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December 6, 2023

How Darktrace Triumphed Over MyKings Botnet

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06
Dec 2023
Darktrace has provided full visibility over the MyKings botnet kill chain from the beginning of its infections to the eventual cryptocurrency mining activity.

Botnets: A persistent cyber threat

Since their appearance in the wild over three decades ago, botnets have consistently been the attack vector of choice for many threat actors. The most prevalent of these attack vectors are distributed denial of service (DDoS) and phishing campaigns. Their persistent nature means that even if a compromised device in identified, attackers can continue to operate by using the additional compromised devices they will likely have on the target network. Similarly, command and control (C2) infrastructure can easily be restructured between infected systems, making it increasingly difficult to remove the infection.  

MyKings Botnet

One of the most prevalent and sophisticated examples in recent years is the MyKings botnet, also known as Smominru or DarkCloud. Darktrace has observed numerous cases of MyKings botnet compromises across multiple customer environments in several different industries as far back as August 2022. The diverse tactics, techniques, and procedures (TTPs) and sophisticated kill chains employed by MyKings botnet may prove a challenge to traditional rule and signature-based detections.

However, Darktrace’s anomaly-centric approach enabled it to successfully detect a wide-range of indicators of compromise (IoCs) related to the MyKings botnet and bring immediate awareness to customer security teams, as it demonstrated on the network of multiple customers between March and August 2023.

Background on MyKings Botnet

MyKings has been active and spreading steadily since 2016 resulting in over 520,000 infections worldwide.[1] Although verified attribution of the botnet remains elusive, the variety of targets and prevalence of crypto-mining software on affected devices suggests the threat group behind the malware is financially motivated. The operators behind MyKings appear to be highly opportunistic, with attacks lacking an obvious specific target industry. Across Darktrace’s customer base, the organizations affected were representative of multiple industries such as entertainment, mining, education, information technology, health, and transportation.

Given its longevity, the MyKings botnet has unsurprisingly evolved since its first appearance years ago. Initial analyses of the botnet showed that the primary crypto-related activity on infected devices was the installation of Monero-mining software. However, in 2019 researchers discovered a new module within the MyKings malware that enabled clipboard-jacking, whereby the malware replaces a user's copied cryptowallet address with the operator's own wallet address in order to siphon funds.[2]

Similar to other botnets such as the Outlaw crypto-miner, the MyKings botnet can also kill running processes of unrelated malware on the compromised hosts that may have resulted from prior infection.[3] MyKings has also developed a comprehensive set of persistence techniques, including: the deployment of bootkits, initiating the botnet immediately after a system reboot, configuring Registry run keys, and generating multiple Scheduled Tasks and WMI listeners.[4] MyKings have also been observed rotating tools and payloads over time to propagate the botnet. For example, some operators have been observed utilizing PCShare, an open-source remote access trojan (RAT) customized to conduct C2 services, execute commands, and download mining software[5].

Darktrace Coverage

Across observed customer networks between March and August 2023, Darktrace identified the MyKings botnet primarily targeting Windows-based servers that supports services like MySQL, MS-SQL, Telnet, SSH, IPC, WMI, and Remote Desktop (RDP).  In the initial phase of the attack, the botnet would initiate a variety of attacks against a target including brute-forcing and exploitation of unpatched vulnerabilities on exposed servers. The botnet delivers a variety of payloads to the compromised systems including worm downloaders, trojans, executable files and scripts.

This pattern of activity was detected across the network of one particular Darktrace customer in the education sector in early March 2023. Unfortunately, this customer did not have Darktrace RESPOND™ deployed on their network at the time of the attack, meaning the MyKings botnet was able to move through the cyber kill chain ultimately achieving its goal, which in this case was mining cryptocurrency.

Initial Access

On March 6, Darktrace observed an internet-facing SQL server receiving an unusually large number of incoming MySQL connections from the rare external endpoint 171.91.76[.]31 via port 1433. While it is not possible to confirm whether these suspicious connections represented the exact starting point of the infection, such a sudden influx of SQL connection from a rare external endpoint could be indicative of a malicious attempt to exploit vulnerabilities in the server's SQL database or perform password brute-forcing to gain unauthorized access. Given that MyKings typically spreads primarily through such targeting of internet-exposed devices, the pattern of activity is consistent with potential initial access by MyKings.[6]

Initial Command and Control

The device then proceeded to initiate a series of repeated HTTP connections between March 6 and March 10, to the domain www[.]back0314[.]ru (107.148.239[.]111). These connections included HTTP GET requests featuring URIs such as ‘/back.txt',  suggesting potential beaconing and C2 communication. The device continued this connectivity to the external host over the course of four days, primarily utilizing destination ports 80, and 6666. While port 80 is commonly utilized for HTTP connections, port 6666 is a non-standard port for the protocol. Such connectivity over non-standard ports can indicate potential detection evasion and obfuscation tactics by the threat actors.  During this time, the device also initiated repeated connections to additional malicious external endpoints with seemingly algorithmically generated hostnames such as pc.pc0416[.]xyz.

Darktrace UI image
Figure 1: Model breach showing details of the malicious domain generation algorithm (DGA) connections.

Tool Transfer

While this beaconing activity was taking place, the affected device also began to receive potential payloads from unusual external endpoints. On April 29, the device made an HTTP GET request for “/power.txt” to the endpoint 192.236.160[.]237, which was later discovered to have multiple open-source intelligence (OSINT) links to malware. Power.txt is a shellcode written in PowerShell which is downloaded and executed with the purpose of disabling Windows Defenders related functions.[7] After the initial script was downloaded (and likely executed), Darktrace went on to detect the device making a series of additional GET requests for several varying compressed and executable files. For example, the device made HTTP requests for '/pld/cmd.txt' to the external endpoint 104.233.224[.]173. In response the external server provided numerous files, including ‘u.exe’, and ‘upsup4.exe’ for download, both of which share file names with previously identified MyKings payloads.

MyKings deploys a diverse array of payloads to expand the botnet and secure a firm position within a compromised system. This multi-faceted approach may render conventional security measures less effective due to the intricacies of and variety of payloads involved in compromises. Darktrace, however, does not rely on static or outdated lists of IoCs in order to detect malicious activity. Instead, DETECT’s Self-Learning AI allows it to identify emerging compromise activity by recognizing the subtle deviations in an affected device’s behavior that could indicate it has fallen into the hands of malicious actors.

Figure 2: External site summary of the endpoint 103.145.106[.]242 showing the rarity of connectivity to the external host.

Achieving Objectives – Crypto-Mining

Several weeks after the initial payloads were delivered and beaconing commenced, Darktrace finally detected the initiation of crypto-mining operations. On May 27, the originally compromised server connected to the rare domain other.xmrpool[.]ru over port 1081. As seen in the domain name, this endpoint appears to be affiliated with pool mining activity and the domain has various OSINT affiliations with the cryptocurrency Monero coin. During this connection, the host was observed passing Monero credentials, activity which parallels similar mining operations observed on other customer networks that had been compromised by the MyKings botnet.

Although mining activity may not pose an immediate or urgent concern for security unauthorized cryptomining on devices can result in detrimental consequences, such as compromised hardware integrity, elevated energy costs, and reduced productivity, and even potential involvement in money laundering.

Figure 3: Event breach log showing details of the connection to the other.xmrpool[.]ru endpoint associated with cryptocurrency mining activity.

Conclusion

Detecting future iterations of the MyKings botnet will likely demand a shift away from an overreliance on traditional rules and signatures and lists of “known bads”, instead requiring organizations to employ AI-driven technology that can identify suspicious activity that represents a deviation from previously established patterns of life.

Despite the diverse range of payloads, malicious endpoints, and intricate activities that constitute a typical MyKing botnet compromise, Darktrace was able successfully detect multiple critical phases within the MyKings kill chain. Given the evolving nature of the MyKings botnet, it is highly probable the botnet will continue to expand and adapt, leveraging new tactics and technologies. By adopting Darktrace’s product of suites, including Darktrace DETECT, organizations are well-positioned to identify these evolving threats as soon as they emerge and, when coupled with the autonomous response technology of Darktrace RESPOND, threats like the MyKings botnet can be stopped in their tracks before they can achieve their ultimate goals.

Credit to: Oluwatosin Aturaka, Analyst Team Lead, Cambridge, Adam Potter, Cyber Analyst

Appendix

IoC Table

IoC - Type - Description + Confidence

162.216.150[.]108- IP - C2 Infrastructure

103.145.106[.]242 - IP - C2 Infrastructure

137.175.56[.]104 - IP - C2 Infrastructure

138.197.152[.]201 - IP - C2 Infrastructure

139.59.74[.]135 - IP - C2 Infrastructure

pc.pc0416[.]xyz - Domain - C2 Infrastructure (DGA)

other.xmrpool[.]ru - Domain - Cryptomining Endpoint

xmrpool[.]ru - Domain - Cryptomining Endpoint

103.145.106[.]55 - IP - Cryptomining Endpoint

ntuser[.]rar - Zipped File - Payload

/xmr1025[.]rar - Zipped File - Payload

/20201117[.]rar - Zipped File - Payload

wmi[.]txt - File - Payload

u[.]exe - Executable File - Payload

back[.]txt - File - Payload

upsupx2[.]exe - Executable File - Payload

cmd[.]txt - File - Payload

power[.]txt - File - Payload

ups[.]html - File - Payload

xmr1025.rar - Zipped File - Payload

171.91.76[.]31- IP - Possible Initial Compromise Endpoint

www[.]back0314[.]ru - Domain - Probable C2 Infrastructure

107.148.239[.]111 - IP - Probable C2 Infrastructure

194.67.71[.]99 - IP- Probable C2 Infrastructure

Darktrace DETECT Model Breaches

  • Device / Initial Breach Chain Compromise
  • Anomalous File / Masqueraded File Transfer (x37)
  • Compromise / Large DNS Volume for Suspicious Domain
  • Compromise / Fast Beaconing to DGA
  • Device / Large Number of Model Breaches
  • Anomalous File / Multiple EXE from Rare External Locations (x30)
  • Compromise / Beacon for 4 Days (x2)
  • Anomalous Server Activity / New User Agent from Internet Facing System
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous Server Activity / New Internet Facing System
  • Anomalous File / EXE from Rare External Location (x37)
  • Device / Large Number of Connections to New Endpoints
  • Anomalous Server Activity / Server Activity on New Non-Standard Port (x3)
  • Device / Threat Indicator (x3)
  • Unusual Activity / Unusual External Activity
  • Compromise / Crypto Currency Mining Activity (x37)
  • Compliance / Internet Facing SQL Server
  • Device / Anomalous Scripts Download Followed By Additional Packages
  • Device / New User Agent

MITRE ATT&CK Mapping

ATT&CK Technique - Technique ID

Reconnaissance – T1595.002 Vulnerability Scanning

Resource Development – T1608 Stage Capabilities

Resource Development – T1588.001 Malware

Initial Access – T1190 Exploit Public-Facing Application

Command and Control – T15568.002 Domain Generated Algorithms

Command and Control – T1571 Non-Standard Port

Execution – T1047 Windows Management Instrumentation

Execution – T1059.001 Command and Scripting Interpreter

Persistence – T1542.003 Pre-OS Boot

Impact – T1496 Resource Hijacking

References

[1] https://www.binarydefense.com/resources/threat-watch/mykings-botnet-is-growing-and-remains-under-the-radar/

[2] https://therecord.media/a-malware-botnet-has-made-more-than-24-7-million-since-2019

[3] https://www.darktrace.com/blog/outlaw-returns-uncovering-returning-features-and-new-tactics

[4] https://www.sophos.com/en-us/medialibrary/pdfs/technical-papers/sophoslabs-uncut-mykings-report.pdf

[5] https://www.antiy.com/response/20190822.html

[6] https://ethicaldebuggers.com/mykings-botnet/

[7] https://ethicaldebuggers.com/mykings-botnet/

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.
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Oluwatosin Aturaka
Analyst Team Lead, Cambridge
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March 18, 2025

Survey findings: How is AI Impacting the SOC?

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There’s no question that AI is already impacting the SOC – augmenting, assisting, and filling the gaps left by staff and skills shortages. We surveyed over 1,500 cybersecurity professionals from around the world to uncover their attitudes to AI cybersecurity in 2025. Our findings revealed striking trends in how AI is changing the way security leaders think about hiring and SOC transformation. Download the full report for the big picture, available now.

Download the full report to explore these findings in depth

The AI-human conundrum

Let’s start with some context. As the cybersecurity sector has rapidly evolved to integrate AI into all elements of cyber defense, the pace of technological advancement is outstripping the development of necessary skills. Given the ongoing challenges in security operations, such as employee burnout, high turnover rates, and talent shortages, recruiting personnel to bridge these skills gaps remains an immense challenge in today’s landscape.

But here, our main findings on this topic seem to contradict each other.

There’s no question over the impact of AI-powered threats – nearly three-quarters (74%) agree that AI-powered threats now pose a significant challenge for their organization.  

When we look at how security leaders are defending against AI-powered threats, over 3 out of 5 (62%) see insufficient personnel to manage tools and alerts as the biggest barrier.  

Yet at the same time, increasing cyber security staff is at the bottom of the priority list for survey participants, with only 11% planning to increase cybersecurity staff in 2025 – less than in 2024. What 64% of stakeholders are committed to, however, is adding new AI-powered tools onto their existing security stacks.

The conclusion? Due to pressures around hiring, defensive AI is becoming integral to the SOC as a means of augmenting understaffed teams.

How is AI plugging skills shortages in the SOC?

As explored in our recent white paper, the CISO’s Guide to Navigating the Cybersecurity Skills Shortage, 71% of organizations report unfilled cybersecurity positions, leading to the estimation that less than 10% of alerts are thoroughly vetted. In this scenario, AI has become an essential multiplier to relieve the burden on security teams.

95% of respondents agree that AI-powered solutions can significantly improve the speed and efficiency of their defenses. But how?

The area security leaders expect defensive AI to have the biggest impact is on improving threat detection, followed by autonomous response to threats and identifying exploitable vulnerabilities.

Interestingly, the areas that participants ranked less highly (reducing alert fatigue and running phishing simulation), are the tasks that AI already does well and can therefore be used already to relieve the burden of manual, repetitive work on the SOC.

Different perspectives from different sides of the SOC

CISOs and SecOps teams aren’t necessarily aligned on the AI defense question – while CISOs tend to see it as a strategic game-changer, SecOps teams on the front lines may be more sceptical, wary of its real-world reliability and integration into workflows.  

From the data, we see that while less than a quarter of execs doubt that AI-powered solutions will block and automatically respond to AI threats, about half of SecOps aren’t convinced. And only 17% of CISOs lack confidence in the ability of their teams to implement and use AI-powered solutions, whereas over 40% those in the team doubt their own ability to do so.

This gap feeds into the enthusiasm that executives share about adding AI-driven tools into the stack, while day-to-day users of the tools are more interested in improving security awareness training and improving cybersecurity tool integration.

Levels of AI understanding in the SOC

AI is only as powerful as the people who use it, and levels of AI expertise in the SOC can make or break its real-world impact. If security leaders want to unlock AI’s full potential, they must bridge the knowledge gap—ensuring teams understand not just the different types of AI, but where it can be applied for maximum value.

Only 42% of security professionals are confident that they fully understand all the types of AI in their organization’s security stack.

This data varies between job roles – executives report higher levels of understanding (60% say they know exactly which types of AI are being used) than participants in other roles. Despite having a working knowledge of using the tools day-to-day, SecOps practitioners were more likely to report having a “reasonable understanding” of the types of AI in use in their organization (42%).  

Whether this reflects a general confidence in executives rather than technical proficiency it’s hard to say, but it speaks to the importance of AI-human collaboration – introducing AI tools for cybersecurity to plug the gaps in human teams will only be effective if security professionals are supported with the correct education and training.  

Download the full report to explore these findings in depth

The full report for Darktrace’s State of AI Cybersecurity is out now. Download the paper to dig deeper into these trends, and see how results differ by industry, region, organization size, and job title.  

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March 18, 2025

Darktrace's Detection of State-Linked ShadowPad Malware

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An integral part of cybersecurity is anomaly detection, which involves identifying unusual patterns or behaviors in network traffic that could indicate malicious activity, such as a cyber-based intrusion. However, attribution remains one of the ever present challenges in cybersecurity. Attribution involves the process of accurately identifying and tracing the source to a specific threat actor(s).

Given the complexity of digital networks and the sophistication of attackers who often use proxies or other methods to disguise their origin, pinpointing the exact source of a cyberattack is an arduous task. Threat actors can use proxy servers, botnets, sophisticated techniques, false flags, etc. Darktrace’s strategy is rooted in the belief that identifying behavioral anomalies is crucial for identifying both known and novel threat actor campaigns.

The ShadowPad cluster

Between July 2024 and November 2024, Darktrace observed a cluster of activity threads sharing notable similarities. The threads began with a malicious actor using compromised user credentials to log in to the target organization's Check Point Remote Access virtual private network (VPN) from an attacker-controlled, remote device named 'DESKTOP-O82ILGG'.  In one case, the IP from which the initial login was carried out was observed to be the ExpressVPN IP address, 194.5.83[.]25. After logging in, the actor gained access to service account credentials, likely via exploitation of an information disclosure vulnerability affecting Check Point Security Gateway devices. Recent reporting suggests this could represent exploitation of CVE-2024-24919 [27,28]. The actor then used these compromised service account credentials to move laterally over RDP and SMB, with files related to the modular backdoor, ShadowPad, being delivered to the  ‘C:\PerfLogs\’ directory of targeted internal systems. ShadowPad was seen communicating with its command-and-control (C2) infrastructure, 158.247.199[.]185 (dscriy.chtq[.]net), via both HTTPS traffic and DNS tunneling, with subdomains of the domain ‘cybaq.chtq[.]net’ being used in the compromised devices’ TXT DNS queries.

Darktrace’s Advanced Search data showing the VPN-connected device initiating RDP connections to a domain controller (DC). The device subsequently distributes likely ShadowPad-related payloads and makes DRSGetNCChanges requests to a second DC.
Figure 1: Darktrace’s Advanced Search data showing the VPN-connected device initiating RDP connections to a domain controller (DC). The device subsequently distributes likely ShadowPad-related payloads and makes DRSGetNCChanges requests to a second DC.
Event Log data showing a DC making DNS queries for subdomains of ‘cbaq.chtq[.]net’ to 158.247.199[.]185 after receiving SMB and RDP connections from the VPN-connected device, DESKTOP-O82ILGG.
Figure 2: Event Log data showing a DC making DNS queries for subdomains of ‘cbaq.chtq[.]net’ to 158.247.199[.]185 after receiving SMB and RDP connections from the VPN-connected device, DESKTOP-O82ILGG.

Darktrace observed these ShadowPad activity threads within the networks of European-based customers in the manufacturing and financial sectors.  One of these intrusions was followed a few months later by likely state-sponsored espionage activity, as detailed in the investigation of the year in Darktrace’s Annual Threat Report 2024.

Related ShadowPad activity

Additional cases of ShadowPad were observed across Darktrace’s customer base in 2024. In some cases, common C2 infrastructure with the cluster discussed above was observed, with dscriy.chtq[.]net and cybaq.chtq[.]net both involved; however, no other common features were identified. These ShadowPad infections were observed between April and November 2024, with customers across multiple regions and sectors affected.  Darktrace’s observations align with multiple other public reports that fit the timeframe of this campaign.

Darktrace has also observed other cases of ShadowPad without common infrastructure since September 2024, suggesting the use of this tool by additional threat actors.

The data theft thread

One of the Darktrace customers impacted by the ShadowPad cluster highlighted above was a European manufacturer. A distinct thread of activity occurred within this organization’s network several months after the ShadowPad intrusion, in October 2024.

The thread involved the internal distribution of highly masqueraded executable files via Sever Message Block (SMB) and WMI (Windows Management Instrumentation), the targeted collection of sensitive information from an internal server, and the exfiltration of collected information to a web of likely compromised sites. This observed thread of activity, therefore, consisted of three phrases: lateral movement, collection, and exfiltration.

The lateral movement phase began when an internal user device used an administrative credential to distribute files named ‘ProgramData\Oracle\java.log’ and 'ProgramData\Oracle\duxwfnfo' to the c$ share on another internal system.  

Darktrace model alert highlighting an SMB write of a file named ‘ProgramData\Oracle\java.log’ to the c$ share on another device.
Figure 3: Darktrace model alert highlighting an SMB write of a file named ‘ProgramData\Oracle\java.log’ to the c$ share on another device.

Over the next few days, Darktrace detected several other internal systems using administrative credentials to upload files with the following names to the c$ share on internal systems:

ProgramData\Adobe\ARM\webservices.dll

ProgramData\Adobe\ARM\wksprt.exe

ProgramData\Oracle\Java\wksprt.exe

ProgramData\Oracle\Java\webservices.dll

ProgramData\Microsoft\DRM\wksprt.exe

ProgramData\Microsoft\DRM\webservices.dll

ProgramData\Abletech\Client\webservices.dll

ProgramData\Abletech\Client\client.exe

ProgramData\Adobe\ARM\rzrmxrwfvp

ProgramData\3Dconnexion\3DxWare\3DxWare.exe

ProgramData\3Dconnexion\3DxWare\webservices.dll

ProgramData\IDMComp\UltraCompare\updater.exe

ProgramData\IDMComp\UltraCompare\webservices.dll

ProgramData\IDMComp\UltraCompare\imtrqjsaqmm

Cyber AI Analyst highlighting an SMB write of a file named ‘ProgramData\Adobe\ARM\webservices.dll’ to the c$ share on an internal system.
Figure 4: Cyber AI Analyst highlighting an SMB write of a file named ‘ProgramData\Adobe\ARM\webservices.dll’ to the c$ share on an internal system.

The threat actor appears to have abused the Microsoft RPC (MS-RPC) service, WMI, to execute distributed payloads, as evidenced by the ExecMethod requests to the IWbemServices RPC interface which immediately followed devices’ SMB uploads.  

Cyber AI Analyst data highlighting a thread of activity starting with an SMB data upload followed by ExecMethod requests.
Figure 5: Cyber AI Analyst data highlighting a thread of activity starting with an SMB data upload followed by ExecMethod requests.

Several of the devices involved in these lateral movement activities, both on the source and destination side, were subsequently seen using administrative credentials to download tens of GBs of sensitive data over SMB from a specially selected server.  The data gathering stage of the threat sequence indicates that the threat actor had a comprehensive understanding of the organization’s system architecture and had precise objectives for the information they sought to extract.

Immediately after collecting data from the targeted server, devices went on to exfiltrate stolen data to multiple sites. Several other likely compromised sites appear to have been used as general C2 infrastructure for this intrusion activity. The sites used by the threat actor for C2 and data exfiltration purport to be sites for companies offering a variety of service, ranging from consultancy to web design.

Screenshot of one of the likely compromised sites used in the intrusion. 
Figure 6: Screenshot of one of the likely compromised sites used in the intrusion.

At least 16 sites were identified as being likely data exfiltration or C2 sites used by this threat actor in their operation against this organization. The fact that the actor had such a wide web of compromised sites at their disposal suggests that they were well-resourced and highly prepared.  

Darktrace model alert highlighting an internal device slowly exfiltrating data to the external endpoint, yasuconsulting[.]com.
Figure 7: Darktrace model alert highlighting an internal device slowly exfiltrating data to the external endpoint, yasuconsulting[.]com.
Darktrace model alert highlighting an internal device downloading nearly 1 GB of data from an internal system just before uploading a similar volume of data to another suspicious endpoint, www.tunemmuhendislik[.]com    
Figure 8: Darktrace model alert highlighting an internal device downloading nearly 1 GB of data from an internal system just before uploading a similar volume of data to another suspicious endpoint, www.tunemmuhendislik[.]com  

Cyber AI Analyst spotlight

Cyber AI Analyst identifying and piecing together the various steps of a ShadowPad intrusion.
Figure 9: Cyber AI Analyst identifying and piecing together the various steps of a ShadowPad intrusion.  
Cyber AI Analyst Incident identifying and piecing together the various steps of the data theft activity.
Figure 10: Cyber AI Analyst Incident identifying and piecing together the various steps of the data theft activity.

As shown in the above figures, Cyber AI Analyst’s ability to thread together the different steps of these attack chains are worth highlighting.

In the ShadowPad attack chains, Cyber AI Analyst was able to identify SMB writes from the VPN subnet to the DC, and the C2 connections from the DC. It was also able to weave together this activity into a single thread representing the attacker’s progression.

Similarly, in the data exfiltration attack chain, Cyber AI Analyst identified and connected multiple types of lateral movement over SMB and WMI and external C2 communication to various external endpoints, linking them in a single, connected incident.

These Cyber AI Analyst actions enabled a quicker understanding of the threat actor sequence of events and, in some cases, faster containment.

Attribution puzzle

Publicly shared research into ShadowPad indicates that it is predominantly used as a backdoor in People’s Republic of China (PRC)-sponsored espionage operations [5][6][7][8][9][10]. Most publicly reported intrusions involving ShadowPad  are attributed to the China-based threat actor, APT41 [11][12]. Furthermore, Google Threat Intelligence Group (GTIG) recently shared their assessment that ShadowPad usage is restricted to clusters associated with APT41 [13]. Interestingly, however, there have also been public reports of ShadowPad usage in unattributed intrusions [5].

The data theft activity that later occurred in the same Darktrace customer network as one of these ShadowPad compromises appeared to be the targeted collection and exfiltration of sensitive data. Such an objective indicates the activity may have been part of a state-sponsored operation. The tactics, techniques, and procedures (TTPs), artifacts, and C2 infrastructure observed in the data theft thread appear to resemble activity seen in previous Democratic People’s Republic of Korea (DPRK)-linked intrusion activities [15] [16] [17] [18] [19].

The distribution of payloads to the following directory locations appears to be a relatively common behavior in DPRK-sponsored intrusions.

Observed examples:

C:\ProgramData\Oracle\Java\  

C:\ProgramData\Adobe\ARM\  

C:\ProgramData\Microsoft\DRM\  

C:\ProgramData\Abletech\Client\  

C:\ProgramData\IDMComp\UltraCompare\  

C:\ProgramData\3Dconnexion\3DxWare\

Additionally, the likely compromised websites observed in the data theft thread, along with some of the target URI patterns seen in the C2 communications to these sites, resemble those seen in previously reported DPRK-linked intrusion activities.

No clear evidence was found to link the ShadowPad compromise to the subsequent data theft activity that was observed on the network of the manufacturing customer. It should be noted, however, that no clear signs of initial access were found for the data theft thread – this could suggest the ShadowPad intrusion itself represents the initial point of entry that ultimately led to data exfiltration.

Motivation-wise, it seems plausible for the data theft thread to have been part of a DPRK-sponsored operation. DPRK is known to pursue targets that could potentially fulfil its national security goals and had been publicly reported as being active in months prior to this intrusion [21]. Furthermore, the timing of the data theft aligns with the ratification of the mutual defense treaty between DPRK and Russia and the subsequent accused activities [20].

Darktrace assesses with medium confidence that a nation-state, likely DPRK, was responsible, based on our investigation, the threat actor applied resources, patience, obfuscation, and evasiveness combined with external reporting, collaboration with the cyber community, assessing the attacker’s motivation and world geopolitical timeline, and undisclosed intelligence.

Conclusion

When state-linked cyber activity occurs within an organization’s environment, previously unseen C2 infrastructure and advanced evasion techniques will likely be used. State-linked cyber actors, through their resources and patience, are able to bypass most detection methods, leaving anomaly-based methods as a last line of defense.

Two threads of activity were observed within Darktrace’s customer base over the last year: The first operation involved the abuse of Check Point VPN credentials to log in remotely to organizations’ networks, followed by the distribution of ShadowPad to an internal domain controller. The second operation involved highly targeted data exfiltration from the network of one of the customers impacted by the previously mentioned ShadowPad activity.

Despite definitive attribution remaining unresolved, both the ShadowPad and data exfiltration activities were detected by Darktrace’s Self-Learning AI, with Cyber AI Analyst playing a significant role in identifying and piecing together the various steps of the intrusion activities.  

Credit to Sam Lister (R&D Detection Analyst), Emma Foulger (Principal Cyber Analyst), Nathaniel Jones (VP), and the Darktrace Threat Research team.

Appendices

Darktrace / NETWORK model alerts

User / New Admin Credentials on Client

Anomalous Connection / Unusual Admin SMB Session

Compliance / SMB Drive Write  

Device / Anomalous SMB Followed By Multiple Model Breaches

Anomalous File / Internal / Unusual SMB Script Write

User / New Admin Credentials on Client  

Anomalous Connection / Unusual Admin SMB Session

Compliance / SMB Drive Write

Device / Anomalous SMB Followed By Multiple Model Breaches

Anomalous File / Internal / Unusual SMB Script Write

Device / New or Uncommon WMI Activity

Unusual Activity / Internal Data Transfer

Anomalous Connection / Download and Upload

Anomalous Server Activity / Rare External from Server

Compromise / Beacon to Young Endpoint

Compromise / Agent Beacon (Short Period)

Anomalous Server Activity / Anomalous External Activity from Critical Network Device

Anomalous Connection / POST to PHP on New External Host

Compromise / Sustained SSL or HTTP Increase

Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Device / Multiple C2 Model Alerts

Anomalous Connection / Data Sent to Rare Domain

Anomalous Connection / Download and Upload

Unusual Activity / Unusual External Data Transfer

Anomalous Connection / Low and Slow Exfiltration

Anomalous Connection / Uncommon 1 GiB Outbound  

MITRE ATT&CK mapping

(Technique name – Tactic ID)

ShadowPad malware threads

Initial Access - Valid Accounts: Domain Accounts (T1078.002)

Initial Access - External Remote Services (T1133)

Privilege Escalation - Exploitation for Privilege Escalation (T1068)

Privilege Escalation - Valid Accounts: Default Accounts (T1078.001)

Defense Evasion - Masquerading: Match Legitimate Name or Location (T1036.005)

Lateral Movement - Remote Services: Remote Desktop Protocol (T1021.001)

Lateral Movement - Remote Services: SMB/Windows Admin Shares (T1021.002)

Command and Control - Proxy: Internal Proxy (T1090.001)

Command and Control - Application Layer Protocol: Web Protocols (T1071.001)

Command and Control - Encrypted Channel: Asymmetric Cryptography (T1573.002)

Command and Control - Application Layer Protocol: DNS (T1071.004)

Data theft thread

Resource Development - Compromise Infrastructure: Domains (T1584.001)

Privilege Escalation - Valid Accounts: Default Accounts (T1078.001)

Privilege Escalation - Valid Accounts: Domain Accounts (T1078.002)

Execution - Windows Management Instrumentation (T1047)

Defense Evasion - Masquerading: Match Legitimate Name or Location (T1036.005)

Defense Evasion - Obfuscated Files or Information (T1027)

Lateral Movement - Remote Services: SMB/Windows Admin Shares (T1021.002)

Collection - Data from Network Shared Drive (T1039)

Command and Control - Application Layer Protocol: Web Protocols (T1071.001)

Command and Control - Encrypted Channel: Asymmetric Cryptography (T1573.002)

Command and Control - Proxy: External Proxy (T1090.002)

Exfiltration - Exfiltration Over C2 Channel (T1041)

Exfiltration - Data Transfer Size Limits (T1030)

List of indicators of compromise (IoCs)

IP addresses and/or domain names (Mid-high confidence):

ShadowPad thread

- dscriy.chtq[.]net • 158.247.199[.]185 (endpoint of C2 comms)

- cybaq.chtq[.]net (domain name used for DNS tunneling)  

Data theft thread

- yasuconsulting[.]com (45.158.12[.]7)

- hobivan[.]net (94.73.151[.]72)

- mediostresbarbas.com[.]ar (75.102.23[.]3)

- mnmathleague[.]org (185.148.129[.]24)

- goldenborek[.]com (94.138.200[.]40)

- tunemmuhendislik[.]com (94.199.206[.]45)

- anvil.org[.]ph (67.209.121[.]137)

- partnerls[.]pl (5.187.53[.]50)

- angoramedikal[.]com (89.19.29[.]128)

- awork-designs[.]dk (78.46.20[.]225)

- digitweco[.]com (38.54.95[.]190)

- duepunti-studio[.]it (89.46.106[.]61)

- scgestor.com[.]br (108.181.92[.]71)

- lacapannadelsilenzio[.]it (86.107.36[.]15)

- lovetamagotchith[.]com (203.170.190[.]137)

- lieta[.]it (78.46.146[.]147)

File names (Mid-high confidence):

ShadowPad thread:

- perflogs\1.txt

- perflogs\AppLaunch.exe

- perflogs\F4A3E8BE.tmp

- perflogs\mscoree.dll

Data theft thread

- ProgramData\Oracle\java.log

- ProgramData\Oracle\duxwfnfo

- ProgramData\Adobe\ARM\webservices.dll

- ProgramData\Adobe\ARM\wksprt.exe

- ProgramData\Oracle\Java\wksprt.exe

- ProgramData\Oracle\Java\webservices.dll

- ProgramData\Microsoft\DRM\wksprt.exe

- ProgramData\Microsoft\DRM\webservices.dll

- ProgramData\Abletech\Client\webservices.dll

- ProgramData\Abletech\Client\client.exe

- ProgramData\Adobe\ARM\rzrmxrwfvp

- ProgramData\3Dconnexion\3DxWare\3DxWare.exe

- ProgramData\3Dconnexion\3DxWare\webservices.dll

- ProgramData\IDMComp\UltraCompare\updater.exe

- ProgramData\IDMComp\UltraCompare\webservices.dll

- ProgramData\IDMComp\UltraCompare\imtrqjsaqmm

- temp\HousecallLauncher64.exe

Attacker-controlled device hostname (Mid-high confidence)

- DESKTOP-O82ILGG

References  

[1] https://www.kaspersky.com/about/press-releases/shadowpad-how-attackers-hide-backdoor-in-software-used-by-hundreds-of-large-companies-around-the-world  

[2] https://media.kasperskycontenthub.com/wp-content/uploads/sites/43/2017/08/07172148/ShadowPad_technical_description_PDF.pdf

[3] https://blog.avast.com/new-investigations-in-ccleaner-incident-point-to-a-possible-third-stage-that-had-keylogger-capacities

[4] https://securelist.com/operation-shadowhammer-a-high-profile-supply-chain-attack/90380/

[5] https://assets.sentinelone.com/c/Shadowpad?x=P42eqA

[6] https://www.cyfirma.com/research/the-origins-of-apt-41-and-shadowpad-lineage/

[7] https://www.csoonline.com/article/572061/shadowpad-has-become-the-rat-of-choice-for-several-state-sponsored-chinese-apts.html

[8] https://global.ptsecurity.com/analytics/pt-esc-threat-intelligence/shadowpad-new-activity-from-the-winnti-group

[9] https://cymulate.com/threats/shadowpad-privately-sold-malware-espionage-tool/

[10] https://www.secureworks.com/research/shadowpad-malware-analysis

[11] https://blog.talosintelligence.com/chinese-hacking-group-apt41-compromised-taiwanese-government-affiliated-research-institute-with-shadowpad-and-cobaltstrike-2/

[12] https://hackerseye.net/all-blog-items/tails-from-the-shadow-apt-41-injecting-shadowpad-with-sideloading/

[13] https://cloud.google.com/blog/topics/threat-intelligence/scatterbrain-unmasking-poisonplug-obfuscator

[14] https://www.domaintools.com/wp-content/uploads/conceptualizing-a-continuum-of-cyber-threat-attribution.pdf

[15] https://www.nccgroup.com/es/research-blog/north-korea-s-lazarus-their-initial-access-trade-craft-using-social-media-and-social-engineering/  

[16] https://www.microsoft.com/en-us/security/blog/2021/01/28/zinc-attacks-against-security-researchers/

[17] https://www.microsoft.com/en-us/security/blog/2022/09/29/zinc-weaponizing-open-source-software/  

[18] https://www.welivesecurity.com/en/eset-research/lazarus-luring-employees-trojanized-coding-challenges-case-spanish-aerospace-company/  

[19] https://blogs.jpcert.or.jp/en/2021/01/Lazarus_malware2.html  

[20] https://usun.usmission.gov/joint-statement-on-the-unlawful-arms-transfer-by-the-democratic-peoples-republic-of-korea-to-russia/

[21] https://media.defense.gov/2024/Jul/25/2003510137/-1/-1/1/Joint-CSA-North-Korea-Cyber-Espionage-Advance-Military-Nuclear-Programs.PDF  

[22] https://kyivindependent.com/first-north-korean-troops-deployed-to-front-line-in-kursk-oblast-ukraines-military-intelligence-says/

[23] https://www.microsoft.com/en-us/security/blog/2024/12/04/frequent-freeloader-part-i-secret-blizzard-compromising-storm-0156-infrastructure-for-espionage/  

[24] https://www.microsoft.com/en-us/security/blog/2024/12/11/frequent-freeloader-part-ii-russian-actor-secret-blizzard-using-tools-of-other-groups-to-attack-ukraine/  

[25] https://www.sentinelone.com/labs/chamelgang-attacking-critical-infrastructure-with-ransomware/    

[26] https://thehackernews.com/2022/06/state-backed-hackers-using-ransomware.html/  

[27] https://blog.checkpoint.com/security/check-point-research-explains-shadow-pad-nailaolocker-and-its-protection/

[28] https://www.orangecyberdefense.com/global/blog/cert-news/meet-nailaolocker-a-ransomware-distributed-in-europe-by-shadowpad-and-plugx-backdoors

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About the author
Sam Lister
SOC Analyst
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