APT

Understanding and Detecting DarkGate Malware

fuffsec

20 Jul 2024 • 3 min read

Introduction

DarkGate is a sophisticated malware-as-a-service (MaaS) operation that has been active since at least 2018. Over the years, it has evolved significantly, incorporating advanced techniques to evade detection and maintain persistence on compromised systems. This blog delves into the recent activities and evolution of DarkGate, focusing on detection mechanisms to protect against its sophisticated attack vectors.

Overview of DarkGate Campaigns

In recent months, DarkGate has been at the center of several cyberattack campaigns, notably leveraging Samba file shares to spread infections. Palo Alto Networks Unit 42 documented a campaign in March and April 2024 that used public-facing Samba shares to host Visual Basic Script (VBS) and JavaScript files, targeting regions including North America, Europe, and parts of Asia.

DarkGate employs various tactics to compromise systems, including phishing emails with malicious Excel attachments that execute VBS or JavaScript to download and run PowerShell scripts. These scripts then fetch and execute the final payload, often an AutoHotKey-based DarkGate package.

FIGURE 6: THE FOUR STAGES OF THE UNPACKING TECHNIQUE — [3]

Technical Details of DarkGate

DarkGate is a versatile malware with a range of capabilities, including:

Remote Control: Allows attackers to remotely control infected hosts.
Code Execution: Executes arbitrary code on compromised systems.
Cryptocurrency Mining: Mines cryptocurrencies using the infected host’s resources. [Removed on recent version]
Reverse Shells: Opens reverse shells for remote access.
Payload Deployment: Drops additional malicious payloads. [c2]
Evasion Techniques: Detects virtual environments and debuggers, and avoids analysis.

DarkGate communicates with its command-and-control (C2) servers using unencrypted HTTP requests, with data obfuscated and encoded in Base64-like encoding text.

Detection Strategies for DarkGate

Given its sophisticated techniques and rapid evolution, detecting DarkGate requires a multi-layered approach. Below are some key strategies:

1. Network Traffic Analysis

DarkGate's C2 communication, though obfuscated, uses HTTP requests. Monitoring network traffic for unusual HTTP requests, especially those containing Base64-encoded data, can help identify potential infections. Implementing network intrusion detection systems (NIDS) with rules to detect these patterns is crucial.

2. Behavioral Analysis

DarkGate exhibits specific behaviors such as:

Downloading and executing scripts from Samba shares.
Invoking PowerShell commands from Office documents.
Using AutoHotKey scripts in unusual contexts.

Implementing endpoint detection and response (EDR) solutions that monitor and analyze these behaviors can provide early indicators of compromise.

3. File Integrity Monitoring

Monitoring file integrity, particularly in directories commonly used by Samba and AutoHotKey, can alert to unauthorized changes. Implementing file integrity monitoring (FIM) solutions can detect modifications to critical files and scripts used by DarkGate.

4. Sandboxing and Automated Analysis

Using sandbox environments to analyze suspicious files and emails can help identify DarkGate’s payloads and execution chains. Automated analysis tools can detect the tell-tale signs of DarkGate, such as PowerShell command execution and AutoHotKey script activities.

5. Signature-Based Detection

While DarkGate frequently updates its techniques, maintaining up-to-date signatures for known malicious scripts, payloads, and network patterns can still be effective. Regularly updating antivirus and intrusion detection system (IDS) signatures is essential.

Example Detection Rules

Below are examples of detection rules that can be implemented to identify DarkGate activities:

YARA Rule for AutoHotKey Scripts

rule DarkGate_AutoHotKey
{
    meta:
        description = "Detects AutoHotKey scripts used by DarkGate"
    strings:
        $ahk_string1 = "AutoHotKey"
        $darkgate_string1 = "DarkGate"
    condition:
        all of them
}

Suricata Rule for Base64-Encoded HTTP Traffic (Must be edited, Generic)

alert http any any -> any any (msg:"DarkGate C2 Communication"; content:"HTTP/1.1"; http_header; content:"Base64"; fast_pattern; sid:1000001; rev:1;)

Conclusion

DarkGate continues to be a potent threat, leveraging creative methods to distribute its malware and evade detection. Implementing robust detection mechanisms, including network traffic analysis, behavioral analysis, file integrity monitoring, sandboxing, and signature-based detection, is essential to mitigate the risks posed by DarkGate. Staying informed about the latest threat intelligence and continuously updating detection strategies will enhance your cybersecurity posture against this evolving malware.

By understanding and deploying these detection techniques, organizations can better protect themselves against the sophisticated tactics employed by DarkGate and similar threats.