Ryuk Ransomware Targeting Major Companies | Expert Analysis
Discover how Ryuk ransomware targets major companies with AI-powered tools to detect unusual activity. Learn the threats behind Autonomous Response technology.
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
Max Heinemeyer
Global Field CISO
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01
Oct 2019
In recent years, cyber-criminals have increasingly directed their efforts toward sophisticated, long-haul attacks against major companies — a tactic known as “big game hunting.” Unlike standardized phishing campaigns that aim to deliver malware en masse, big game hunting involves exploiting the particular vulnerabilities of a single, high-value target. Catching such attacks requires AI-powered tools that learn what’s normal for each unique user and device, thereby shining a light on the subtle signs of unusual activity that they introduce.
In the threat detailed below, cyber-criminals targeted a major firm with Ryuk ransomware, which Darktrace observed during a trial deployment period. Leveraged very often in the final stage of such tailored attacks, Ryuk encrypts only crucial assets in each targeted environment that the attackers have handpicked. Here’s how this particular incident unfolded, as well as how AI Autonomous Response technology, if in active mode, would have contained the threat in seconds:
Incident overview
Figure 1: Clustering of alerts during intrusion (top right)
Rooted in its evolving understanding of ‘self’ for the targeted firm, Darktrace AI flagged myriad instances of anomalous behavior over the course of the incident — each represented by a dot in the visualization above. The anomalous activity is organized vertically according to how unusual each behavior was in comparison to “normal” for the users and devices involved. The colored dots represent particularly high-confidence detections, which should have prompted immediate investigation by the security team.
Compromised admin keys
The first sign of attack was the highly unusual use of an administrator account not previously seen on the network, suggesting that the attackers had gained access to the account outside the limited scope of the Darktrace trial before moving laterally to the monitored environments. Had Darktrace been deployed across the digital infrastructure, the initial hijacking of the account would have been obvious right away. Nevertheless, Darktrace alerted on the anomalous admin session repeatedly and in real time, as shown below:
Figure 2: Strong detections of compromised admin credential
This behavior is typical of big game hunting. Rather than firing their payload straight away upon accessing the network, the attackers engaged in a longer-term compromise to attain the best position for a crippling attack.
Infiltration via TrickBot
Darktrace then detected the infamous TrickBot banking trojan being downloaded onto the network. While the attacker already had access via the compromised admin credentials, Trickbot was used as a loader for further malicious files and as an additional command & control (C&C) channel. Among the most common post-exploitation steps were:
Figure 3: Detection of later-stage Trickbot download
Command & Control communication
Once the Trickbot infection had begun, Darktrace observed C&C communication back to the attackers. And whereas many devices exhibited anomalous behavior, Darktrace pinpointed one such device at the nexus of the infection. The below image illustrates the plethora of suspicious connections detected on this single device:
Figure 4: Every coloured dot represents a Darktrace detection — very obvious chains of malicious activity is seen above
Using TLS Fingerprinting — also called JA3, the subject of a previous blog post — Darktrace detected a new piece of software making encrypted connections from this device to multiple unusual destinations, a behavior known as beaconing.
Figure 5: The communication in this graph is filtered down to unusual TLS connections — clearly showing a spike in communication during the compromise
Ransomware encryption commences
Following the establishment of the connection with the C&C infrastructure, the Ryuk ransomware was finally deployed. During this “noisy” period with many suspicious SMB activities, Darktrace even more clearly indicated the seriousness and extent of the attack:
Figure 6: A sample of different, non-signature dependant Ransomware detections that fired
In just 12 hours, Ryuk had encrypted more than 200,000 files. The entire incident took place over 36 hours — after that, the company shut down its network to prevent further damage.
Ransomware retrospective
Following the incident, the business traced the initial compromise back to a part of their network in another country that Darktrace did not have visibility over during this trial period. The infection spread until it reached a recently installed file server that Darktrace was, in fact, monitoring. The attacker likely got access to an administrative account that had been used to build this server and, at that point, they had the access needed to fire the Ryuk ransomware.
This incident put Darktrace in the unique position of observing a ransomware attack wherein none of the alerts were seen or actioned by the internal IT team, demonstrating what such an attack can do absent any intervention and response. Had the company actively monitored its Darktrace deployment, the security team would have received and actioned the alerts in real time, as its thousands of users do on a daily basis.
Autonomous Response to the rescue
Had the firm deployed Autonomous Response technology, the lack of attention afforded to Darktrace’s alerts would not have mattered. Whereas four hours passed from the executable download to the first encrypted file, Autonomous Response would have neutralized the threat within seconds, preventing widespread damage and giving the security team the crucial time to catch up.
The screenshot below shows an excerpt of Darktrace’s detections at the beginning of the file server compromise. The detections are listed in chronological order from bottom to top, along with the action that Darktrace’s AI Autonomous Response tool, Antigena, would have taken:
In sum, Antigena would have taken appropriate action by enforcing normal behavior, rather than applying a binary block (e.g. completely quarantining the device) as legacy tools would.
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)
