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April 2, 2024

Darktrace Threat Research Investigates Raspberry Robin Worm

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02
Apr 2024
The Darktrace Threat Research team investigates Raspberry Robin, an evasive worm in USB drives. Learn how to protect yourself from this malicious variant.

Introduction

In the face of increasingly hardened digital infrastructures and skilled security teams, malicious actors are forced to constantly adapt their attack methods, resulting in sophisticated attacks that are designed to evade human detection and bypass traditional network security measures.  

One such example that was recently investigated by Darktrace is Raspberry Robin, a highly evasive worm malware renowned for merging existing and novel techniques, as well as leveraging both physical hardware and software, to establish a foothold within organization’s networks and propagate additional malicious payloads.

What is Raspberry Robin?

Raspberry Robin, also known as ‘QNAP worm’, is a worm malware that was initially discovered at the end of 2023 [1], however, its debut in the threat landscape may have predated this, with Microsoft uncovering malicious artifacts linked to this threat (which it tracks under the name Storm-0856) dating back to 2019 [4]. At the time, little was known regarding Raspberry Robin’s objectives or operators, despite the large number of successful infections worldwide. While the identity of the actors behind Raspberry Robin still remains a mystery, more intelligence has been gathered about the malware and its end goals as it was observed delivering payloads from different malware families.

Who does Raspberry Robin target?

While it was initially reported that Raspberry Robin primarily targeted the technology and manufacturing industries, researchers discovered that the malware had actually targeted multiple sectors [3] [4]. Darktrace’s own investigations echoed this, with Raspberry Robin infections observed across various industries, including public administration, finance, manufacturing, retail education and transportation.

How does Raspberry Robin work?

Initially, it appeared that Raspberry Robin's access to compromised networks had not been utilized to deliver final-stage malware payloads, nor to steal corporate data. This uncertainty led researchers to question whether the actors involved were merely “cybercriminals playing around” or more serious threats [3]. This lack of additional exploitation was indeed peculiar, considering that attackers could easily escalate their attacks, given Raspberry Robin’s ability to bypass User Account Control using legitimate Windows tools [4].

However, at the end of July 2022, some clarity emerged regarding the operators' end goals. Microsoft researchers revealed that the access provided by Raspberry Robin was being utilized by an access broker tracked as DEV-0206 to distribute the FakeUpdates malware downloader [2]. Researchers further discovered malicious activity associated with Evil Corp TTPs (i.e., DEV-0243) [5] and payloads from the Fauppod malware family leveraging Raspberry Robin’s access [8]. This indicates that Raspberry Robin may, in fact, be an initial access broker, utilizing its presence on hundreds of infected networks to distribute additional payloads for paying malware operators. Thus far, Raspberry Robin has been observed distributing payloads linked to FIN11, Clop Gang, BumbleBee, IcedID, and TrueBot on compromised networks [12].

Raspberry Robin’s Continued Evolution

Since it first appeared in the wild, Raspberry Robin has evolved from "being a widely distributed worm with no observed post-infection actions [...] to one of the largest malware distribution platforms currently active" [8]. The fact that Raspberry Robin has become such a prevalent threat is likely due to the continual addition of new features and evasion capabilities to their malware [6] [7].  

Since its emergence, the malware has “changed its communication method and lateral movement” [6] in order to evade signature detections based on threat intelligence and previous versions. Endpoint security vendors commonly describe it as heavily obfuscated malware, employing multiple layers of evasion techniques to hinder detection and analysis. These include for example dropping a fake payload when analyzed in a sandboxed environment and using mixed-case executing commands, likely to avoid case-sensitive string-based detections.  

In more recent campaigns, Raspberry Robin further appears to have added a new distribution method as it was observed being downloaded from archive files sent as attachments using the messaging service Discord [11]. These attachments contained a legitimate and signed Windows executable, often abused by attackers for side-loading, alongside a malicious dynamic-link library (DLL) containing a Raspberry Robin sample.

Another reason for the recent success of the malware may be found in its use of one-day exploits. According to researchers, Raspberry Robin now utilizes several local privilege escalation exploits that had been recently disclosed, even before a proof of concept had been made available [9] [10]. This led cyber security professionals to believe that operators of the malware may have access to an exploit seller [6]. The use of these exploits enhances Raspberry Robin's detection evasion and persistence capabilities, enabling it to propagate on networks undetected.

Darktrace’s Coverage of Raspberry Robin

Through two separate investigations carried out by Darktrace’s Threat Research team, first in late 2022 and then in November 2023, it became evident that Raspberry Robin was capable of integrating new functionalities and tactics, techniques and procedures (TTPs) into its attacks. Darktrace DETECT™ provided full visibility over the evolving campaign activity, allowing for a comparison of the threat across both investigations. Additionally, if Darktrace RESPOND™ was enabled on affected networks, it was able to quickly mitigate and contain emerging activity during the initial stages, thwarting the further escalation of attacks.

Raspberry Robin Initial Infection

The most prevalent initial infection vector appears to be the introduction of an infected external drive, such as a USB stick, containing a malicious .LNK file (i.e., a Windows shortcut file) disguised as a thumb drive or network share. When clicked, the LNK file automatically launches cmd.exe to execute the malicious file stored on the external drive, and msiexec.exe to connect to a Raspberry Robin command-and-control (C2) endpoint and download the main malware component. The whole process leverages legitimate Windows processes and is therefore less likely to raise any alarms from more traditional security solutions. However, Darktrace DETECT was able to identify the use of Msiexec to connect to a rare endpoint as anomalous in every case investigated.

Little is currently known regarding how the external drives are infected and distributed, but it has been reported that affected USB drives had previously been used for printing at printing and copying shops, suggesting that the infection may have originated from such stores [13].

A method as simple as leaving an infected USB on a desk in a public location can be a highly effective social engineering tactic for attackers. Exploiting both curiosity and goodwill, unsuspecting individuals may innocently plug in a found USB, hoping to identify its owner, unaware that they have unwittingly compromised their device.

As Darktrace primarily operates on the network layer, the insertion of a USB endpoint device would not be within its visibility. Nevertheless, Darktrace did observe several instances wherein multiple Microsoft endpoints were contacted by compromised devices prior to the first connection to a Raspberry Robin domain. For example, connections to the URI '/fwlink/?LinkID=252669&clcid=0x409' were observed in multiple customer environments prior to the first Raspberry Robin external connection. This connectivity seems to be related to Windows attempting to retrieve information about installed hardware, such as a printer, and could also be related to the inserting of an external USB drive.

Figure 1: Device Event Log showing an affected device making connections to Microsoft endpoints, prior to contacting the Raspberry Robin C2 endpoint ‘vqdn[.]net’.
Figure 1: Device Event Log showing an affected device making connections to Microsoft endpoints, prior to contacting the Raspberry Robin C2 endpoint ‘vqdn[.]net’.

Raspberry Robin Command-and-Control Activity

In all cases investigated by Darktrace, compromised devices were detected making HTTP GET connections via the unusual port 8080 to Raspberry Robin C2 endpoints using the new user agent 'Windows Installer'.

The C2 hostnames observed were typically short and matched the regex /[a-zA-Z0-9]{2,4}.[a-zA-Z0-9]{2,6}/, and were hosted on various top-level domains (TLD) such as ‘.rocks’, ‘.pm’, and ‘.wf’. On one customer network, Darktrace observed the download of an MSI file from the Raspberry Robin domain ‘wak[.]rocks’. This package contained a heavily protected malicious DLL file whose purpose was unknown at the time.  

However, in September 2022, external researchers revealed that the main purpose of this DLL was to download further payloads and enable lateral movement, persistence and privilege escalation on compromised devices, as well as exfiltrating sensitive information about the device. As worm infections spread through networks automatically, exfiltrating device data is an essential process for threat actor to keep track of which systems have been infected.

On affected networks investigated by Darktrace, compromised devices were observed making C2 connections that contained sensitive device information, including hostnames and credentials, with additional host information likely found within the data packets [12].

Figure 2: Model Breach Event Log displaying the events that triggered the the ‘New User Agent and Suspicious Request Data’ DETECT model breach.
Figure 2: Model Breach Event Log displaying the events that triggered the the ‘New User Agent and Suspicious Request Data’ DETECT model breach.

As for C2 infrastructure, Raspberry Robin leverages compromised Internet of Things (IoT) devices such as QNAP network attached storage (NAS) systems with hijacked DNS settings [13]. NAS devices are data storage servers that provide access to the files they store from anywhere in the world. These features have been abused by Raspberry Robin operators to distribute their malicious payloads, as any uploaded file could be stored and shared easily using NAS features.

However, Darktrace found that QNAP servers are not the only devices being exploited by Raspberry Robin, with DETECT identifying other IoT devices being used as C2 infrastructure, including a Cerio wireless access point in one example. Darktrace recognized that this connection was new to the environment and deemed it as suspicious, especially as it also used new software and an unusual port for the HTTP protocol (i.e., 8080 rather than 80).

In several instances, Darktrace observed Raspberry Robin utilizing TOR exit notes as backup C2 infrastructure, with compromised devices detected connecting to TOR endpoints.

Figure 3: Raspberry Robin C2 endpoint when viewed in a sandbox environment.
Figure 3: Raspberry Robin C2 endpoint when viewed in a sandbox environment.
Figure 4: Raspberry Robin C2 endpoint when viewed in a sandbox environment.
Figure 4: Raspberry Robin C2 endpoint when viewed in a sandbox environment.

Raspberry Robin in 2022 vs 2023

Despite the numerous updates and advancements made to Raspberry Robin between the investigations carried out in 2022 and 2023, Darktrace’s detection of the malware was largely the same.

DETECT models breached during first investigation at the end of 2022:

  • Device / New User Agent
  • Anomalous Server Activity / New User Agent from Internet Facing System
  • Device / New User Agent and New IP
  • Compromise / Suspicious Request Data
  • Compromise / Uncommon Tor Usage
  • Possible Tor Usage

DETECT models breached during second investigation in late 2023:

  • Device / New User Agent and New IP
  • Device / New User Agent and Suspicious Request Data
  • Device / New User Agent
  • Device / Suspicious Domain
  • Possible Tor Usage

Darktrace’s anomaly-based approach to threat detection enabled it to consistently detect the TTPs and IoCs associated with Raspberry Robin across the two investigations, despite the operator’s efforts to make it stealthier and more difficult to analyze.

In the first investigation in late 2022, Darktrace detected affected devices downloading addition executable (.exe) files following connections to the Raspberry Robin C2 endpoint, including a numeric executable file that appeared to be associated with the Vidar information stealer. Considering the advanced evasion techniques and privilege escalation capabilities of Raspberry Robin, early detection is key to prevent the malware from downloading additional malicious payloads.

In one affected customer environment investigated in late 2023, a total of 12 devices were compromised between mid-September and the end of October. As this particular customer did not have Darktrace RESPOND, the Raspberry Robin infection was able to spread through the network unabated until the customer acted upon Darktrace DETECT’s alerts.

Had Darktrace RESPOND been enabled in autonomous response mode, it would have been able to take immediate action following the first observed connection to a Raspberry Robin C2 endpoint, by blocking connections to the suspicious endpoint and enforcing a device’s normal ‘pattern of life’.

By enforcing a pattern of life on an affected device, RESPOND would prevent it from carrying out any activity that deviates from this learned pattern, including connections to new endpoints using new software as was the case in Figure 5, effectively shutting down the attack in the first instance.

Model Breach Event Log showing RESPOND’s actions against connections to Raspberry Robin C2 endpoints.
Figure 5: Model Breach Event Log showing RESPOND’s actions against connections to Raspberry Robin C2 endpoints.

Conclusion

Raspberry Robin is a highly evasive and adaptable worm known to evolve and change its TTPs on a regular basis in order to remain undetected on target networks for as long as possible. Due to its ability to drop additional malware variants onto compromised devices, it is crucial for organizations and their security teams to detect Raspberry Robin infections at the earliest possible stage to prevent the deployment of potentially disruptive secondary attacks.

Despite its continued evolution, Darktrace's detection of Raspberry Robin remained largely unchanged across the two investigations. Rather than relying on previous IoCs or leveraging existing threat intelligence, Darktrace DETECT’s anomaly-based approach allows it to identify emerging compromises by detecting the subtle deviations in a device’s learned behavior that would typically come with a malware compromise.

By detecting the attacks at an early stage, Darktrace gave its customers full visibility over malicious activity occurring on their networks, empowering them to identify affected devices and remove them from their environments. In cases where Darktrace RESPOND was active, it would have been able to take autonomous follow-up action to halt any C2 communication and prevent the download of any additional malicious payloads.  

Credit to Alexandra Sentenac, Cyber Analyst, Trent Kessler, Senior Cyber Analyst, Victoria Baldie, Director of Incident Management

Appendices

Darktrace DETECT Model Coverage

Device / New User Agent and New IP

Device / New User Agent and Suspicious Request Data

Device / New User Agent

Compromise / Possible Tor Usage

Compromise / Uncommon Tor Usage

MITRE ATT&CK Mapping

Tactic - Technique

Command & Control - T1090.003 Multi-hop Proxy

Lateral Movement - T1210 Exploitation of remote services

Exfiltration over C2 Data - T1041 Exfiltration over C2 Channel

Data Obfuscation - T1001 Data Obfuscation

Vulnerability Scanning - T1595.002 Vulnerability Scanning

Non-Standard Port - T1571 Non-Standard Port

Persistence - T1176 Browser Extensions

Initial Access - T1189 Drive By Compromise / T1566.002  Spearphishing Link

Collection - T1185 Man in the browser

List of IoCs

IoC - Type - Description + Confidence

vqdn[.]net - Hostname - C2 Server

mwgq[.]net - Hostname - C2 Server

wak[.]rocks - Hostname - C2 Server

o7car[.]com - Hostname - C2 Server

6t[.]nz - Hostname - C2 Server

fcgz[.]net - Hostname - Possible C2 Server

d0[.]wf - Hostname - C2 Server

e0[.]wf - Hostname - C2 Server

c4z[.]pl - Hostname - C2 Server

5g7[.]at - Hostname - C2 Server

5ap[.]nl - Hostname - C2 Server

4aw[.]ro - Hostname - C2 Server

0j[.]wf - Hostname - C2 Server

f0[.]tel - Hostname - C2 Server

h0[.]pm - Hostname - C2 Server

y0[.]pm - Hostname - C2 Server

5qy[.]ro - Hostname - C2 Server

g3[.]rs - Hostname - C2 Server

5qe8[.]com - Hostname - C2 Server

4j[.]pm - Hostname - C2 Server

m0[.]yt - Hostname - C2 Server

zk4[.]me - Hostname - C2 Server

59.15.11[.]49 - IP address - Likely C2 Server

82.124.243[.]57 - IP address - C2 Server

114.32.120[.]11 - IP address - Likely C2 Server

203.186.28[.]189 - IP address - Likely C2 Server

70.124.238[.]72 - IP address - C2 Server

73.6.9[.]83 - IP address - Likely C2 Server

References

[1] https://redcanary.com/blog/raspberry-robin/  

[2] https://www.bleepingcomputer.com/news/security/microsoft-links-raspberry-robin-malware-to-evil-corp-attacks/

[3] https://7095517.fs1.hubspotusercontent-na1.net/hubfs/7095517/FLINT%202022-016%20-%20QNAP%20worm_%20who%20benefits%20from%20crime%20(1).pdf

[4] https://www.bleepingcomputer.com/news/security/microsoft-finds-raspberry-robin-worm-in-hundreds-of-windows-networks/

[5] https://therecord.media/microsoft-ties-novel-raspberry-robin-malware-to-evil-corp-cybercrime-syndicate

[6] https://securityaffairs.com/158969/malware/raspberry-robin-1-day-exploits.html

[7] https://research.checkpoint.com/2024/raspberry-robin-keeps-riding-the-wave-of-endless-1-days/

[8] https://redmondmag.com/articles/2022/10/28/microsoft-details-threat-actors-leveraging-raspberry-robin-worm.aspx

[9] https://www.bleepingcomputer.com/news/security/raspberry-robin-malware-evolves-with-early-access-to-windows-exploits/

[10] https://www.bleepingcomputer.com/news/security/raspberry-robin-worm-drops-fake-malware-to-confuse-researchers/

[11] https://thehackernews.com/2024/02/raspberry-robin-malware-upgrades-with.html

[12] https://decoded.avast.io/janvojtesek/raspberry-robins-roshtyak-a-little-lesson-in-trickery/

[13] https://blog.bushidotoken.net/2023/05/raspberry-robin-global-usb-malware.html

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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Alexandra Sentenac
Cyber Analyst
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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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