Securing Operational Technology in Remote Working Conditions
Remote work poses new challenges for cybersecurity professionals. Use these tips to secure your operational technology (OT) in remote working conditions.
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
David Masson
VP, Field CISO
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24
Mar 2020
Remote work poses new challenges
As organizations rapidly transition to remote working, security professionals tasked with defending critical infrastructure and OT systems are faced with a broad set of challenges. New business measures, many of which were enacted overnight, have introduced risks to OT environments that can be safety-critical. This blog post summarizes the emerging vulnerabilities and offers advice for OT security professionals to stay secure under these evolving and dynamic business conditions.
Remote access
Under new business pressures, operators and engineers are being granted levels of remote access that were previously considered unacceptable risks. Remote access to OT networks has always been a significant threat vector, whether the intended users are company staff or third-party contractors and vendors. Compromised remote access can serve as a launching point for many other malicious or dangerously misguided activities – something referred to many times in the recently released MITRE ATT&CK for ICS matrix under the ‘Initial Access’ and ‘Lateral Movement’ sections. This is especially true in the current period of sweeping and sudden changes in working practices, where staff may not have been trained in advance and static cyber defenses have to be rapidly adjusted. The potential for new oversights and mistakes is at an all-time high.
Many OT security architectures heavily rely on a ‘defense-in-depth’ approach, which involves building multiple layers of defense outside the core OT functions. This has always been vulnerable to a dedicated attacker or an effective worm malware. However, recent measures have seen a rapid escalation in the most dangerous form of remote access, which likely emerges within most of those defensive layers – and without the long planning process that would usually be followed in preparation.
These changes open the door to new vulnerabilities at a time when industrial environments are already experiencing significant operator resource problems. Remote access is not efficient, which means these organizations will already be struggling. Asking these organizations to also take on new security responsibilities, that take time to put in place and facilitate, hugely exacerbates the problem.
Convergence with IT
This transition to remote access exposes some of the longer-term security challenges faced by teams overseeing industrial environments. This includes the historical trend of IT hardware, operating systems, and services invading OT networks for financial efficiency without being suitable for the availability-first environment – hence the difficulty of maintaining up-to-date patching.
The increasing interconnectivity of OT and IT means that defending against an attack on the operational side, whether intentional or as collateral damage, has become of paramount importance. Vulnerable OT equipment is often used as a gateway for a more pernicious attack on the network, and in equal measure, attacks that start in the corporate IT system can result in disruption to physical operations – causing catastrophic losses to production.
Supply chain risk
Physically establishing a test environment may be impossible given the current circumstances, and yet the production environment has to keep running. This may again result in a lower level of testing than was previously acceptable, as well as opening up another vector of attack through the supply chain – as pre-infected hardware and malware can appear directly within the production environment.
In these conditions, carrying out risk and security reviews for all vendors and the products they are purchasing has never been more important. Additional reviews and monitoring of any outsourced or open-sourced components will be critical to mitigate against supply chain risk – but these precautions may be neglected due to current business environments and policies.
An overnight change
The sudden shift in working practices will also expose the limitations of staff training – for example, in what they are supposed to be doing and not doing over remote access. Taken away from the secure environment normally supported by a location in a physical HQ, security professionals and OT engineers will now be working within their own home networks, which invariably will not be as secure as the working environment. The required level of education cannot be rolled out over this short timeframe. As well-meaning employees seek to urgently resolve business obstacles, protocol will inevitably be breached.
Further, sudden changes in static security like firewall rules are destabilizing, and more likely to have errors and unwanted permissions. Alterations to OT systems, in particular safety-critical processes, take enormous forward planning, and it is extremely rare for them to have to take place because of sudden and fundamental change.
Mitigating the risks
The transition to remote working means OT security teams will have to be able to better investigate security incidents without being onsite. This means a marked improvement in visibility and forensic capabilities is required.
The limitations of traditional security tools reliant on rules and signatures of previously identified threats will be thrown into the spotlight under the current circumstances. Organizations will instead need to move to more flexible security platforms that can adapt to sudden business changes. Hundreds of organizations have turned to cyber AI as an ally in enhancing their defense strategy to combat these OT challenges. AI is particularly suited to supporting security teams in this new set of dynamic conditions due to three key features:
The detection capability is consistent across both OT and IT technologies. These are always intermingled in real OT networks, but significant remote access increases the presence of more traditionally IT services and risks.
Its unsupervised machine learning core does not require extensive manual configuration or maintenance. This is particularly crucial at a time when working practices have changed to generally less efficient methods, meaning human resources are now at a premium.
The Cyber AI Analyst advances both of the prior themes even further by automatically applying expert IT and OT analysis skills, saving human analysts large amounts of time on triage and investigation.
The Industrial Immune System can be installed within just one hour, allowing organizations to adapt to these sudden changes within the timeframe required. Darktrace is committed to helping its customers with their urgent cyber security needs at this time of rapid and sudden change.
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.avif)
![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.avif)
