Ruixuan Li (Tsinghua University), Chaoyi Lu (Zhongguancun Laboratory), Baojun Liu (Tsinghua University), Yanzhong Lin (Coremail Technology Co. Ltd), Qingfeng Pan (Coremail Technology Co. Ltd), Jun Shao (Zhejiang Gongshang University; Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology)

This paper introduces a novel and powerful email convergence amplification attack, named COORDMAIL. Traditional email DoS attacks primarily send spam to targeted mailboxes, with little ability to affect email servers’ operation. In contrast, COORDMAIL exploits the inherent properties of the SMTP protocol, i.e., long session timeouts and client-controlled interactions, to cleverly coordinate reflected emails from various email middleware and eventually direct them to an incoming mail server simultaneously. As a result, the amplification capabilities of different email middleware are concentrated to form highly amplified attack traffic. From the SMTP session state machine and email reflection behaviors, we identify many real-world email middleware suitable for COORDMAIL, including 10,079 bounce servers, 584 open email relays, and 6 email forwarding providers. By building SMTP command sequences, COORDMAIL can maintain prolonged SMTP communications with these middleware at an extremely low rate and control them to reflect emails steadily at any given moment. We show that COORDMAIL is effective at a low cost: 1000 SMTP connections can achieve more than 30,000 times of bandwidth amplification. While most existing security mechanisms are ineffective against COORDMAIL, we propose feasible mitigations that reduce the convergence amplification power of COORDMAIL by tens of times. We have responsibly reported COORDMAIL to email middleware and popular email providers, some of which have accepted our recommendations.

View More Papers

G-Prove: Gossip-Based Provenance for Scalable Detection of Cross-Domain Flow...

Moustapha Awwalou Diouf (SnT, University of Luxembourg), Maimouna Tamah Diao (SnT, University of Luxembourg), El-hacen Diallo (SnT, University of Luxembourg), Samuel Ouya (Cheikh Hamidou KANE Digital University), Jacques Klein (SnT, University of Luxembourg), Tegawendé F. Bissyandé (SnT, University of Luxembourg)

Read More

Was My Data Used for Training? Membership Inference in...

Xue Tan (Fudan University), Hao Luan (Fudan University), Mingyu Luo (Fudan University), Zhuyang Yu (Fudan University), Jun Dai (Worcester Polytechnic Institute), Xiaoyan Sun (Worcester Polytechnic Institute), Ping Chen (Fudan University)

Read More

Building Next-Generation Datasets for Provenance-Based Intrusion Detection

Qizhi Cai (Zhejiang University), Lingzhi Wang (Northwestern University), Yao Zhu (Zhejiang University), Zhipeng Chen (Zhejiang University), Xiangmin Shen (Hofstra University), Zhenyuan Li (Zhejiang University)

Read More