RedNovember Hackers Attacking Government and Technology Organizations to Deploy Backdoor

In mid-2024, cybersecurity professionals began observing a surge of targeted intrusions against government, defense, and technology organizations worldwide.

These incidents were linked to a previously uncharacterized threat group later christened RedNovember, which leverages open-source and commodity tools to deploy a stealthy Go-based backdoor.

Initial compromises often stemmed from the exploitation of Internet-facing devices—including VPN appliances, load balancers, and webmail portals—using publicly available proof-of-concept exploits.

Subsequent post-exploitation activities typically involved the deployment of the Pantegana command-and-control (C2) framework alongside variants of Cobalt Strike and SparkRAT, allowing operators to maintain long-term access and execute espionage activities undetected.

Recorded Future analysts identified RedNovember’s activity following a July 2025 reconnaissance wave targeting Ivanti Connect Secure VPN appliances across multiple regions.

During this campaign, operators scanned dozens of government ministries and private sector entities, then delivered a malicious Go loader masquerading as a legitimate software update.

Victims ranged from foreign affairs directorates in Southeast Asia to defense contractors in the United States, underscoring the group’s strategic focus on high-value targets.

The use of readily available exploits such as CVE-2024-3400 for Palo Alto GlobalProtect and CVE-2024-24919 for Check Point VPN gateways exemplifies RedNovember’s preference for rapid, high-volume initial access over bespoke malware development.

Observers have noted that the group’s operations accelerated in the wake of geopolitical events.

For instance, reconnaissance against Taiwanese research facilities coincided with Chinese military exercises in the Taiwan Strait, and extensive Panamanian government targeting followed high-level U.S. diplomatic visits.

The correlation between RedNovember activity and diplomatic or military movements suggests a state-sponsored intelligence motive, with the group harnessing open-source tools to obfuscate attribution and reduce operational costs.

This tactic magnifies the risk of widespread exploitation, as adversaries can quickly weaponize newly released proof-of-concept code without extensive development overhead.

Infection Mechanism

A critical component of RedNovember’s toolkit is LESLIELOADER, a Go-based loader that authenticates and decrypts its payload before executing it in memory.

The loader is distributed via spear-phishing emails containing a PDF lure document. Upon execution, LESLIELOADER performs an AES decryption routine to unpack SparkRAT or Cobalt Strike Beacon modules.

A simplified YARA rule from Recorded Future’s Appendix D illustrates this decryption behavior:-

rule MALLESLIELOADER {
    meta:
        author = "Insikt Group, Recorded Future"
        description = "Detects LESLIELOADER Malware used by RedNovember"
    strings:
        $s1 = ".DecrptogAES"
        $s2 = ".UnPaddingText1"
    condition:
        uint16(0) == 0x4D5A and all of ($s*)
}

Once deployed, the loader contacts a hardcoded domain (e.g., download.offiec.us.kg) over HTTP, retrieves the encrypted payload, and drops it into a temporary directory.

The AES keys—embedded within the binary—are used to decrypt the payload directly into memory, bypassing disk writes and evading traditional antivirus engines.

Following payload execution, the backdoor establishes persistence by creating a Windows registry Run key under HKCUSoftwareMicrosoftWindowsCurrentVersionRun and disabling event log features to hamper forensic auditing.

This combination of in-memory execution, encrypted payload delivery, and log manipulation enables RedNovember to maintain covert footholds for extended periods, granting operators the ability to exfiltrate sensitive data and perform lateral movement with minimal detection risk.

Despite the sophistication of these tactics, defenders can disrupt RedNovember’s operations by monitoring for known C2 domains, enforcing strict patch management on perimeter devices, and employing behavior-based detection capable of identifying in-memory loaders.

Continuous network segmentation and enhanced visibility on external-facing appliances remain crucial for mitigating this persistent threat.

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