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February 9, 2023

Vidar Network: Analyzing a Prolific Info Stealer

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09
Feb 2023
Discover the latest insights on the Vidar network-based info stealer from our Darktrace experts and stay informed on cybersecurity threats.

In the latter half of 2022, Darktrace observed a rise in Vidar Stealer infections across its client base. These infections consisted in a predictable series of network behaviors, including usage of certain social media platforms for the retrieval of Command and Control (C2) information and usage of certain URI patterns in C2 communications. In the blog post, we will provide details of the pattern of network activity observed in these Vidar Stealer infections, along with details of Darktrace’s coverage of the activity. 

Background on Vidar Stealer

Vidar Stealer, first identified in 2018, is an info-stealer capable of obtaining and then exfiltrating sensitive data from users’ devices. This data includes banking details, saved passwords, IP addresses, browser history, login credentials, and crypto-wallet data [1]. The info-stealer, which is typically delivered via malicious spam emails, cracked software websites, malicious ads, and websites impersonating legitimate brands, is known to access profiles on social media platforms once it is running on a user’s device. The info-stealer does this to retrieve the IP address of its Command and Control (C2) server. After retrieving its main C2 address, the info-stealer, like many other info-stealers, is known to download several third-party Dynamic Link Libraries (DLLs) which it uses to gain access to sensitive data saved on the infected device. The info-stealer then bundles the sensitive data which it obtains and sends it back to the C2 server.  

Details of Attack Chain 

In the second half of 2022, Darktrace observed the following pattern of activity within many client networks:

1. User’s device makes an HTTPS connection to Telegram and/or to a Mastodon server

2. User’s device makes an HTTP GET request with an empty User-Agent header, an empty Host header and a target URI consisting of 4 digits to an unusual, external endpoint

3. User’s device makes an HTTP GET request with an empty User-Agent header, an empty Host header and a target URI consisting of 10 digits followed by ‘.zip’ to the unusual, external endpoint

4. User’s device makes an HTTP POST request with an empty User-Agent header, an empty Host header, and the target URI ‘/’ to the unusual, external endpoint 

Figure 1: The above network logs, taken from Darktrace’s Advanced Search interface, show an infected device contacting Telegram and then making a series of HTTP requests to 168.119.167[.]188
Figure 2:  The above network logs, taken from Darktrace’s Advanced Search interface, show an infected device contacting a Mastadon server and then making a series of HTTP requests to 107.189.31[.]171

Each of these activity chains occurred as the result of a user running Vidar Stealer on their device. No common method was used to trick users into running Vidar Stealer on their devices. Rather, a variety of methods, ranging from malspam to cracked software downloads appear to have been used. 

Once running on a user’s device, Vidar Stealer went on to make an HTTPS connection to either Telegram (https://t[.]me/) or a Mastodon server (https://nerdculture[.]de/ or https://ioc[.]exchange/). Telegram and Mastodon are social media platforms on which users can create profiles. Malicious actors are known to create profiles on these platforms and then to embed C2 information within the profiles’ descriptions [2].  In the Vidar cases observed across Darktrace’s client base, it seems that Vidar contacted Telegram and/or Mastodon servers in order to retrieve the IP address of its C2 server from a profile description. Since social media platforms are typically trusted, this ‘Dead Drop’ method of sharing C2 details with malware samples makes it possible for threat actors to regularly update C2 details without the communication of these changes being blocked. 

Figure 3: A screenshot a profile on the Mastodon server, nerdculture[.]de. The profile’s description contains a C2 address 

After retrieving its C2 address from the description of a Telegram or Mastodon profile, Vidar went on to make an HTTP GET request with an empty User-Agent header, an empty Host header and a target URI consisting of 4 digits to its C2 server. The sequences of digits appearing in these URIs are campaign IDs. The C2 server responded to Vidar’s GET request with configuration details that likely informed Vidar’s subsequent data stealing activities. 

After receiving its configuration details, Vidar went on to make a GET request with an empty User-Agent header, an empty Host header and a target URI consisting of 10 digits followed by ‘.zip’ to the C2 server. This request was responded to with a ZIP file containing legitimate, third-party Dynamic Link Libraries such as ‘vcruntime140.dll’. Vidar used these libraries to gain access to sensitive data saved on the infected host. 

Figure 4: The above PCAP provides an example of the configuration details provided by a C2 server in response to Vidar’s first GET request 
Figure 5: Examples of DLLs included within ZIP files downloaded by Vidar samples

After downloading a ZIP file containing third-party DLLs, Vidar made a POST request containing hundreds of kilobytes of data to the C2 endpoint. This POST request likely represented exfiltration of stolen information. 

Darktrace Coverage

After infecting users’ devices, Vidar contacted either Telegram or Mastodon, and then made a series of HTTP requests to its C2 server. The info-stealer’s usage of social media platforms, along with its usage of ZIP files for tool transfer, complicate the detection of its activities. The info-stealer’s HTTP requests to its C2 server, however, caused the following Darktrace DETECT/Network models to breach:

  • Anomalous File / Zip or Gzip from Rare External Location 
  • Anomalous File / Numeric File Download
  • Anomalous Connection / Posting HTTP to IP Without Hostname

These model breaches did not occur due to users’ devices contacting IP addresses known to be associated with Vidar. In fact, at the time that the reported activities occurred, many of the contacted IP addresses had no OSINT associating them with Vidar activity. The cause of these model breaches was in fact the unusualness of the devices’ HTTP activities. When a Vidar-infected device was observed making HTTP requests to a C2 server, Darktrace recognised that this behavior was highly unusual both for the device and for other devices in the network. Darktrace’s recognition of this unusualness caused the model breaches to occur. 

Vidar Stealer infections move incredibly fast, with the time between initial infection and data theft sometimes being less than a minute. In cases where Darktrace’s Autonomous Response technology was active, Darktrace RESPOND/Network was able to autonomously block Vidar’s connections to its C2 server immediately after the first connection was made. 

Figure 6: The Event Log for an infected device, shows that Darktrace RESPOND/Network autonomously intervened 1 second after the device first contacted the C2 server 95.217.245[.]254

Conclusion 

In the latter half of 2022, a particular pattern of activity was prolific across Darktrace’s client base, with the pattern being seen in the networks of customers across a broad range of industry verticals and sizes. Further investigation revealed that this pattern of network activity was the result of Vidar Stealer infection. These infections moved fast and were effective at evading detection due to their usage of social media platforms for information retrieval and their usage of ZIP files for tool transfer. Since the impact of info-stealer activity typically occurs off-network, long after initial infection, insufficient detection of info-stealer activity leaves victims at risk of attackers operating unbeknownst to them and of powerful attack vectors being available to launch broad compromises. 

Despite the evasion attempts made by the operators of Vidar, Darktrace DETECT/Network was able to detect the unusual HTTP activities which inevitably resulted from Vidar infections. When active, Darktrace RESPOND/Network was able to quickly take inhibitive actions against these unusual activities. Given the prevalence of Vidar Stealer [3] and the speed at which Vidar Stealer infections progress, Autonomous Response technology proves to be vital for protecting organizations from info-stealer activity.  

Thanks to the Threat Research Team for its contributions to this blog.

MITRE ATT&CK Mapping

List of IOCs

107.189.31[.]171 - Vidar C2 Endpoint

168.119.167[.]188 – Vidar C2 Endpoint 

77.91.102[.]51 - Vidar C2 Endpoint

116.202.180[.]202 - Vidar C2 Endpoint

79.124.78[.]208 - Vidar C2 Endpoint

159.69.100[.]194 - Vidar C2 Endpoint

195.201.253[.]5 - Vidar C2 Endpoint

135.181.96[.]153 - Vidar C2 Endpoint

88.198.122[.]116 - Vidar C2 Endpoint

135.181.104[.]248 - Vidar C2 Endpoint

159.69.101[.]102 - Vidar C2 Endpoint

45.8.147[.]145 - Vidar C2 Endpoint

159.69.102[.]192 - Vidar C2 Endpoint

193.43.146[.]42 - Vidar C2 Endpoint

159.69.102[.]19 - Vidar C2 Endpoint

185.53.46[.]199 - Vidar C2 Endpoint

116.202.183[.]206 - Vidar C2 Endpoint

95.217.244[.]216 - Vidar C2 Endpoint

78.46.129[.]14 - Vidar C2 Endpoint

116.203.7[.]175 - Vidar C2 Endpoint

45.159.249[.]3 - Vidar C2 Endpoint

159.69.101[.]170 - Vidar C2 Endpoint

116.202.183[.]213 - Vidar C2 Endpoint

116.202.4[.]170 - Vidar C2 Endpoint

185.252.215[.]142 - Vidar C2 Endpoint

45.8.144[.]62 - Vidar C2 Endpoint

74.119.192[.]157 - Vidar C2 Endpoint

78.47.102[.]252 - Vidar C2 Endpoint

212.23.221[.]231 - Vidar C2 Endpoint

167.235.137[.]244 - Vidar C2 Endpoint

88.198.122[.]116 - Vidar C2 Endpoint

5.252.23[.]169 - Vidar C2 Endpoint

45.89.55[.]70 - Vidar C2 Endpoint

References

[1] https://blog.cyble.com/2021/10/26/vidar-stealer-under-the-lens-a-deep-dive-analysis/

[2] https://asec.ahnlab.com/en/44554/

[3] https://blog.sekoia.io/unveiling-of-a-large-resilient-infrastructure-distributing-information-stealers/

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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Roberto Romeu
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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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