From Call to Compromise: Darktrace’s Response to a Vishing-Induced Network Attack
03
Oct 2024
When a remote user fell victim to a vishing attack, allowing a malicious actor to gain access to a customer network, Darktrace swiftly detected the intrusion and responded effectively. This prompt action prevented any data loss and reinforced trust in Darktrace’s robust security measures.
What is vishing?
Vishing, or voice phishing, is a type of cyber-attack that utilizes telephone devices to deceive targets. Threat actors typically use social engineering tactics to convince targets that they can be trusted, for example, by masquerading as a family member, their bank, or trusted a government entity. One method frequently used by vishing actors is to intimidate their targets, convincing them that they may face monetary fines or jail time if they do not provide sensitive information.
What makes vishing attacks dangerous to organizations?
Vishing attacks utilize social engineering tactics that exploit human psychology and emotion. Threat actors often impersonate trusted entities and can make it appear as though a call is coming from a reputable or known source. These actors often target organizations, specifically their employees, and pressure them to obtain sensitive corporate data, such as privileged credentials, by creating a sense of urgency, intimidation or fear. Corporate credentials can then be used to gain unauthorized access to an organization’s network, often bypassing traditional security measures and human security teams.
Darktrace’s coverage of vishing attack
On August 12, 2024, Darktrace / NETWORK identified malicious activity on the network of a customer in the hospitality sector. The customer later confirmed that a threat actor had gained unauthorized access through a vishing attack. The attacker successfully spoofed the IT support phone number and called a remote employee, eventually leading to the compromise.
Figure 1: Timeline of events in the kill chain of this attack.
Establishing a Foothold
During the call, the remote employee was requested to authenticate via multi-factor authentication (MFA). Believing the caller to be a member of their internal IT support, using the legitimate caller ID, the remote user followed the instructions and confirmed the MFA prompt, providing access to the customer’s network.
This authentication allowed the threat actor to login into the customer’s environment by proxying through their Virtual Private Network (VPN) and gain a foothold in the network. As remote users are assigned the same static IP address when connecting to the corporate environment, the malicious actor appeared on the network using the correct username and IP address. While this stealthy activity might have evaded traditional security tools and human security teams, Darktrace’s anomaly-based threat detection identified an unusual login from a different hostname by analyzing NTLM requests from the static IP address, which it determined to be anomalous.
Observed Activity
On 2024-08-12 the static IP was observed using a credential belonging to the remote user to initiate an SMB session with an internal domain controller, where the authentication method NTLM was used
A different hostname from the usual hostname associated with this remote user was identified in the NTLM authentication request sent from a device with the static IP address to the domain controller
This device does not appear to have been seen on the network prior to this event.
Darktrace, therefore, recognized that this login was likely made by a malicious actor.
Internal Reconnaissance
Darktrace subsequently observed the malicious actor performing a series of reconnaissance activities, including LDAP reconnaissance, device hostname reconnaissance, and port scanning:
The affected device made a 53-second-long LDAP connection to another internal domain controller. During this connection, the device obtained data about internal Active Directory (AD) accounts, including the AD account of the remote user
The device made HTTP GET requests (e.g., HTTP GET requests with the Target URI ‘/nice ports,/Trinity.txt.bak’), indicative of Nmap usage
The device started making reverse DNS lookups for internal IP addresses.
Figure 2: Model alert showing the IP address from which the malicious actor connected and performed network scanning activities via port 9401.
Figure 3: Model Alert Event Log showing the affected device connecting to multiple internal locations via port 9401.
Lateral Movement
The threat actor was also seen making numerous failed NTLM authentication requests using a generic default Windows credential, indicating an attempt to brute force and laterally move through the network. During this activity, Darktrace identified that the device was using a different hostname than the one typically used by the remote employee.
Cyber AI Analyst
In addition to the detection by Darktrace / NETWORK, Darktrace’s Cyber AI Analyst launched an autonomous investigation into the ongoing activity. The investigation was able to correlate the seemingly separate events together into a broader incident, continuously adding new suspicious linked activities as they occurred.
Figure 4: Cyber AI Analyst investigation showing the activity timeline, and the activities associated with the incident.
Upon completing the investigation, Cyber AI Analyst provided the customer with a comprehensive summary of the various attack phases detected by Darktrace and the associated incidents. This clear presentation enabled the customer to gain full visibility into the compromise and understand the activities that constituted the attack.
Figure 5: Cyber AI Analyst displaying the observed attack phases and associated model alerts.
Darktrace Autonomous Response
Despite the sophisticated techniques and social engineering tactics used by the attacker to bypass the customer’s human security team and existing security stack, Darktrace’s AI-driven approach prevented the malicious actor from continuing their activities and causing more harm.
Darktrace’s Autonomous Response technology is able to enforce a pattern of life based on what is ‘normal’ and learned for the environment. If activity is detected that represents a deviation from expected activity from, a model alert is triggered. When Darktrace’s Autonomous Response functionality is configured in autonomous response mode, as was the case with the customer, it swiftly applies response actions to devices and users without the need for a system administrator or security analyst to perform any actions.
In this instance, Darktrace applied a number of mitigative actions on the remote user, containing most of the activity as soon as it was detected:
Block all outgoing traffic
Enforce pattern of life
Block all connections to port 445 (SMB)
Block all connections to port 9401
Figure 6: Darktrace’s Autonomous Response actions showing the actions taken in response to the observed activity, including blocking all outgoing traffic or enforcing the pattern of life.
The growing threat of vishing in a remote workforce
This vishing attack underscores the significant risks remote employees face and the critical need for companies to address vishing threats to prevent network compromises. The remote employee in this instance was deceived by a malicious actor who spoofed the phone number of internal IT Support and convinced the employee to perform approve an MFA request. This sophisticated social engineering tactic allowed the attacker to proxy through the customer’s VPN, making the malicious activity appear legitimate due to the use of static IP addresses.
Despite the stealthy attempts to perform malicious activities on the network, Darktrace’s focus on anomaly detection enabled it to swiftly identify and analyze the suspicious behavior. This led to the prompt determination of the activity as malicious and the subsequent blocking of the malicious actor to prevent further escalation.
While the exact motivation of the threat actor in this case remains unclear, the 2023 cyber-attack on MGM Resorts serves as a stark illustration of the potential consequences of such threats. MGM Resorts experienced significant disruptions and data breaches following a similar vishing attack, resulting in financial and reputational damage [1]. If the attack on the customer had not been detected, they too could have faced sensitive data loss and major business disruptions. This incident underscores the critical importance of robust security measures and vigilant monitoring to protect against sophisticated cyber threats.
Darktrace’s First 6: Half-Year Threat Report 2024 highlights the latest attack trends and key threats observed by the Darktrace Threat Research team in the first six months of 2024.
Focuses on anomaly detection and behavioral analysis to identify threats
Maps mitigated cases to known, publicly attributed threats for deeper context
Offers guidance on improving security posture to defend against persistent threats
Appendices
Credit to Rajendra Rushanth (Cyber Security Analyst) and Ryan Traill (Threat Content Lead)
Darktrace Model Detections
Device / Unusual LDAP Bind and Search Activity
Device / Attack and Recon Tools
Device / Network Range Scan
Device / Suspicious SMB Scanning Activity
Device / RDP Scan
Device / UDP Enumeration
Device / Large Number of Model Breaches
Device / Network Scan
Device / Multiple Lateral Movement Model Breaches (Enhanced Monitoring)
Device / Reverse DNS Sweep
Device / SMB Session Brute Force (Non-Admin)
List of Indicators of Compromise (IoCs)
IoC - Type – Description
/nice ports,/Trinity.txt.bak - URI – Unusual Nmap Usage
Oops! Something went wrong while submitting the form.
Newsletter
Enjoying the blog?
Sign up to receive the latest news and insights from the Darktrace newsletter – delivered directly to your inbox
Thanks for signing up!
Look out for your first newsletter, coming soon.
Oops! Something went wrong while submitting the form.
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.
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
.jpg)
.jpeg)
![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)
