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June 19, 2023

Darktrace Detection of 3CX Supply Chain Attack

Explore how the 3CX supply chain compromise was uncovered, revealing key insights into the detection of sophisticated cyber threats.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Nahisha Nobregas
SOC Analyst
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19
Jun 2023

Ever since the discovery of the SolarWinds hack that affected tens of thousands of organizations around the world in 2020, supply chain compromises have remained at the forefront of the minds of security teams and continue to pose a significant threat to their business operations. 

Supply chain compromises can have far-reaching implications, from disrupting an organization’s daily operations, incurring huge financial and reputational damage, to affecting the critical infrastructure of entire countries. As such, it is essential for organizations to have effective security measures in place able to identify and halt these attacks at the earliest possible stage.

In March 2023 the 3CX Desktop application became the latest victim of a supply chain compromise dubbed as the “SmoothOperator” by SentinelOne. This application is used by over 600,000 companies worldwide and the customer list contains high-profile customers across a variety of industries [2]. The 3CX Desktop application is a Voice over Internet Protocol (VoIP) communication software for enterprises that allows for chats, video calls, and voice calls. [3] The 3CX installers for both Windows and macOS systems were affected by information stealing malware. Researchers were able to discern that threat actors also known as UNC 4736 related to financially motivated North Korean operators also known as AppleJeus were responsible for the supply chain compromise.  Researchers have also linked it to another supply chain compromise that occurred prior on the Trading Technologies X_TRADER platform, making this the first known cascading software supply chain compromise used to distribute malware on a wide scale and still be able to align operator interests. [3] Customer reports following the compromise began to surface about the 3CX software being picked up as malicious by several cybersecurity vendors such as CrowdStrike, SentinelOne, and Palo Alto Networks. [6] 

By leveraging integrations with other security vendors like CrowdStrike and SentinelOne, Darktrace DETECT™ was able to identify activity from the “SmoothOperator” across the customer base at multiple stages of the kill chain in March 2023. Darktrace RESPOND™ was then able to autonomously intervene against these emerging threats, preventing significant disruption to customer networks. 

Background on the first known cascading supply chain attack 

Initial Access

In April 2023, security researchers identified the initial target in this story was not the 3CX desktop application, rather, it was another software application called X_TRADER by Trading Technologies. [3] Trading Technologies is a provider that offers high-performance financial trading packages, allowing financial professionals to analyze and trade assets within the stock market more efficiently. Unfortunately, a compromise already existed in the supply chain for this organization. The X_TRADER installer, which had been retired in 2020, still had its code signing certificate set to expire in October 2022. This code signing certificate was exploited by attackers to digitally sign the malicious software. [3] It also inopportunely led to 3CX when an employee unknowingly downloaded a trojanized installer for the X_TRADER software from Trading Technologies prior to the certificate’s expiration. [4]. This compromise of 3CX via X_TRADER was the first case of a cascading supply chain attack reported on within the wider threat landscape. 

Persistence and Privilege Escalation 

Following these findings, researchers were able to identify the likely kill chain that occurred on Windows systems, beginning with the download of the 3CX DesktopApp installer that executed an executable (.exe) file before dropping two trojanized Data Link Libraries (DLLs) alongside a benign executable that was used to sideload malicious DLLs. These DLLs contained and used SIGFLIP and DAVESHELL; both publicly available projects. [3] In this case, the DLLs were used to decrypt using an RC4 key and load a payload into the memory of a compromised system. [3] SIGFLIP and DAVESHELL also extract and decrypt the modular backdoor named VEILEDSIGNAL, which also contains a command and control (C2) configuration. This malware allowed the North Korean threat operators to gain administrative control to the 3CX employee’s device. [3] This was followed by access to the employee’s corporate credentials, ultimately leading to access to 3CX systems. [4] 

Lateral Movement and C2 activity

Security researchers were also able to identify other malware families that were mainly utilized in the supply chain attack to move laterally within the 3CX environment, and allow for C2 communication [3], these malware families are detailed below:

  • TaxHaul: when executed it decrypts shellcode payload, observed by Mandiant to persist via DLL search-order hijacking.
  • Coldcat: complex downloader, which also beacons to a C2 infrastructure.
  • PoolRat: collects system information and executes commands. This is the malware that was found to affect macOS systems.
  • IconicStealer: served as a third stage payload on 3CX systems to steal data or information.

Furthermore, it was also reported early on by Kaspersky that a backdoor named Gopuram, routinely used by the North Korean threat actors Lazarus and typically used against cryptocurrency companies, was also used as a second stage payload on a limited number of 3CX’s customers compromised systems. [5]

3CX detections observed by Darktrace

CrowdStrike and SentinelOne, two of the major detection platforms with which Darktrace partners through security integrations, initially revealed that their platforms had identified the campaign appeared to be targeting 3CXDesktopApp customers in March 2023. 

At this time, Darktrace was also observing this activity and alerting customers to unusual behavior on their networks. [1][7] Darktrace DETECT identified activity related to the supply chain compromise primarily through host-level alerts associated with CrowdStrike and SentinelOne integrations, as well as model breaches related to lateral movement and C2 activity. 

Some of the activity related to the 3CX supply chain compromise that Darktrace detected was observed solely via integration models picking up executable and Microsoft Software Installer (msi) file downloads for the 3CXDesktopApp, suggesting the compromise likely was stopped at the endpoint device. 

CrowdStrike integration model breach identifying 3CXDesktopApp[.]exe as possible malware
Figure 1: CrowdStrike integration model breach identifying 3CXDesktopApp[.]exe as possible malware on March 30, 2023.
showcases the Model Breach Event Log for the CrowdStrike integration model breach
Figure 2: The above figure, showcases the Model Breach Event Log for the CrowdStrike integration model breach shown in Figure 1.

In another case highlighted in Figure 3 and 4, security platforms were associating 3CX as malicious. The device in these figures was observed downloading a 3CXDesktopApp executable followed by an msi file about an hour later. This pattern of activity correlates with the compromise process that had been on reported, where the “SmoothOperator” malware that affected 3CX systems was able to persist through DLL side-loading of malicious DLL files delivered with benign executable files, making it difficult for traditional security tools to detect. [2][3][7]

The activity in this case was detected by the DETECT integration model, ‘High Severity Integration Malware Detection’ and was later blocked by the Darktrace RESPOND/Network model, ‘Antigena Significant Anomaly from Client Block’ which applied the “Enforce Pattern of Life” action to intercept the malicious download that was taking place. Darktrace RESPOND uses AI to learn every devices normal pattern of life and act autonomously to enforce its normal activity. In this event, RESPOND would not only intercept the malicious download that was taking place on the device, but also not allow the device to significantly deviate from its normal pattern of activity.

The Model Breach Event log for the device displays the moment in which the SentinelOne integration model breached for the 3CXDesktopApp.exe file
Figure 3: The Model Breach Event log for the device displays the moment in which the SentinelOne integration model breached for the 3CXDesktopApp.exe file followed subsequently by the RESPOND model, ‘Antigena Significant Anomaly from Client Block’, on March 29, 2023.
Another ‘High Severity Integration Malware Detection’ breached
Figure 4: Another ‘High Severity Integration Malware Detection’ breached for the same device in Figure 3 approximately one hour later because of the msi file, 3CXDesktopApp-18.12.416.msi, which also led to the Darktrace RESPOND model, ‘Antigena Significant Anomaly from Client Block’, on March 29, 2023.

In a separate case, Darktrace also detected a device performing unusual SMB drive writes for the file ‘3CXDesktopApp-18.10.461.msi’. This breached the DETECT model ‘SMB Drive Write’. This model detects when a device starts writing files to another internal device it does not usually communicate with via the SMB protocol using the admin$ or drive shares.

This Model Breach Event log highlights the moment Darktrace captured the msi application file for the 3CXDesktopApp being transferred internally on this customer’s network
Figure 5: This Model Breach Event log highlights the moment Darktrace captured the msi application file for the 3CXDesktopApp being transferred internally on this customer’s network, this was picked up as new activity for the device on March 28, 2023. 

In a couple of other cases observed by Darktrace, connections detected were made from affected devices to 3CX compromise related endpoints. In Figure 6, the device in question was detected connecting to the endpoint, journalide[.]org. This breached the model, ‘Suspicious Self-Signed SSL’, which looks for connections being made to an endpoint with a self-signed SSL certificate which is designed to look legitimate, as self-signed certificates are often used in malware communication.

Model Breach Event log for connections to the 3CX C2 related endpoint
Figure 6: Model Breach Event log for connections to the 3CX C2 related endpoint, journalide[.]org, these connections breached the model Suspicious Self-Signed SSL on April 24, 2023.

On another Darktrace customer environment, a 3CX C2 endpoint, pbxphonenetwork[.]com, had already been added to the Watched Domains list around the time reports of the 3CX application software being malicious had been reported. The Watched Domains list allows Darktrace to detect if any device on the network makes connections to these domains with more scrutiny and breach a model for further visibility of threats on the network. Activity in this case was detected and subsequently blocked by a Darktrace RESPOND action, “Block connections to 89.45.67[.]160 port 443 and pbxphonenetwork[.]com on port 443”, blocking the device from connecting to this 3CX C2 endpoints on the spot (see Figure 7). This activity subsequently breached the RESPOND model, ‘Antigena Watched Domain Block’. 

Figure 7: History log of the Darktrace RESPOND action applied to the device breaching the Darktrace RESPOND model, Antigena Watched Domain Block and applying the action, “Block connections to 89.45.67[.]160 port 443 and pbxphonenetwork[.]com on port 443” on March 31, 2023.

Darktrace Coverage 

Utilizing integrations with Darktrace such as those with CrowdStrike and SentinelOne, Darktrace was able to detect and respond to activity identified as malicious 3CX activity by CrowdStrike and SentinelOne as seen in Figures 1, 2, 3, and 4. This activity breached the following Darktrace DETECT models: 

  • Integration / CrowdStrike Alert
  • Security Integration / High Severity Integration Malware Detection

Darktrace was also able to identify lateral movement activity such as in the case illustrated in Figure 5.

  • Compliance / SMB Drive Write

Lastly, C2 beaconing activity from malicious endpoints associated with the 3CX compromise was also detected as seen in Figure 6, this activity breached the following Darktrace DETECT model:

  • Anomalous Connection / Suspicious Self-Signed SSL

For customers with Darktrace RESPOND configured in autonomous response mode, Darktrace RESPOND models also breached to activity related to the 3CX supply chain compromise as seen in Figures 3, 4, and 7. Below are the models that breached and the following autonomous actions that were applied:

  • Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block, “Enforce pattern of life”
  • Antigena / Network / External Threat / Antigena Watched Domain Block, “Block connections to 89.45.67[.]160 port 443 and pbxphonenetwork[.]com on port 443”

Conclusion 

The first known cascading supply chain compromise occurred inopportunely for 3CX but conveniently for UNC 4736 North Korean threat actors. This “SmoothOperator” compromise was detected by endpoint security platforms such as CrowdStrike who was at the cusp of this discovery when it became one of the first platforms to report on malicious activity related to the 3CX DesktopApp supply chain compromise.  

Although still novel at the time and largely without reported indicators of compromise, Darktrace was able to capture and identify activity related to the 3CX compromise across its customer base, as well as respond autonomously to contain it. Darktrace was able to amplify security integrations with CrowdStrike and SentinelOne, and via anomaly-based model breaches, contribute unique insights by highlighting activity in varied parts of the 3CX supply chain compromise kill chain. The “SmoothOperator” supply chain attack proves that the Darktrace suite of products, including DETECT and RESPOND, can not only act autonomously to identify and respond to novel threats, but also work with security integrations to further amplify intervention and prevent cyber disruption on customer networks. 

Credit to Nahisha Nobregas, SOC Analyst and Trent Kessler, SOC Analyst.

Appendices

MITRE ATT&CK Framework

Resource Development

  • T1588 Obtain Capabilities  
  • T1588.004 Digital Certificates
  • T1608 Stage Capabilities  
  • T1608.003 Install Digital Certificate

Initial Access

  • T1190 Exploit Public-Facing Application
  • T1195 Supply Chain Compromise  
  • T1195.002 Compromise Software Supply Chain

Persistence

  • T1574 Hijack Execution Flow
  • T1574.002 DLL Side-Loading

Privilege Escalation

  • T1055 Process Injection
  • T1574 Hijack Execution Flow  
  • T1574.002 DLL Side-Loading

Command and Control

  • T1071 Application Layer Protocol
  • T1071.001 Web Protocols
  • T1071.004 DNS  
  • T1105 Ingress Tool Transfer
  • T1573 Encrypted Channel

List of IOCs

C2 Hostnames

  • journalide[.]org
  • pbxphonenetwork[.]com

Likely C2 IP address

  • 89.45.67[.]160

References

  1. https://www.crowdstrike.com/blog/crowdstrike-detects-and-prevents-active-intrusion-campaign-targeting-3cxdesktopapp-customers/
  2. https://www.bleepingcomputer.com/news/security/3cx-confirms-north-korean-hackers-behind-supply-chain-attack/
  3. https://www.mandiant.com/resources/blog/3cx-software-supply-chain-compromise
  4. https://www.securityweek.com/cascading-supply-chain-attack-3cx-hacked-after-employee-downloaded-trojanized-app/
  5. https://securelist.com/gopuram-backdoor-deployed-through-3cx-supply-chain-attack/109344/
  6. https://www.bleepingcomputer.com/news/security/3cx-hack-caused-by-trading-software-supply-chain-attack/
  7. https://www.sentinelone.com/blog/smoothoperator-ongoing-campaign-trojanizes-3cx-software-in-software-supply-chain-attack/
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Nahisha Nobregas
SOC Analyst

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February 6, 2026

AppleScript Abuse: Unpacking a macOS Phishing Campaign

AppleScript Abuse: Unpacking a macOS Phishing CampaignDefault blog imageDefault blog image

Introduction

Darktrace security researchers have identified a campaign targeting macOS users through a multistage malware campaign that leverages social engineering and attempted abuse of the macOS Transparency, Consent and Control (TCC) privacy feature.

The malware establishes persistence via LaunchAgents and deploys a modular Node.js loader capable of executing binaries delivered from a remote command-and-control (C2) server.

Due to increased built-in security mechanisms in macOS such as System Integrity Protection (SIP) and Gatekeeper, threat actors increasingly rely on alternative techniques, including fake software and ClickFix attacks [1] [2]. As a result, macOS threats r[NJ1] ely more heavily on social engineering instead of vulnerability exploitation to deliver payloads, a trend Darktrace has observed across the threat landscape [3].

Technical analysis

The infection chain starts with a phishing email that prompts the user to download an AppleScript file named “Confirmation_Token_Vesting.docx.scpt”, which attemps to masquerade as a legitimate Microsoft document.

The AppleScript header prompting execution of the script.
Figure 1: The AppleScript header prompting execution of the script.

Once the user opens the AppleScript file, they are presented with a prompt instructing them to run the script, supposedly due to “compatibility issues”. This prompt is necessary as AppleScript requires user interaction to execute the script, preventing it from running automatically. To further conceal its intent, the malicious part of the script is buried below many empty lines, assuming a user likely will not to the end of the file where the malicious code is placed.

Curl request to receive the next stage.
Figure 2: Curl request to receive the next stage.

This part of the script builds a silent curl request to “sevrrhst[.]com”, sending the user’s macOS operating system, CPU type and language. This request retrieves another script, which is saved as a hidden file at in ~/.ex.scpt, executed, and then deleted.

The retrieved payload is another AppleScript designed to steal credentials and retrieve additional payloads. It begins by loading the AppKit framework, which enables the script to create a fake dialog box prompting the user to enter their system username and password [4].

 Fake dialog prompt for system password.
Figure 3: Fake dialog prompt for system password.

The script then validates the username and password using the command "dscl /Search -authonly <username> <password>", all while displaying a fake progress bar to the user. If validation fails, the dialog window shakes suggesting an incorrect password and prompting the user to try again. The username and password are then encoded in Base64 and sent to: https://sevrrhst[.]com/css/controller.php?req=contact&ac=<user>&qd=<pass>.

Figure 4: Requirements gathered on trusted binary.

Within the getCSReq() function, the script chooses from trusted Mac applications: Finder, Terminal, Script Editor, osascript, and bash. Using the codesign command codesign -d --requirements, it extracts the designated code-signing requirement from the target application. If a valid requirement cannot be retrieved, that binary is skipped. Once a designated requirement is gathered, it is then compiled into a binary trust object using the Code Signing Requirement command (csreq). This trust object is then converted into hex so it can later be injected into the TCC SQLite database.[NB2]

To bypass integrity checks, the TCC directory is renamed to com.appled.tcc using Finder. TCC is a macOS privacy framework designed to restrict application access to sensitive data, requiring users to explicitly grant permissions before apps can access items such as files, contacts, and system resources [1].

Example of how users interact with TCC.
Figure 5: TCC directory renamed to com.appled.TCC.
Figure 6: Example of how users interact with TCC.

After the database directory rename is attempted, the killall command is used on the tccd daemon to force macOS to release the lock on the database. The database is then injected with the forged access records, including the service, trusted binary path, auth_value, and the forged csreq binary. The directory is renamed back to com.apple.TCC, allowing the injected entries to be read and the permissions to be accepted. This enables persistence authorization for:

  • Full disk access
  • Screen recording
  • Accessibility
  • Camera
  • Apple Events 
  • Input monitoring

The malware does not grant permissions to itself; instead, it forges TCC authorizations for trusted Apple-signed binaries (Terminal, osascript, Script Editor, and bash) and then executes malicious actions through these binaries to inherit their permissions.

Although the malware is attempting to manipulate TCC state via Finder, a trusted system component, Apple has introduced updates in recent macOS versions that move much of the authorization enforcement into the tccd daemon. These updates prevent unauthorized permission modifications through directory or database manipulation. As a result, the script may still succeed on some older operating systems, but it is likely to fail on newer installations, as tcc.db reloads now have more integrity checks and will fail on Mobile Device Management (MDM) [NB5] systems as their profiles override TCC.

 Snippet of decoded Base64 response.
Figure 7: Snippet of decoded Base64 response.

A request is made to the C2, which retrieves and executes a Base64-encoded script. This script retrieves additional payloads based on the system architecture and stores them inside a directory it creates named ~/.nodes. A series of requests are then made to sevrrhst[.]com for:

/controller.php?req=instd

/controller.php?req=tell

/controller.php?req=skip

These return a node archive, bundled Node.js binary, and a JavaScript payload. The JavaScript file, index.js, is a loader that profiles the system and sends the data to the C2. The script identified the system platform, whether macOS, Linux or Windows, and then gathers OS version, CPU details, memory usage, disk layout, network interfaces, and running process. This is sent to https://sevrrhst[.]com/inc/register.php?req=init as a JSON object. The victim system is then registered with the C2 and will receive a Base64-encoded response.

LaunchAgent patterns to be replaced with victim information.
Figure 8: LaunchAgent patterns to be replaced with victim information.

The Base64-encoded response decodes to an additional Javacript that is used to set up persistence. The script creates a folder named com.apple.commonjs in ~/Library and copies the Node dependencies into this directory. From the C2, the files package.json and default.js are retrieved and placed into the com.apple.commonjs folder. A LaunchAgent .plist is also downloaded into the LaunchAgents directory to ensure the malware automatically starts. The .plist launches node and default.js on load, and uses output logging to log errors and outputs.

Default.js is Base64 encoded JavaScript that functions as a command loop, periodically sending logs to the C2, and checking for new payloads to execute. This gives threat actors ongoing and the ability to dynamically modify behavior without having to redeploy the malware. A further Base64-encoded JavaScript file is downloaded as addon.js.

Addon.js is used as the final payload loader, retrieving a Base64-encoded binary from https://sevrrhst[.]com/inc/register.php?req=next. The binary is decoded from Base64 and written to disk as “node_addon”, and executed silently in the background. At the time of analysis, the C2 did not return a binary, possibly because certain conditions were not met.  However, this mechanism enables the delivery and execution of payloads. If the initial TCC abuse were successful, this payload could access protected resources such as Screen Capture and Camera without triggering a consent prompt, due to the previously established trust.

Conclusion

This campaign shows how a malicious threat actor can use an AppleScript loader to exploit user trust and manipulate TCC authorization mechanisms, achieving persistent access to a target network without exploiting vulnerabilities.

Although recent macOS versions include safeguards against this type of TCC abuse, users should keep their systems fully updated to ensure the most up to date protections.  These findings also highlight the intentions of threat actors when developing malware, even when their implementation is imperfect.

Credit to Tara Gould (Malware Research Lead)
Edited by Ryan Traill (Analyst Content Lead)

Indicators of Compromise (IoCs)

88.119.171[.]59

sevrrhst[.]com

https://sevrrhst[.]com/inc/register.php?req=next

https://stomcs[.]com/inc/register.php?req=next
https://techcross-es[.]com

Confirmation_Token_Vesting.docx.scpt - d3539d71a12fe640f3af8d6fb4c680fd

EDD_Questionnaire_Individual_Blank_Form.docx.scpt - 94b7392133935d2034b8169b9ce50764

Investor Profile (Japan-based) - Shiro Arai.pdf.scpt - 319d905b83bf9856b84340493c828a0c

MITRE ATTACK

T1566 - Phishing

T1059.002 - Command and Scripting Interpreter: Applescript

T1059.004 – Command and Scripting Interpreter: Unix Shell

T1059.007 – Command and Scripting Interpreter: JavaScript

T1222.002 – File and Directory Permissions Modification

T1036.005 – Masquerading: Match Legitimate Name or Location

T1140 – Deobfuscate/Decode Files or Information

T1547.001 – Boot or Logon Autostart Execution: Launch Agent

T1553.006 – Subvert Trust Controls: Code Signing Policy Modification

T1082 – System Information Discovery

T1057 – Process Discovery

T1105 – Ingress Tool Transfer

References

[1] https://www.darktrace.com/blog/from-the-depths-analyzing-the-cthulhu-stealer-malware-for-macos

[2] https://www.darktrace.com/blog/unpacking-clickfix-darktraces-detection-of-a-prolific-social-engineering-tactic

[3] https://www.darktrace.com/blog/crypto-wallets-continue-to-be-drained-in-elaborate-social-media-scam

[4] https://developer.apple.com/documentation/appkit

[5] https://www.huntress.com/blog/full-transparency-controlling-apples-tcc

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Tara Gould
Malware Research Lead

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February 5, 2026

Darktrace Malware Analysis: Unpacking SnappyBee

darktace malware analysis snappybeeDefault blog imageDefault blog image

Introduction

The aim of this blog is to be an educational resource, documenting how an analyst can perform malware analysis techniques such as unpacking. This blog will demonstrate the malware analysis process against well-known malware, in this case SnappyBee.

SnappyBee (also known as Deed RAT) is a modular backdoor that has been previously attributed to China-linked cyber espionage group Salt Typhoon, also known as Earth Estries [1] [2]. The malware was first publicly documented by TrendMicro in November 2024 as part of their investigation into long running campaigns targeting various industries and governments by China-linked threat groups.

In these campaigns, SnappyBee is deployed post-compromise, after the attacker has already obtained access to a customer's system, and is used to establish long-term persistence as well as deploying further malware such as Cobalt Strike and the Demodex rootkit.

To decrease the chance of detection, SnappyBee uses a custom packing routine. Packing is a common technique used by malware to obscure its true payload by hiding it and then stealthily loading and executing it at runtime. This hinders analysis and helps the malware evade detection, especially during static analysis by both human analysts and anti-malware services.

This blog is a practical guide on how an analyst can unpack and analyze SnappyBee, while also learning the necessary skills to triage other malware samples from advanced threat groups.

First principles

Packing is not a new technique, and threat actors have generally converged on a standard approach. Packed binaries typically feature two main components: the packed data and an unpacking stub, also called a loader, to unpack and run the data.

Typically, malware developers insert a large blob of unreadable data inside an executable, such as in the .rodata section. This data blob is the true payload of the malware, but it has been put through a process such as encryption, compression, or another form of manipulation to render it unreadable. Sometimes, this data blob is instead shipped in a different file, such as a .dat file, or a fake image. When this happens, the main loader has to read this using a syscall, which can be useful for analysis as syscalls can be easily identified, even in heavily obfuscated binaries.

In the main executable, malware developers will typically include an unpacking stub that takes the data blob, performs one or more operations on it, and then triggers its execution. In most samples, the decoded payload data is loaded into a newly allocated memory region, which will then be marked as executable and executed. In other cases, the decoded data is instead dropped into a new executable on disk and run, but this is less common as it increases the likelihood of detection.

Finding the unpacking routine

The first stage of analysis is uncovering the unpacking routine so it can be reverse engineered. There are several ways to approach this, but it is traditionally first triaged via static analysis on the initial stages available to the analyst.

SnappyBee consists of two components that can be analyzed:

  • A Dynamic-link Library (DLL) that acts as a loader, responsible for unpacking the malicious code
  • A data file shipped alongside the DLL, which contains the encrypted malicious code

Additionally, SnappyBee includes a legitimate signed executable that is vulnerable to DLL side-loading. This means that when the executable is run, it will inadvertently load SnappyBee’s DLL instead of the legitimate one it expects. This allows SnappyBee to appear more legitimate to antivirus solutions.

The first stage of analysis is performing static analysis of the DLL. This can be done by opening the DLL within a disassembler such as IDA Pro. Upon opening the DLL, IDA will display the DllMain function, which is the malware’s initial entry point and the first code executed when the DLL is loaded.

The DllMain function
Figure 1: The DllMain function

First, the function checks if the variable fdwReason is set to 1, and exits if it is not. This variable is set by Windows to indicate why the DLL was loaded. According to Microsoft Developer Network (MSDN), a value of 1 corresponds to DLL_PROCESS_ATTACH, meaning “The DLL is being loaded into the virtual address space of the current process as a result of the process starting up or as a result of a call to LoadLibrary” [3]. Since SnappyBee is known to use DLL sideloading for execution, DLL_PROCESS_ATTACH is the expected value when the legitimate executable loads the malicious DLL.

SnappyBee then uses the GetModule and GetProcAddress to dynamically resolve the address of the VirtualProtect in kernel32 and StartServiceCtrlDispatcherW in advapi32. Resolving these dynamically at runtime prevents them from showing up as a static import for the module, which can help evade detection by anti-malware solutions. Different regions of memory have different permissions to control what they can be used for, with the main ones being read, write, and execute. VirtualProtect is a function that changes the permissions of a given memory region.

SnappyBee then uses VirtualProtect to set the memory region containing the code for the StartServiceCtrlDispatcherW function as writable. It then inserts a jump instruction at the start of this function, redirecting the control flow to one of the SnappyBee DLL’s other functions, and then restores the old permissions.

In practice, this means when the legitimate executable calls StartServiceCtrlDispatcherW, it will immediately hand execution back to SnappyBee. Meanwhile, the call stack now appears more legitimate to outside observers such as antimalware solutions.

The hooked-in function then reads the data file that is shipped with SnappyBee and loads it into a new memory allocation. This pattern of loading the file into memory likely means it is responsible for unpacking the next stage.

The start of the unpacking routine that reads in dbindex.dat.
Figure 2: The start of the unpacking routine that reads in dbindex.dat.

SnappyBee then proceeds to decrypt the memory allocation and execute the code.

The memory decryption routine.
Figure 3: The memory decryption routine.

This section may look complex, however it is fairly straight forward. Firstly, it uses memset to zero out a stack variable, which will be used to store the decryption key. It then uses the first 16 bytes of the data file as a decryption key to initialize the context from.

SnappyBee then calls the mbed_tls_arc4_crypt function, which is a function from the mbedtls library. Documentation for this function can be found online and can be referenced to better understand what each of the arguments mean [4].

The documentation for mbedtls_arc4_crypt.
Figure 4: The documentation for mbedtls_arc4_ crypt.

Comparing the decompilation with the documentation, the arguments SnappyBee passes to the function can be decoded as:

  • The context derived from 16-byte key at the start of the data is passed in as the context in the first parameter
  • The file size minus 16 bytes (to account for the key at the start of the file) is the length of the data to be decrypted
  • A pointer to the file contents in memory, plus 16 bytes to skip the key, is used as the input
  • A pointer to a new memory allocation obtained from VirtualAlloc is used as the output

So, putting it all together, it can be concluded that SnappyBee uses the first 16 bytes as the key to decrypt the data that follows , writing the output into the allocated memory region.

SnappyBee then calls VirtualProtect to set the decrypted memory region as Read + Execute, and subsequently executes the code at the memory pointer. This is clearly where the unpacked code containing the next stage will be placed.

Unpacking the malware

Understanding how the unpacking routine works is the first step. The next step is obtaining the actual code, which cannot be achieved through static analysis alone.

There are two viable methods to retrieve the next stage. The first method is implementing the unpacking routine from scratch in a language like Python and running it against the data file.

This is straightforward in this case, as the unpacking routine in relatively simple and would not require much effort to re-implement. However, many unpacking routines are far more complex, which leads to the second method: allowing the malware to unpack itself by debugging it and then capturing the result. This is the approach many analysts take to unpacking, and the following will document this method to unpack SnappyBee.

As SnappyBee is 32-bit Windows malware, debugging can be performed using x86dbg in a Windows sandbox environment to debug SnappyBee. It is essential this sandbox is configured correctly, because any mistake during debugging could result in executing malicious code, which could have serious consequences.

Before debugging, it is necessary to disable the DYNAMIC_BASE flag on the DLL using a tool such as setdllcharacteristics. This will stop ASLR from randomizing the memory addresses each time the malware runs and ensures that it matches the addresses observed during static analysis.

The first place to set a breakpoint is DllMain, as this is the start of the malicious code and the logical place to pause before proceeding. Using IDA, the functions address can be determined; in this case, it is at offset 10002DB0. This can be used in the Goto (CTRL+G) dialog to jump to the offset and place a breakpoint. Note that the “Run to user code” button may need to be pressed if the DLL has not yet been loaded by x32dbg, as it spawns a small process to load the DLL as DLLs cannot be executed directly.

The program can then run until the breakpoint, at which point the program will pause and code recognizable from static analysis can be observed.

Figure 5: The x32dbg dissassembly listing forDllMain.

In the previous section, this function was noted as responsible for setting up a hook, and in the disassembly listing the hook address can be seen being loaded at offset 10002E1C. It is not necessary to go through the whole hooking process, because only the function that gets hooked in needs to be run. This function will not be naturally invoked as the DLL is being loaded directly rather than via sideloading as it expects. To work around this, the Extended Instruction Pointer (EIP) register can be manipulated to point to the start of the hook function instead, which will cause it to run instead of the DllMain function.

To update EIP, the CRTL+G dialog can again be used to jump to the hook function address (10002B50), and then the EIP register can be set to this address by right clicking the first instruction and selecting “Set EIP here”. This will make the hook function code run next.

Figure 6: The start of the hookedin-in function

Once in this function, there are a few addresses where breakpoints should be set in order to inspect the state of the program at critical points in the unpacking process. These are:

-              10002C93, which allocates the memory for the data file and final code

-              10002D2D, which decrypts the memory

-              10002D81, which runs the unpacked code

Setting these can be done by pressing the dot next to the instruction listing, or via the CTRL+G Goto menu.

At the first breakpoint, the call to VirtualAlloc will be executed. The function returns the memory address of the created memory region, which is stored in the EAX register. In this case, the region was allocated at address 00700000.

Figure 7: The result of the VirtualAlloc call.

It is possible to right click the address and press “Follow in dump” to pin the contents of the memory to the lower pane, which makes it easy to monitor the region as the unpacking process continues.

Figure 8: The allocated memory region shown in x32dbg’s dump.

Single-stepping through the application from this point eventually reaches the call to ReadFile, which loads the file into the memory region.

Figure 9: The allocated memory region after the file is read into it, showing high entropy data.

The program can then be allowed to run until the next breakpoint, which after single-stepping will execute the call to mbedtls_arc4_crypt to decrypt the memory. At this point, the data in the dump will have changed.

Figure 10: The same memory region after the decryption is run, showing lower entropy data.

Right-clicking in the dump and selecting "Disassembly” will disassemble the data. This yields valid shell code, indicating that the unpacking succeeded, whereas corrupt or random data would be expected if the unpacking had failed.

Figure 11: The disassembly view of the allocated memory.

Right-clicking and selecting “Follow in memory map” will show the memory allocation under the memory map view. Right-clicking this then provides an option to dump the entire memory block to file.

Figure 12: Saving the allocated memory region.

This dump can then be opened in IDA, enabling further static analysis of the shellcode. Reviewing the shellcode, it becomes clear that it performs another layer of unpacking.

As the debugger is already running, the sample can be allowed to execute up to the final breakpoint that was set on the call to the unpacked shellcode. Stepping into this call will then allow debugging of the new shellcode.

The simplest way to proceed is to single-step through the code, pausing on each call instruction to consider its purpose. Eventually, a call instruction that points to one of the memory regions that were assigned will be reached, which will contain the next layer of unpacked code. Using the same disassembly technique as before, it can be confirmed that this is more unpacked shellcode.

Figure 13: The unpacked shellcode’s call to RDI, which points to more unpacked shellcode. Note this screenshot depicts the 64-bit variant of SnappyBee instead of 32-bit, however the theory is the same.

Once again, this can be dumped out and analyzed further in IDA. In this case, it is the final payload used by the SnappyBee malware.

Conclusion

Unpacking remains one of the most common anti-analysis techniques and is a feature of most sophisticated malware from threat groups. This technique of in-memory decryption reduces the forensic “surface area” of the malware, helping it to evade detection from anti-malware solutions. This blog walks through one such example and provides practical knowledge on how to unpack malware for deeper analysis.

In addition, this blog has detailed several other techniques used by threat actors to evade analysis, such as DLL sideloading to execute code without arising suspicion, dynamic API resolving to bypass static heuristics, and multiple nested stages to make analysis challenging.

Malware such as SnappyBee demonstrates a continued shift towards highly modular and low-friction malware toolkits that can be reused across many intrusions and campaigns. It remains vital for security teams  to maintain the ability to combat the techniques seen in these toolkits when responding to infections.

While the technical details of these techniques are primarily important to analysts, the outcomes of this work directly affect how a Security Operations Centre (SOC) operates at scale. Without the technical capability to reliably unpack and observe these samples, organizations are forced to respond without the full picture.

The techniques demonstrated here help close that gap. This enables security teams to reduce dwell time by understanding the exact mechanisms of a sample earlier, improve detection quality with behavior-based indicators rather than relying on hash-based detections, and increase confidence in response decisions when determining impact.

Credit to Nathaniel Bill (Malware Research Engineer)
Edited by Ryan Traill (Analyst Content Lead)

Indicators of Compromise (IoCs)

SnappyBee Loader 1 - 25b9fdef3061c7dfea744830774ca0e289dba7c14be85f0d4695d382763b409b

SnappyBee Loader 2 - b2b617e62353a672626c13cc7ad81b27f23f91282aad7a3a0db471d84852a9ac          

SnappyBee Payload - 1a38303fb392ccc5a88d236b4f97ed404a89c1617f34b96ed826e7bb7257e296

References

[1] https://www.trendmicro.com/en_gb/research/24/k/earth-estries.html

[2] https://www.darktrace.com/blog/salty-much-darktraces-view-on-a-recent-salt-typhoon-intrusion

[3] https://learn.microsoft.com/en-us/windows/win32/dlls/dllmain#parameters

[4] https://mbed-tls.readthedocs.io/projects/api/en/v2.28.4/api/file/arc4_8h/#_CPPv418mbedtls_arc4_cryptP20mbedtls_arc4_context6size_tPKhPh

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About the author
Nathaniel Bill
Malware Research Engineer
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