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.
Written by
Brianna Luong (Leddy)
Sr. Technical Alliances Manager
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13
Jun 2021
While cutting-edge technology is essential for organizations to secure their digital assets, having on-hand human support to deal with threats can be invaluable for lean security teams and organizations without Autonomous Response in their digital enterprise.
Cyber AI technology recently detected the QakBot banking trojan in a customer environment, and with the help of Darktrace’s SOC team, the customer was able to shut down the attack in under two hours.
QakBot malware
QakBot has built a name for itself over the past twelve years as one of the most deadly trojans in the game. Used in fast-paced, automated attacks against individual businesses, it has the ability to drain company resources and steal vast amounts of financial data. It is often downloaded during Emotet campaigns to infect devices and harvest bank account information.
Like other banking trojans, QakBot uses a dropper to install itself on a corporate device. It then self-propagates through a system and collects credentials at machine speed. Cyber-criminals can use this information to extract private data or distribute ransomware and further malicious payloads.
QakBot is extremely difficult for traditional security tools to detect. Due to a combination of its automatic worm-like capabilities, its use of a virus dropper with delayed execution, and several other obfuscation methods, it is able to bypass the majority of legacy tools and can lead to extreme financial repercussions if not dealt with in its initial stages.
The Darktrace SOC team
Darktrace’s Security Operations Center (SOC) team, located in Cambridge, San Francisco, and Singapore, deal with a wide range of these quick-moving and stealthy threats which are identified by Cyber AI, including ransomware deployments, SaaS account takeovers, and data exfiltration.
Such attacks often use ‘Living off the Land’ techniques which make them difficult to differentiate from legitimate network traffic. Moreover, many threat actors carry out malicious activities outside of a target organization’s normal working hours, amplifying the potential impact of a breach before it is discovered.
The Darktrace SOC team provides around-the-clock coverage of customer environments through Proactive Threat Notification (PTN) and Ask the Expert (ATE) services. Alongside autonomous AI detection, these services provide additional human monitoring and support for customers undergoing significant security events.
Uncovering the QakBot banking trojan
Figure 1: Timeline of the QakBot banking trojan attack, including the response from Darktrace’s services.
At a company in the EMEA region with around 7,000 devices, Cyber AI detected the early signs of a trojan horse. The organization did not have Antigena Email analyzing its email traffic in order to respond to attacks in the inbox, so when a phishing email slipped through the gateway and was opened by a user, their device began connecting to a high volume of suspicious endpoints.
This resembled command and control (C2) communication, and, based on the unusual nature of this activity for the device and the environment, this behavior triggered multiple high scoring model breaches. One of these was a high fidelity model breach for ‘Suspicious SSL Activity’, which prompted an investigation through the Proactive Threat Notification service.
Figure 2: An example of the Cyber AI Analyst incident timeline for an infected device, showing command and control and reconnaissance activity.
An expert Darktrace analyst was alerted to the unusual connectivity by the Enterprise Immune System and began to investigate the anomalous behavior, determining that this device was exhibiting strong signs of a banking trojan infection. The analyst needed to move quickly: the trojan had immediately begun reconnaissance and was preparing to spread across the network.
Within an hour, the analyst had produced a brief report summarizing the activity and this was sent as a PTN alert to the customer. The report contained key technical information from the model breach and Cyber AI Analyst incident – including the timeframe, device hostname and IP address, suspicious external domains, and a reference for the customer to view this alert in the Darktrace UI.
Figure 3: Visual example of the Darktrace threat tray. In the QakBot attack, four Enhanced Monitoring model breaches were triggered, and these were investigated and alerted through the PTN service. They were all high scoring detections, clearly indicating a compromise.
Upon receiving the alert, the customer initiated further investigation and quickly shut down the affected device. The attack was contained in less than two hours.
Ask the Expert
After their initial remediation, the company reached out to the Darktrace team via Ask the Expert to confirm that this was a QakBot infection and to gain additional assistance in investigating the extent of the compromise.
The analyst team provided ongoing support to the investigation over the next six hours, concluding that this likely came from a phishing email and that no other devices in the environment were compromised. The analyst provided a list of observed Indicators of Compromise (IoCs) and worked with the customer to add these to the Darktrace Watched Domains List for further monitoring. The customer was also able to use this list to block the IoCs at the firewall.
The organization contained the infection, and no further suspicious behavior was observed from network devices.
Humans and AI
This case study is a perfect example of how Darktrace’s services provide constant assistance to customers every day of every week. On top of Darktrace’s advanced machine learning technology, the Darktrace SOC team serves as an additional layer of support for security teams of all sizes. Proactive Threat Notifications offer an extra set of eyes on emerging threats, while Ask The Expert provides a mechanism for customers to gain investigative support directly from Darktrace analysts.
The early detection of this banking trojan allowed the organization to deal with the threat before it could develop into a serious infection or a ransomware attack. QakBot is just one of many strains of swift self-spreading malware in today’s threat landscape. Such automated attacks consistently outpace the fastest of human defenders, exposing the desperate need for AI and autonomous systems to augment human teams and protect digital systems in real time.
If Antigena Network had been active in this environment, the suspicious external connectivity would have been blocked upon first detection, stopping the attack within seconds. In fact, the customer decided to deploy Antigena Network following this incident, and now benefits from 24/7 Autonomous Response against all emerging cyber-threats.
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.
Hola VPN Abuse: From Proxy Traffic to Malware and Cryptomining
Introduction
In enterprise environments, non-compliant software traffic can introduce unexpected exposure by creating unmanaged paths for outbound connectivity. Hola VPN is a notable example because of its peer-to-peer design, which can effectively turn user devices into routing or exit nodes for other parties’ traffic, shifting the risk profile from that of a traditional virtual private network (VPN) to something closer to a distributed proxy.
As a result, the appearance of Hola-related activity, whether from prior installation or unintended background connections, should be treated with caution. Such activity may provide a foothold for malicious behavior, including lateral movement or command-and-control communication.
This blog explores how Hola-associated activity appeared as part of broader patterns of suspicious behavior observed across the Darktrace customer base.
The campaign
In February and March 2026, Darktrace observed similar anomalous activity across multiple customer environments, with affected devices showing consistent behavioral patterns. These included connections to multiple *.hola[.]org endpoints using Hola-related user agents, suggesting interaction with Hola infrastructure rather than isolated or incidental traffic.
Following these connections, affected customer environments showed downloads of suspicious executable files from rare external endpoints 188.241.219[.]55 and 184.241.218[.]111. Both endpoints have been flagged as potentially malicious by open-source intelligence (OSINT) [1][2].
These downloads were conducted using consistent user agents across impacted customers, specifically ‘Hola svc_js_win32/1.249.408’ and ‘Hola svc_js_win32/1.251.389’, suggesting a possible association with Hola-related activity.
Notably, this pattern aligns with recent reporting that, in some cases, Hola distributed an undeclared executable component, me[.]exe, which was later assessed to be a likely Monero-mining binary introduced via a compromised delivery pipeline [3].
Case Study 1
Darktrace first observed a new device on January 19, 2026, within a customer environment based in the Europe, Middle East, and Africa (EMEA) region. On the same day it appeared on the network, the device communicated with multiple pieces of Hola VPN-linked infrastructure before downloading a binary from a hola[.]org subdomain.
Figure 1: Cyber AI Analyst investigation highlighting Hola VPN service activity potentially associated with subsequent HTTP command-and-control (C2) connections.
Subsequent Darktrace telemetry revealed a recurring pattern of activity from the day the device was first observed through to March 4, 2026. During this period, the device repeatedly issued HTTP GET requests to the URI /bwfile?size=1048576, each returning a 200 OK response, indicating successful file retrieval.
This behavior was accompanied by a POST request to /bwfile, followed by an additional GET request for a significantly larger file at /bwfile?size=26214400, suggesting a deliberate and structured file transfer pattern.
Notably, the binary download activity was not tied to a single static host. Instead, it was observed across multiple URLs that changed over time while remaining within the same hola[.]org domain. This pattern suggests the use of rotating or distributed delivery infrastructure rather than a fixed endpoint.
Figure 2: Variation in URLs over time within the same hola[.]org domain, indicating the use of dynamically changing endpoints.
Across these events, the activity was consistently associated with the user agent Hola svc_js_win32/1.249.408, further linking the traffic to Hola-related service components. Amid these persistent and unusual connections, on February 22, Darktrace observed the device connecting to 188.241.219[.]55/proxy-peer-windows-amd64[.]exe, resulting in the download of an executable file.
Figure 3: File transfer event showing the download of an executable from the rare external endpoint 188.241.219[.]55.
Based on its file hash, the downloaded file was assessed as a likely Trojan downloader [4], with import hash (imphash) values showing similarities to samples linked to Vidar, Rhadamanthys, and Stealc according to OSINT [5]. Overall, this sequence of activity suggests that Hola-related connectivity may have been leveraged as part of a broader malware delivery chain.
Darktrace’s Autonomous Response
Due to the highly unusual activity observed, Darktrace Autonomous Response was triggered by the device’s behavior. However, as the customer deployment was configured in “Human Confirmation” mode, manual approval was required before any action could be taken.
Had the deployment been set to “Fully Autonomous” mode, Darktrace would have automatically:
Blocked connections to the associated ports and external endpoints
Prevented all outgoing network connections from the device
Enforced the device’s established ‘pattern of life’, allowing normal activity to continue while restricting any anomalous behavior
Figure 4: Example of a Darktrace Autonomous Response model highlighting the action that would have been taken, demonstrating how the system identifies anomalous behavior and applies targeted containment measures to restrict suspicious network activity.
Case Study 2
While the first case focused on anomalous activity from a newly observed device, Darktrace also identified cases in which devices had already been communicating with Hola-related endpoints prior to the suspected campaign. This may suggest pre-existing Hola usage within the environment, potentially increasing exposure and creating an avenue for subsequent suspicious activity.
One case involved three devices within a customer network based in the Americas (AMS). In this instance, a different payload was identified: me[.]exe, a potentially malicious cryptocurrency miner also referred to as HolaMonitorService[.]exe [6][7]. The downloads were observed from infrastructure similar to that seen in Case 1, including an IP address within the same 188.241.0.0/16 subnet.
Connections to *.hola[.]org, alongside the use of potential Hola-related user agents consistent with those in Case 1, were also identified, further suggesting a link between the observed activity and Hola-associated infrastructure.
Darktrace observed activity indicative of unusual VPN usage on the first affected device on February 2, followed by telemetry suggesting potential Tor usage. This was later followed by the download of me[.]exe on March 10 from 188.241.218[.]111. Notably, this device was the earliest among the three within the deployment to exhibit the presence of the suspicious executable.
Figure 5: Cyber AI Analyst detection highlighting the download of a suspicious executable from a similar external endpoint in a separate deployment.
On March 5, 2026, the second affected device exhibited a slightly different progression, initiating connections to http-test1[.]hola[.]org using the user agent ‘hola_get’. This activity was followed by the download of me[.]exe from the same endpoint on March 13, consistent with the broader pattern of Hola-related downloads observed across the environment.
Figure 6: Example of Hola VPN-related connectivity observed on the network prior to the suspected campaign, indicating pre-existing usage that may have contributed to subsequent activity.
The final affected device within this customer’s network demonstrated a more limited but related pattern, also downloading me[.]exe on March 17 using the same ‘hola_get’ user agent.
While the earlier Hola VPN usage observed across the deployment may not have been directly related to the suspected malware campaign, it may nonetheless have contributed to reduced visibility. The presence of pre-existing Hola-related traffic could have obscured malicious activity, making it more difficult to distinguish legitimate usage from attacker-driven behavior and, in turn, hindering the timely identification of the emerging compromise.
Darktrace’s Autonomous Response
For this deployment, the customer had their Autonomous Response capability configured in “Fully Autonomous” mode, allowing Darktrace to take action without human intervention. As a result, the system was able to autonomously disrupt the activity as soon as relevant events were identified through model detections.
Figure 7: Darktrace Autonomous Response actions taken against suspicious activity linked to Hola VPN.
Suspected cryptomining activity
As previously noted, some of the observed executable payloads appear to be linked to cryptomining malware. Across a subset of affected customer environments, this assessment was further supported by subsequent device activity consistent with Monero mining. Affected devices established follow-on connections to multiple external endpoints aligned with known mining infrastructure, indicating post-download execution.
Considering the broader sequence of activity, this pattern may point to a wider form of abuse in which legitimate VPN-related traffic is used to mask or facilitate malicious behavior following compromise.
On several devices, the download of executable files, including a newly observed peer[.]exe, was followed by alerts indicative of cryptocurrency mining activity. Mining-related credentials such as ‘x’ were observed using the Minergate protocol to communicate with endpoints within the 89.125.255.0/24 subnet and 188.241.218[.]111, the same endpoint involved in earlier download activity. Additional credentials appeared to reflect device-specific CPU identifiers, for example ‘12th Gen Intel(R) Core (TM) i5-1235U’.
Observed mining methods included login, submit, and job, consistent with active participation in a pool-based mining workflow rather than passive or incidental contact. The login method indicates that the host authenticated to the mining service as a worker, job reflects the assignment of computational tasks, and submit shows completed work being returned to the pool [8]. This sequence suggests that affected devices were actively contributing processing resources as part of an unauthorized distributed mining operation.
The presence of unauthorized cryptominers can lead to degraded system performance and reduced device stability. Beyond the immediate resource impact, such activity often serves as an indicator of a broader compromise rather than an isolated issue. This may increase the risk of further malware deployment, persistence mechanisms, and lateral movement, particularly in environments where the initial intrusion has not been fully contained.
Conclusion
Across affected environments, detections such as unusual VPN usage, connections to Hola infrastructure, anomalous HTTP activity, suspicious file downloads, and subsequent cryptomining behavior were linked into a single, evolving incident narrative. This aggregation provided a clearer view of attack progression, enabling security teams to understand not just isolated alerts, but the full sequence of compromise from initial contact through to post-exploitation.
Ultimately, these activities show that the risk posed by non-compliant software such as Hola VPN can extend far beyond simple policy violations. What began as traffic to Hola-related infrastructure was, in multiple cases, followed by behavior suggesting deliberate misuse, including suspicious executable downloads using Hola-related user agents and, in some instances, evidence of active cryptomining. These were not isolated anomalies, but elements of a broader pattern in which seemingly benign proxy or VPN-related communications may have created a pathway for malicious delivery and unauthorized resource exploitation.
The significance of this activity lies not only in the downloads or mining, but in what it reveals about an attacker’s ability to blend malicious operations into traffic associated with software that may already have a foothold in the environment. When unapproved software operates within an enterprise, it can reduce visibility, blur the distinction between legitimate and malicious traffic, and create opportunities to extend compromise in ways that are persistent and difficult to detect. Darktrace’s anomaly-based approach enables these behavioral distinctions to be identified, regardless of whether the device is new or long established within the network.
Credit to Min Kim (Associate Principal Analyst), Priya Thapa (Senior Cyber Analyst) Edited by Ryan Traill (Content Manager)
![Variation in URLs over time within the same hola[.]org domain, indicating the use of dynamically changing endpoints.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/6a304bdeff111215c0436931_Screenshot%202026-06-15%20at%2012.00.43%E2%80%AFPM.png)
![File transfer event showing the download of an executable from the rare external endpoint 188.241.219[.]55.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/6a304c2468d20aeff16a2721_Screenshot%202026-06-15%20at%2012.01.53%E2%80%AFPM.png)
