Russian APT28's "Nearest Neighbor Attack": Weaponizing Nearby Wi-Fi Networks for Covert Access

In early 2022, Russian state-sponsored threat actor APT28 (also tracked as GruesomeLarch) deployed a novel attack vector dubbed the “Nearest Neighbor Attack,” compromising Ukrainian-affiliated targets by exploiting nearby Wi-Fi networks. This technique bypassed traditional perimeter defenses by daisy-chaining access through physically adjacent organizations, leveraging weak Wi-Fi authentication and living-off-the-land binaries (LoLBins). The attack underscores the growing sophistication of APT28’s tactics and the critical need for robust Wi-Fi security measures.

Key Takeaways for Security Teams

APT28’s campaign exploited proximity-based vulnerabilities, targeting organizations with weak Wi-Fi authentication. The attackers used credential spraying to gain initial access, then pivoted through dual-homed devices to bridge air-gapped networks. Unique anti-forensics techniques, such as secure file deletion via Cipher.exe, were employed to evade detection. Microsoft’s GooseEgg tool analysis confirmed the exploitation of CVE-2022-38028, further solidifying attribution.

Defenders should prioritize enforcing multi-factor authentication (MFA) for Wi-Fi access, segmenting wired and wireless networks, and monitoring suspicious usage of utilities like netsh and Cipher.exe.

Technical Breakdown of the Attack Chain

Stage 1: Initial Compromise via Credential Spraying

APT28 brute-forced credentials against a public-facing service at a Ukrainian-linked organization. While MFA prevented direct internet access, the enterprise Wi-Fi network only required domain credentials, allowing the attackers to pivot internally. This highlights the risks of relying solely on password-based authentication for Wi-Fi networks.

“The Wi-Fi network was not protected by MFA, meaning proximity to the target network and valid credentials were the only requirements to connect.” [Volexity]

Stage 2: Lateral Movement via Nearby Networks

From a remote location, APT28 compromised a secondary organization (Organization B) via VPN, then identified a dual-homed device (connected via both Ethernet and Wi-Fi). The attackers executed a custom PowerShell script to scan for SSIDs and connect to the target organization’s Wi-Fi network:

# Redacted snippet from APT28's PowerShell Wi-Fi scanner
Add-Type -AssemblyName System.Net.NetworkInformation;
$interfaces = [System.Net.NetworkInformation.NetworkInterface]::GetAllNetworkInterfaces() | Where-Object { $_.NetworkInterfaceType -eq 'Wireless80211' };
$interfaces | ForEach-Object { $_.GetIPProperties().UnicastAddresses | Where-Object { $_.Address.AddressFamily -eq 'InterNetwork' } };

Stage 3: Privilege Escalation and Data Exfiltration

APT28 leveraged a zero-day (CVE-2022-38028) via a batch script (servtask.bat) to dump SAM and SYSTEM registry hives. The attackers then compressed the data for exfiltration and used Cipher.exe /W:C to securely delete files—a rare tactic for this group.

Detection and Mitigation Strategies

Detection Signatures

EDR Rules: Alert on vssadmin.exe spawning reg save or Compress-Archive commands.
Network Monitoring: Flag NBNS queries from Wi-Fi clients revealing foreign Active Directory domains.
Wi-Fi Controllers: Log MAC addresses and signal strength to detect rogue dual-homed devices.

Mitigation Controls

Control	Implementation
Wi-Fi MFA	Deploy certificate-based authentication or RADIUS with MFA.
Network Segmentation	Isolate guest Wi-Fi from corporate VLANs.
Logging	Enable DHCP/Wi-Fi controller logs for MAC-IP-user mapping.

Conclusion

The Nearest Neighbor Attack demonstrates how advanced persistent threats (APTs) exploit physical proximity as a trusted vector. Organizations must treat Wi-Fi security with the same rigor as VPNs, particularly for high-value targets in dense urban areas. Continuous monitoring of lateral movement and living-off-the-land binaries remains critical to detecting and mitigating such threats.