HomeLatest UpdatesEnter The Gates: An Analysis of the DarkGate AutoIt Loader

Enter The Gates: An Analysis of the DarkGate AutoIt Loader


AutoIt is a scripting language designed for automating the Windows GUI and general scripting. Over the years, it has been utilized for malicious purposes, including AutoIt-compiled malware, which dates back to as early as 2008.

Malware creators have exploited the versatility of AutoIT in a variety of ways, such as using obfuscated scripts for payload decryption, utilizing legitimate tools like BaSupportVNC, and even creating worms capable of spreading through removable media and Windows shares.

DarkGate is one of the malware that uses Auto-It compiled loaders that poses a significant threat due to its sophisticated evasion techniques and persistence within compromised systems. The malware employs multi-stage payloads and leverages obfuscated AutoIt scripting, complicating its identification through traditional signature-based methods. Its ability to exfiltrate sensitive data and establish command and control communications demands vigilant detection and analysis.

In this blog, the Splunk Threat Research Team (STRT) provides a deep dive analysis of DarkGate malware and its use of AutoIt. Below, we’ll cover:

  • The DarkGate loader and campaign flow
  • DarkGate Tactics, Techniques, and Procedures
  • Atomic Test for AutoIt malware
  • DarkGate detections from the Splunk Threat Research Team

Loader/Campaign Flow

The Splunk Threat Research Team has identified multiple campaigns deploying a loader designed to initiate DarkGate on compromised hosts. One such instance involves the discovery of malicious PDF files, detected and submitted to Splunk Attack Analyzer. The PDF file acts as a carrier, triggering a sequence where a malicious CAB file is downloaded. This CAB file, in turn, fetches a .MSI file, which contains and loads the DarkGate malware payload.

This chain of events showcases a method employed by threat actors, utilizing seemingly maliciously crafted PDF files as a gateway to execute a sequence resulting in the installation of the DarkGate malware. The multi-stage nature of this attack demonstrates the intricacy and stealth employed by adversaries to infiltrate and compromise targeted systems.

Figure 1: Analysis of malicious PDF infection chain in Splunk Attack Analyzer

In Figure 1, a detailed diagram showcases the .MSI file’s functionality, executing its role in the orchestration of DarkGate’s deployment. This file manifests a sequence where it loads multiple components, including the legitimate wndbg.exe, a DLL module, and two .BIN files, all instrumental in the execution of DarkGate.

Moreover, the Splunk Threat Research Team found another variant of this malicious .MSI. This variant extends its infection strategy by introducing an additional .CAB installer into the installation process on the targeted host. This augmented approach further amplifies the complexity and sophistication of the infection methodology adopted by threat actors, emphasizing their persistent efforts to evade detections.

Upon analysis and reverse engineering of the .MSI file, our investigation unveiled a loader execution flow with a series of file executions, as visualized in Figure 2.

Figure 2: Malicious MSI Infection Flow (For a larger resolution of this diagram visit this link)

We’ve segmented the loader execution flow into four distinct phases:

  • Phase 1: .MSI executes .CAB
  • Phase 2: Exploiting DLL side-loading through Wndbg.exe
  • Phase 3: The AutoIt loader
  • Phase 4: The final loader

Below, we’ll dive into each of these phases to elaborate on the specific files and processes initiated by the .MSI file, which ultimately lead to the decryption of the actual DarkGate malware.

Phase 1: .MSI Executes .CAB

The initial phase of the execution flow involves the .MSI file attempting to launch its primary component, an embedded .CAB file labeled “Binary.bz.WrappedSetupProgram.” This component serves as a pivotal element within the MSI’s operational sequence, marking the outset of its intended execution path.

Figure 3: Binary.bz.WrappedSetupProgram Query for Execution

Within the .CAB file, a collection of files has been identified, as depicted in Figure 4. Among these files, the pivotal components driving the initiation of DarkGate malware include windbg.exe, dbgeng.dll, data.bin, and data2.bin. 

However, it’s important to note that the four .png files are utilized solely as decoys or dummies in this specific scenario, designed to obfuscate or mislead the observer from the critical components of the DarkGate execution.

Figure 4: .CAB Extracted Files

Phase 2: Exploiting DLL Side-Loading Through Wndbg.exe

The next phase in installing this malicious .CAB file involves the execution of a specially crafted dbgeng.dll using DLL side-loading techniques via windbg.exe. This process essentially entails windbg.exe automatically loading the dbgeng.dll, facilitating the progression of the malicious code.

The dbgeng.dll module functions to read and decode the contents of the base64 encoded data.bin file, utilizing customized base64 character sets for decoding purposes. The decoded data.bin is actually an executable that will process the data2.bin.

Figure 5: data.bin decoded

Phase 3: The AutoIt Loader 

In this phase, the decrypted .exe from the data.bin file proceeds to decode the data2.bin file. Unlike its predecessor, data2.bin holds two encoded files, separated by the ‘splitres’ string.

The first decoded file resulting from the base64 process is a valid Autoit3.exe, employed to execute the second file: a compiled AutoIt script named script.au3. Both files are dropped within the ‘c:\tmpa’ directory and executed through the straightforward commandline directive. 

c:\tmpa\Autoit3.exe c:\tmpa\script.au3

Figure 6: Decoded files from data2.bin

Figure 7: Command line for execution of compiled AutoIt script

As part of our analysis, we decompiled the script.au3 file to unveil the underlying AutoIt script. This exploration was crucial to understand the full scope and behavior of this malicious script, allowing us to gain insight into its complete functionality and operational behavior.

Figure 8 presents a code snippet from the decompiled script.au3, revealing the initialization phase along with numerous concatenations of hexadecimal strings stored within the ‘oylnnnhx’ variable. This concatenated content constitutes a shellcode encapsulated with an .exe file, set to execute using the ‘Execute’ command in AutoIt. Additionally, we’ve included the de-obfuscated version of all ‘BinaryToString’ values in commented format. This provides a comprehensive view of the entire process, including how it was executed by leveraging the callback function of the EnumWindows() API.

Figure 8: Decompiled script.au3

Phase 4: The Final Loader

The final loader encompasses both shellcode and an .exe file designed to decrypt the DarkGate malware. Notably, the shellcode employs an intriguing technique utilizing the ‘MZ’ or DOS header bytes from the embedded win32 PE within its code as part of its shellcode to initiate execution at the win32 PE file entry point. This methodology mirrors a technique employed by the Cobalt Strike beacon, as documented in tccontre’s blog.

Figure 9: MZ header as shellcode

The embedded win32 PE file, triggered by the shellcode execution, will read the compiled AutoIt script script.au3. Its primary objective is to search for a specific string recognized as the AutoIt script compiled bytes header, denoted by ‘AU3!EA06.’ This string search operation holds significance, as it aims to pinpoint an essential 8-byte decryption key instrumental in decrypting the DarkGate malware. The 8-byte decryption key is placed right after the ‘AU3!EA06’ string.

Figure 10: Search for AU3!EA06 bytes header

Figure 11 illustrates the decryption process of the encrypted DarkGate malware employing an 8-byte decryption key through a straightforward XOR operation.

Figure 11: Decrypting Darkgate malware

DarkGate Tactics, Techniques, and Procedures

There are a number of Tactics, Techniques, and Procedures (TTPs) related to DarkGate — too many for us to cover a single blog post. Other blogs have covered some of these, such as:

  • Information theft through key logging
  • Leveraging remote connections
  • Establishing persistence via registry run keys
  • Browser Information Stealer
  • C2 communication

Therefore, in this post we’re going to highlight four TTPs we haven’t seen covered as much:

  • Lateral movement via PSEXEC
  • Malicious download and execution (CryptoMiner)
  • Proxy Setup
  • RDP Configuration

Lateral Movement via PSEXEC

DarkGate leverages PSEXEC for its privilege escalation capabilities and potentially for lateral movement within compromised networks, enabling the exfiltration or collection of sensitive information

Figure 12: Psexec Execution

Malicious Download and Execution (CryptoMiner)

DarkGate possesses the capability to download and install a malicious CryptoMiner malware on the compromised host, constituting a part of its malicious behavior and exploitation of the compromised system.

Figure 13: Installation of CryptoMiner

Proxy Setup

This malware will also try to enable proxy and set up a proxy server in the compromised host to anonymize its communications. It can route its traffic through the proxy, obscuring the actual source of the communication, which can make it harder to trace back to the attacker.

Figure 14: Proxy Setup

RDP Configuration

DarkGate also manipulates multiple registry settings related to Remote Desktop Protocol (RDP) configurations on the compromised host. These alterations grant DarkGate control over the system through this protocol, allowing the malware to potentially modify RDP settings to suit its operational needs or facilitate remote access and control.

Figure 15: RDP Settings

Atomic Testing

For testing purposes, we wanted to create a new Atomic Test that folks may load up and begin utilizing right away. This Atomic test is centered around the AutoIt3 execution. 

attack_technique: T1059
display_name: Command and Scripting Interpreter
atomic_tests:
- name: AutoIt Message Box Test with Download and Extract
  description: |
    Downloads AutoIt to the temporary directory, extracts it, and executes an AutoIt script that shows a message box.
  supported_platforms:
    - windows
  input_arguments:
    autoit_script_path:
      description: The local path to the AutoIt script to execute
      type: Path
      default: "PathToAtomicsFolder\\T1059\\src\\automsgbox.au3"
  executor:
    name: powershell
    elevation_required: false
    command: |
      $ErrorActionPreference="Stop";
      $autoitExePath = "$env:TEMP\\autoit-v3\\install\\autoit3.exe";
      if (-not (Test-Path -Path $autoitExePath)) {
        iwr 'https://www.autoitscript.com/cgi-bin/getfile.pl?autoit3/autoit-v3.zip' -OutFile "$env:TEMP\\autoit-v3.zip";
        Expand-Archive -LiteralPath "$env:TEMP\\autoit-v3.zip" -DestinationPath "$env:TEMP\\autoit-v3";
      }
      Start-Process -FilePath $autoitExePath -ArgumentList (Resolve-Path "#{autoit_script_path}").Path;

Save this to where Autoit3.exe can access:

Automsgbox.au3

MsgBox(0, "Atomic Message", "hello from Atomic Red Team")

The Atomic test will download AutoIT3.exe, and run the automsgbox.au3 file. 

A successful run will have a message box popup:

Figure 16: AutoIt Atomic Test

The telemetry traces will now be left correlating with the security content that has been generated. 

Security Content

The Splunk Threat Research Team has curated relevant detections and tagged them to the DarkGate Analytic Story to help security analysts detect adversaries leveraging the malware. 

This release used and considered relevant data endpoint telemetry sources such as:

  • Process Execution & Command Line Logging
  • Windows Security SACL Event ID, Sysmon, or any Common Information Model-compliant EDR technology
  • Windows Security Event Log
  • Windows System Event Log
  • Windows PowerShell Script Block Logging 

Below are some of the analytic SPL searches that the Splunk Threat Research Team developed for DarkGate malware. 

Windows Credentials from Password Stores Creation

This analytic identifies a process execution of Windows OS’s cmdkey.exe tool. This tool is being abused or used by several post exploitation tools and malware such as Darkgate to create stored user names, passwords or credentials in the targeted Windows OS host.

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes 
  where Processes.process_name="cmdkey.exe" OR Processes.original_file_name = "cmdkey.exe" AND Processes.process = "*/generic*" Processes.process IN ("*/user*", "*/password*")
  by Processes.process_name Processes.original_file_name Processes.process Processes.process_id
  Processes.process_guid Processes.parent_process_name Processes.parent_process Processes.parent_process_guid Processes.dest Processes.user 
  | `drop_dm_object_name(Processes)` 
  | `security_content_ctime(firstTime)`
  | `security_content_ctime(lastTime)` 
  | `windows_credentials_from_password_stores_creation_filter`

Figure 17: Detection Test 1

Windows Modify Registry DisableRemoteDesktopAntiAlias

This analytic identifies a modification in the Windows registry to DisableRemoteDesktopAntiAlias. This registry setting might be intended to manage or control anti-aliasing behavior (smoothing of edges and fonts) within Remote Desktop sessions. 

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry 
  where Registry.registry_path = "*\\Terminal Services\\DisableRemoteDesktopAntiAlias"  Registry.registry_value_data = 0x00000001
  by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action Registry.user Registry.dest 
  | `drop_dm_object_name(Registry)` 
  | `security_content_ctime(firstTime)`
  | `security_content_ctime(lastTime)` 
  | `windows_modify_registry_disableremotedesktopantialias_filter`

 Figure 18: Detection Test 2

Windows Modify Registry DontShowUI

This analytic identifies a modification in the Windows Error Reporting registry. This registry value is present and set to a specific configuration that influences the behavior of error reporting dialogs or prompts, suppressing them from being displayed to the user. For instance, setting DontShowUI to a value of 1 often indicates that the Windows Error Reporting UI prompts will be suppressed, meaning users won’t see error reporting pop-ups when errors occur.

| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Registry 
  where Registry.registry_path = "*\\SOFTWARE\\Microsoft\\Windows\\Windows Error Reporting\\DontShowUI"  Registry.registry_value_data = 0x00000001
  by  Registry.registry_path Registry.registry_value_name Registry.registry_value_data Registry.process_guid Registry.action Registry.user Registry.dest 
  | `drop_dm_object_name(Registry)` 
  | `security_content_ctime(firstTime)`
  | `security_content_ctime(lastTime)` 
  | `windows_modify_registry_dontshowui_filter`

Figure 18: Detection Test 3

Overall, the DarkGate Analytic Story introduces 41 detections across MITRE ATT&CK techniques. The table below provides details on the indicators of compromise (IOCs) the Splunk Threat Research Team analyzed to develop the analytic story, which were the DarkGate phishing attachment and two loader hashes.

SHA256

Description

7257b4ccec0ceb27b6fb141ce12c8dfb8a401d3edfaeca12699561eccda5a23e

JE412OO-OCT26.pdf

7a92489050089498d6ec05fb7bdfad37da13bb965023d126c41789c5756e4e02

146129.msi

8b6c6c007efa8e1a7da241564142f8a8a934dcce451c7e522cdd86292e81ead7

Another .msi darkgate loader

In Summary

By understanding DarkGate malware’s behaviors, the Splunk Threat Research Team was able to generate telemetry and datasets to develop and test Splunk detections to help defend against and respond to this threat. Security analysts, blue teamers and Splunk customers can use the insights and detections described in this blog to discover DarkGate tactics, techniques and procedures potentially being used by threat actors and adversaries in their environments. 

Early detection of DarkGate activities enables prompt containment and remediation, mitigating potential damage and preventing further propagation. Collaborative sharing of threat intelligence across security communities is crucial to enhance collective defense strategies. Continuous monitoring, alongside updated defense mechanisms, is essential to keep pace with DarkGate’s evolving tactics and ensure robust protection against its threats.

Learn More

You can find the latest Splunk content about security analytic stories on GitHub and in Splunkbase. Splunk Security Essentials also has all these detections now available via push update. 

For a full list of security content, check out the release notes on Splunk Docs.

Feedback

Any feedback or requests? Feel free to put in an issue on Github and we’ll follow up. Alternatively, join us on the Slack channel #security-research. Follow these instructions if you need an invitation to our Splunk user groups on Slack.

Contributors

We would like to thank Teoderick Contreras and Michael Haag for authoring this post and the entire Splunk Threat Research Team for their contributions, including Mauricio Velazco, Lou Stella, Bhavin Patel, Rod Soto, Eric McGinnis, and Patrick Bareiss.





Source link

RELATED ARTICLES

Most Popular

Recent Comments