Southeast University), Lan Luo (Anhui University of Technology), Xinhui Shao (Southeast University), Kai Dong (Southeast University), Ping Jiang (Southeast University), Ming Yang (Southeast University), Junzhou Luo (Southeast University, Nanjing, P.R. China), Xinwen Fu (University of Massachusetts Lowell)

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Wenjun Zhu (Zhejiang University), Yuan Sun (Zhejiang University), Jiani Liu (Zhejiang University), Yushi Cheng (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

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Tobias Lüscher (ETH Zurich), Martin Strohmeier (Cyber-Defence Campus, armasuisse S+T), Vincent Lenders (Cyber-Defence Campus, armasuisse S+T)

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Raushan Kumar Singh (IIT Ropar), Sudeepta Mishra (IIT Ropar)

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Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

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Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)