Discover how remote access tools in exploitations across OT/ICS and corporate environments benefit from Darktrace's product suite.
Understanding remote access tools
In 2022, remote access tools continue to provide versatile support to organizations. By controlling devices remotely from across the globe, IT teams save on response costs, travel times, and can receive remote support from external parties like contractors [1 & 2]. This is particularly relevant in cases involving specialty machines such as OT/ICS systems where physical access is sometimes limited. These tools, however, come with their own risks. The following blog will discuss these risks and how they can be addressed (particularly in OT environments) by looking at two exploit examples from the popular sphere and within the Darktrace customer base.
What are remote access tools?
One of the most popular remote tools is TeamViewer, a comprehensive videoconferencing and remote management tool which can be used on both desktop and handheld devices[3]. Like other sophisticated tools, when it works as intended, it can seem like magic. However, remote access tools can be exploited and may grant privileged network access to potential threat actors. Although TeamViewer needs to be installed on both perpetrator and victim devices, if an attacker has access to a misconfigured TeamViewer device, it becomes trivial to establish a foothold and deploy malware.
How secure is remote access?
Security vulnerabilities in remote access tools
In early 2021, remote access tooling was seen on a new scale against the City of Oldsmar’s water treatment plant [4] (Figure 1). Oldsmar manages chemical concentration levels in the water for a 15,000-person city. The water treatment plant had been using TeamViewer to allow employees to share screens and work through IT issues. However, in February an employee noticed he had lost control of his mouse cursor. Initially he was unconcerned; the employee assumed that the cursor was being controlled by his boss, who regularly connected to the computer to monitor the facility’s systems. A few hours later though, the employee again saw his cursor moving out of his control and this time noticed that it was attempting to change levels of sodium hydroxide in the water supply (which is extremely dangerous for human consumption). Thankfully, the employee was able to quickly spot the changes and return them to their normal level. When looking back at the event, the key question posed by officials was where exactly the vulnerability was located in their security stack. [5]. The answer was unclear.
Figure 1: Photograph of compromised water plant in Florida
Tactics and strategies
When attackers get initial network access, the primary challenge for any enterprise is identifying a) that a device compromise has happened and b) how it happened. These were the same challenges seen in the Oldsmar attack. When the first physical signs of compromise occurred (cursor movement), the impacted user was still unsure whether the activity was malicious. A detailed investigation from Dragos revealed the how: evidence of a watering hole, reconnaissance activity a month prior, a targeted variant of the Tofsee botnet, and the potential presence of two separate threat actors [6 & 7]. The answer to both questions pointed to a complex attack. However, with Darktrace these questions become less important.
How Darktrace stops compromised remote access
Darktrace does not rely on signatures but instead has AI-based models for live detection of these tools and anomalies within the wider network. Regardless of the security ‘hole’, live detection gives security teams the potential to respond in near-live time.
According to Darktrace’s Chief Product Officer, Max Heinemeyer, the Oldsmar attack was possible because it “Abused off-the-shelf tools that were already used by the client, specifically TeamViewer. This tactic, which targeted the domain controller as the initial vector, made the malware deployment easy and effective.” [8].
Darktrace has multiple DETECT models to provide visibility over anomalous TeamViewer or remote access tool usage:
· Compliance / Incoming Remote Access Tool
· Compliance / Remote Management Tool On Client
· Compliance / Remote Management Tool On Server
· Device / Activity Identifier / Teamviewer
General incoming privileged connections:
· Compliance / Incoming Remote Desktop
· Compliance / Incoming SSH
Industrial DETECT can also highlight any new or unusual changes in ICS/OT systems:
· ICS / Incoming ICS Command
· ICS / Incoming RDP And ICS Commands
· ICS / Uncommon ICS Error
Darktrace gives security teams the opportunity for a proactive response, and it is up to those teams to utilize that opportunity. In recent months our SOC Team have also seen remote access controls being abused for high-profile threats. In one example, Darktrace detected a ransomware attack supported by the installation of AnyDesk.
Initial detection of compromise
In May a company’s mail server was detected making multiple external requests for an unusual file ‘106.exe’ using a PowerShell agent (6b79549200af33bf0322164f8a4d56a0fa08a5a62ab6a5c93a6eeef2065430ce). Although some requests were directed to sinkholes, many were otherwise successful. Subsequently a DDL file with hash f126ce9014ee87de92e734c509e1b5ab71ffb2d5a8b27171da111f96f3ba0e75 (marked by VirusTotal as malicious) was downloaded. This was followed by the installation of AnyDesk: a remote access tool likely deployed for backdoor purposes during further compromises. It is clear the threat actor then moved on to reconnaissance, with new Mimikatz use and a large volume of ICMP and SMBv.1 scanning sessions using a default credential. DCE-RPC calls were also made to the Netlogon service, suggesting a possible attempt to exploit 2020’s Zerologon vulnerability (CVE-2020-1472) [9]. When the customer then discovered a ransom note pertaining to LV (repurposed REvil), Darktrace analysts helped them to re-configure Darktrace RESPOND and turn it to active rather than human confirmation mode (Figure 2).
Figure 2: Capture of LV ransom note provided by customer
Whilst in this instance the tool was not used for initial access, it was still an important contingency tool to ensure the threat actor’s persistency as the customer tried to respond to the ongoing breach. Yet it was the visibility provided by Darktrace model detection and changes to RESPOND configuration which ensured the customer kept up with this actor and reduced the impact of the attack.
Looking back at Oldsmar, it is clear that being aware of remote access tools is only half the battle. More importantly, most organizations are asking if their use in attacks can be prevented in the first place. As an off-the-shelf tool, restricting TeamViewer use seems like an easy solution but such tools are often essential for maintenance and support operations. Even if limited to privileged users, these accounts are also subject to potential compromise. Instead, companies can take a large-scale view and consider the environment in which the Oldsmar attack occurred.
How IT & OT convergence complicated this attack
In this context, the separation of OT and IT systems is a potential solution - if attackers cannot access at-risk systems, then they also cannot attack those systems. However, with recent discourse around the IT-OT convergence and increased use of IoT devices, this separation is increasingly challenging to implement [10]. Complex networking designs, stringent patching requirements and ever-changing business/operational needs are all big considerations when establishing industrial security. In fact, Tenable’s CEO Amit Yoran encouraged less separation following Oldsmar: “There’s business reasons and efficiency reasons that you might want to connect those to be able to predict when parts are going to fail or when outages are going to occur [sic].” [11].
When neither addressing remote access use or industrial set-up provides a quick solution, then security teams need to look to third-party support to stop similar attacks. In addition to Darktrace DETECT, our Darktrace PREVENT range with PREVENT/Attack Surface Management (ASM) can also alert security teams to internet-facing devices at risk of remote access exploitation. ASM actively queries the Shodan API for open ports on company websites and exposed servers. This highlights those assets which might be vulnerable to this type of remote access.
Conclusion
In conclusion, TeamViewer and other remote access tools offer a lot of convenience for security teams but also for attackers. Attackers can remotely access important systems including those in the industrial network and install malware using remote access tools as leverage. Security teams need to know both their normal authorized activities and how to enforce them. With Darktrace DETECT, the tools are given transparency, with Darktrace RESPOND they can be blocked, and now Darktrace PREVENT/ASM helps to mitigate the risk of attack before it happens. As the professional world continues to embrace hybrid working, it becomes increasingly crucial to embrace these types of products and ensure protection against the dangers of unwanted remote access.
Thanks to Connor Mooney for his contributions to this blog.
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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.
Author
Dylan Hinz
Cyber Analyst
Gabriel Few-Wiegratz
Product Marketing Manager, Exposure Management and Incident Readiness
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.
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.
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.
Darktrace's Detection of State-Linked ShadowPad Malware
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.
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.
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.
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.
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
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.
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.
Figure 6: Screenshotof 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.
Figure 7: Darktrace model alert highlighting an internal device slowly exfiltrating data to the external endpoint, yasuconsulting[.]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
Figure 9: Cyber AI Analyst identifying and piecing together the various steps of a ShadowPad intrusion.
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
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![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.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/67d1dbda2020b168dc1d83c2_Screenshot%202025-03-12%20at%2012.07.41%E2%80%AFPM.png)
![Darktrace model alert highlighting an internal device slowly exfiltrating data to the external endpoint, yasuconsulting[.]com.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/67d1dd09fe9cf54a67c48f13_Screenshot%202025-03-12%20at%2012.14.04%E2%80%AFPM.png)
![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](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/67d1dd45f3743ebe06d81d89_Screenshot%202025-03-12%20at%2012.14.46%E2%80%AFPM.png)
