MalVirt | .NET Virtualization Thrives in Malvertising Attacks

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MalVirt | .NET Virtualization Thrives in Malvertising Attacks

By Aleksandar Milenkoski and Tom Hegel
While investigating recent malvertising (malicious advertising) attacks, SentinelLabs spotted a cluster of virtualized malware loaders that has joined the trend. The loaders are implemented in .NET and use virtualization, based on the KoiVM virtualizing protector of .NET applications, in order to obfuscate their implementation and execution. We refer to these loaders as MalVirt (a recently observed and likely related implementation is referred to as KoiVM Loader). Although popular for hacking tools and cracks, the use of KoiVM virtualization is not often seen as an obfuscation method utilized by cybercrime threat actors.
Among the payloads that MalVirt loaders distribute, we spotted infostealer malware of the Formbook family as part of an ongoing campaign at the time of writing. The distribution of this malware through the MalVirt loaders is characterized by an unusual amount of applied anti-analysis and anti-detection techniques.
The current spikes in threat actors using alternative malware distribution methods to Office macros, such as malvertising, Windows Shortcuts (LNK files), and ISO files, comes as a response to Microsoft blocking by default Office macros in documents from the Internet. Malvertising is a malware delivery method that is currently very popular among threat actors, marked by a significant increase in malicious search engine advertisements in recent weeks.
The Formbook family – Formbook and its newer version XLoader –  is a feature-rich infostealer malware that implements a wide range of functionalities, such as keylogging, screenshot theft, theft of web and other credentials, and staging of additional malware. For example, one of the hallmarks of XLoader is its intricate disguising of C2 traffic.
This malware is sold on the dark web and is traditionally delivered as an attachment to phishing emails. While it is typically used by threat actors with cybercrime motivations, its use has also been recently observed as part of attacks with potentially political motivations – in September 2022,  Ukraine’s CERT reported a Formbook/XLoader campaign targeting Ukrainian state organizations through war-themed phishing emails. In the case of an intricate loader, this could suggest an attempt to co-opt cybercriminal distribution methods to load more targeted second-stage malware onto specific victims after initial validation.
We focus on the MalVirt loaders and the infostealer malware subsequently distributed by them in order to highlight the effort the threat actors have invested in evading detection and thwarting analysis.
We first spotted a MalVirt sample when performing a routine Google search for “Blender 3D” and examining the Ad results.
The MalVirt samples we analyzed have the PDB path 
They can be further characterized by obfuscated namespace, class, and function names composed of alphanumeric characters, such as Birthd1y or Tota2, in the same manner as previously observed Formbook loaders.
The loaders pretend to be digitally signed using signatures and countersignatures from companies such as Microsoft, Acer, DigiCert, Sectigo, and AVG Technologies USA. However, in each case the signatures are invalid, created using invalid certificates or are certificates untrusted by the system (i.e., not stored in the Trusted Root Certification Authorities certificate store). For example, the following certificate appears to be from Microsoft but doesn’t pass signature validation.
The MalVirt loaders we analyzed, especially those distributing malware of the Formbook family, implement a range of anti-analysis and anti-detection techniques, with some variations across MalVirt samples. For example, some samples patch the AmsiScanBuffer function implemented in amsi.dll to bypass the Anti Malware Scan Interface (AMSI) that detects malicious PowerShell commands.
Further, in an attempt to evade static detection mechanisms, some strings (such as amsi.dll and AmsiScanBuffer) are Base-64 encoded and AES-encrypted. The MalVirt loaders decode and decrypt such strings using hardcoded, Base64-encoded AES encryption keys.
We also observed MalVirt samples evaluating whether they are executing within a virtual machine or an application sandbox environment. For example, detecting the VirtualBox and VMWare environments involves querying the registry keys HKEY_LOCAL_MACHINESOFTWAREOracleVirtualBox Guest and HKEY_LOCAL_MACHINESOFTWAREVMware, Inc.VMware Tools, and evaluating the presence of the drivers vboxmouse.sys, vmmouse.sys, and vmhgfs.sys on victim systems.
Detecting the Wine and Sandboxie application sandbox environments involves evaluating the presence of the wine_get_unix_file_name function in the kernel32.dll Windows library and the SbieDll.dll Sandboxie library on victim systems.
We observed MalVirt samples deploying and loading the Process Explorer driver, part of the Windows Sysinternals toolset. This includes a sample (SHA-1: 15DB79699DCEF4EB5D731108AAD6F97B2DC0EC9C) that distributes malware of the Formbook family as part of an active campaign at the time of writing. An assembly named 0onfirm, which this sample reflectively loads, deploys the Process Explorer driver in the %LOCALAPPDATA%Temp directory under the name Иисус.sys. The driver has a valid digital signature issued by Microsoft using an expired certificate (validity period between 15 December 2020 and 12 December 2021).
0onfirm then deploys the driver by creating a service named TaskKill. The assembly creates the ImagePath, Start, Type, and ErrorControl registry values at HKEY_LOCAL_MACHINESYSTEMCurrentControlSetServicesTaskKill to deploy the driver and configure its loading at system start-up. The name TaskKill indicates the potential malicious use of Иисус.sys – process termination with kernel privileges.
Malware in general uses the Process Explorer driver to conduct activities with kernel privileges, such as killing processes of detection mechanisms to evade detection or duplicating process handles for tampering. For example, in late 2022, the use of the Иисус.sys driver was observed as part of the deployment (potentially also through a MalVirt loader) of a different payload – Agent Tesla. The open-source tool Backstab also demonstrates the malicious use of the Process Explorer driver.
A hallmark of the MalVirt loaders is the use of .NET virtualization as an anti-analysis and -detection technique. When executed, a MalVirt sample reflectively loads an assembly, such as 0onfirm,  which further orchestrates the staging of the final payload. These assemblies are virtualized using the KoiVM virtualizing protector of .NET applications, modified with additional obfuscation techniques. Code virtualization on its own is among the most advanced methods for obfuscating executables at this time.
Virtualization frameworks such as KoiVM obfuscate executables by replacing the original code, such as NET Common Intermediate Language (CIL) instructions, with virtualized code that only the virtualization framework understands. A virtual machine engine executes the virtualized code by translating it into the original code at runtime. When put to malicious use, virtualization makes malware analysis challenging and also represents an attempt to evade static analysis mechanisms.
Tools for the automated de-virtualization of virtualized executables using KoiVM, such as OldRod, can be very effective when facing the standard implementation of KoiVM. OldRod recompiles virtualized code into .NET CIL code in an attempt to recover the original code.
The current standard implementation of KoiVM defines 119 constant variables that the framework uses to virtualize code constructs. These constructs include, for example, flag and instruction opcode definitions. The variables are grouped and ordered according to the constructs they virtualize.
When initialized, KoiVM assigns values to these variables in a designated routine. This is a crucial component of the KoiVM virtualization process. Automated de-virtualization involves detecting this routine by searching for assignment instructions, and using the assigned values to recompile the virtualized code to its native form. However, MalVirt makes automated de-virtualization challenging by using a modified version of the standard KoiVM implementation with obfuscation techniques.
The designated KoiVM routine is obfuscated such that it conducts arithmetic operations instead of concise assignments. This is to confuse devirtualization frameworks, such as OldRod, attempting to detect the routine and extract the variable values crucial for accurate de-virtualization.
To defeat this obfuscation technique, the values that the modified implementation of KoiVM assigns to the constant variables can be extracted from the memory of the virtualized MalVirt assembly while it executes. The routine can then be patched such that it assigns the appropriate value to each constant variable using concise assignments. This helps a de-virtualization framework to detect the routine and extract the values.
However, the modified implementation of KoiVM used by MalVirt adds yet another layer of obfuscation – it distorts the original order of the constant variables defined by the standard KoiVM implementation. This confuses de-virtualization frameworks and may lead to incorrect de-virtualization.
Restoring the original order can be a very challenging and time-consuming task. This involves the manual inference of the constructs that each of the 119 variables is used for based on code analysis. Alternatively, one could develop logic to automate this activity, which may prove to be an equally challenging endeavor.
The infostealer malware samples that the MalVirt loaders distribute are part of an on-going campaign at the time of writing. A campaign is marked by an identifier that is present in HTTP POST and GET requests issued by the malware.
Formbook and XLoader have traditionally been distributed via phishing emails and malspam via Macro-enabled Office documents. Our observation of malware of the Formbook family being distributed through MalVirt loaders suggests that it is likely that Formbook and/or XLoader are being (or will be) distributed via malvertising as well. This follows the trend of crimeware actors in their quick shift into Google malvertising.
In addition to the MalVirt loaders, Formbook and XLoader themselves implement considerable protection against analysis and detection, both at executable- and network-level.
Formbook and XLoader disguise real C2 traffic among smokescreen HTTP requests with encoded and encrypted content to multiple domains, randomly selected from an embedded list. Only one of the domains is the real C2 server and the rest are decoys. A sample we analyzed issued HTTP GET and/or POST requests with encoded and encrypted HTTP data to 17 domains (16 endpoints) listed in the IOC table below. Previous research provides detailed information on how XLoader in particular implements this technique.
The technique of camouflaging the true C2 domain through beaconing to multiple domains remains consistent with the previously noted research. The malware beacons to domains containing legitimate and/or unused registered domains. As shown in the following image, as a snapshot of some domains the malware contacts, there is a wide variety of domain times, hosting providers, and age between their relevant registration date.
The domains are hosted by a range of providers including Choopa, Namecheap, and multiple others. The random approach to domain selection is beyond the scope of this report; however, it remains a highly effective way of concealing true C2s. XLoader’s recent infrastructure concealing techniques in particular should serve as an example of how crimeware can be even more technically sophisticated than many of today’s APTs.
At an executable-level, among other anti-analysis techniques, the malware detects the presence of user- and kernel-land debuggers using the NtQueryInformationProcess and NtQuerySystemInformation functions by specifying the ProcessDebugPort (0x7) and SystemKernelDebuggerInformation (0x23) information classes. Previous research provides a detailed overview of the implemented anti-analysis and -detection techniques.
As a response to Microsoft blocking Office macros by default in documents from the Internet, threat actors have turned to alternative malware distribution methods – most recently, malvertising. The MalVirt loaders we observed demonstrate just how much effort threat actors are investing in evading detection and thwarting analysis.
Malware of the Formbook family is a highly capable infostealer that is deployed through the application of a significant amount of anti-analysis and anti-detection techniques by the MalVirt loaders. Traditionally distributed as an attachment to phishing emails, we assess that threat actors distributing this malware are likely joining the malvertising trend.
Given the massive size of the audience threat actors can reach through malvertising, we expect malware to continue being distributed using this method.
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In the era of interconnectivity, when markets, geographies, and jurisdictions merge in the melting pot of the digital domain, the perils of the threat ecosystem become unparalleled. Crimeware families achieve an unparalleled level of technical sophistication, APT groups are competing in fully-fledged cyber warfare, while once decentralized and scattered threat actors are forming adamant alliances of operating as elite corporate espionage teams.
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