Suspected China-Aligned Cluster Targets University Roundcube Servers

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Summary

Proofpoint reports UNK_MassTraction has targeted U.S. and Canadian universities via malicious emails exploiting Roundcube flaws CVE-2024-42009 and CVE-2025-49113 to steal credentials and deploy backdoors. China alignment is a low-confidence assessment.

Key points

  • Proofpoint tracks the activity as UNK_MassTraction and says it has targeted universities in the U.S. and Canada, including physics, engineering, and national security-related research organizations.
  • Malicious emails, sent from compromised accounts or spoofed domains, exploit Roundcube’s CVE-2024-42009 cross-site scripting flaw to load the IceCube stealer.
  • Researchers say IceCube can collect usernames, passwords, cookies, two-factor authentication data, and browser information.
  • IceCube also attempts to exploit Roundcube’s CVE-2025-49113 deserialization flaw to install SquareShell, a PHP webshell with remote code execution capabilities.
  • If SquareShell is not installed, the malware downloads a shell script that loads the VShell backdoor in memory; VShell supports interactive shell access and port forwarding.
  • Proofpoint assesses that the cluster is likely China-aligned, citing infrastructure overlap and Chinese-language artifacts, but says the attribution is not high-confidence.
  • Administrators are advised to apply security updates addressing both Roundcube flaws and secure mail servers like other remote-access systems.

Article Details

Event Type
Cyber espionage campaign exploiting Roundcube vulnerabilities
Impact
The China-linked cluster UNK_MassTraction has targeted university mail servers in the United States and Canada to steal credentials and deploy backdoors. The reported payloads can collect passwords, cookies, two-factor authentication data, and browser information; successful installation of SquareShell can provide remote code execution on a mail server, while VShell provides remote access capabilities. Proofpoint describes the China-alignment attribution as an assessment with low confidence.

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