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February 29, 2024

Protecting Against AlphV BlackCat Ransomware

Learn how Darktrace AI is combating AlphV BlackCat ransomware, including the details of this ransomware and how to protect yourself from it.
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.
Written by
Sam Lister
Specialist Security Researcher
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29
Feb 2024

As-a-Service malware trending

Throughout the course of 2023, “as-a-Service” strains of malware remained the most consistently observed threat type to affect Darktrace customers, mirroring their overall prominence across the cyber threat landscape. With this trend expected to continue throughout 2024, organizations and their security teams should be prepared to defend their network against increasingly versatile and tailorable malware-as-a-service (MaaS) and ransomware-as-a-service (RaaS) strains [1].

What is ALPHV ransomware?

The ALPHV ransomware, also known as ‘BlackCat’ or ‘Noberus’, is one example of a RaaS strain that has been prominent across the threat landscape over the last few years.

ALPHV is a ransomware strain coded in the Rust programming language. The ransomware is sold as part of the RaaS economy [2], with samples of the ransomware being provided and sold by a criminal group (the RaaS ‘operator’) to other cybercriminals (the RaaS ‘affiliates’) who then gain entry to organizations' networks with the intention of detonating the ransomware and demanding ransom payments.

ALPHV was likely first used in the wild back in November 2021 [3]. Since then, it has become one of the most prolific ransomware strains, with the Federal Bureau of Investigation (FBI) reporting nearly USD 300 million in ALPHV ransom payments as of September 2023 [4].

In December 2023, the FBI and the US Department of Justice announced a successful disruption campaign against the ALPHV group, which included a takedown of the their data leak site, and the release of a decryption tool for the ransomware strain [5], and in February 2024, the US Department of State announced  a reward of up to USD 10 million for information leading to the identification or location of anyone occupying a key leadership position in the group operating the ALPHV ransomware strain [6].

The disruption campaign against the ransomware group appeared to have been successful, as evidenced by the recent, significant decline in ALPHV attacks, however, it would not be surprising for the group to simply return with new branding, in a similar vein to its apparent predecessors, DarkSide and BlackMatter [7].

How does ALPHV ransomware work?

ALPHV affiliates have been known to employ a variety of methods to progress towards their objective of detonating ALPHV ransomware [4]. In the latter half of 2023, ALPHV affiliates were observed using malicious advertising (i.e, malvertising) to deliver a Python-based backdoor-dropper known as 'Nitrogen' to users' devices [8][12]. These malvertising operations consisted in affiliates setting up malicious search engine adverts for tools such as WinSCP and AnyDesk.

Users' interactions with these adverts led them to sites resembling legitimate software distribution sites. Users' attempts to download software from these spoofed sites resulted in the delivery of a backdoor-dropping malware sample dubbed 'Nitrogen' to their devices. Nitrogen has been observed dropping a variety of command-and-control (C2) implants onto users' devices, including Cobalt Strike Beacon and Sliver C2. ALPHV affiliates often used the backdoor access afforded to them by these C2 implants to conduct reconnaissance and move laterally, in preparation for detonating ALPHV ransomware payloads.

Darktrace Detection of ALPHV Ransomware

During October 2023, Darktrace observed several cases of ALPHV affiliates attempting to infiltrate organizations' networks via the use of malvertising to socially engineer users into downloading and installing Nitrogen from impersonation websites such as 'wireshhark[.]com' and wìnscp[.]net (i.e, xn--wnscp-tsa[.]net).

While the attackers managed to bypass traditional security measures and evade detection by using a device from the customer’s IT team to perform its malicious activity, Darktrace DETECT™ swiftly identified the subtle indicators of compromise (IoCs) in the first instance. This swift detection of ALPHV, along with Cyber AI Analyst™ autonomously investigating the wide array of post-compromise activity, provided the customer with full visibility over the attack enabling them to promptly initiate their remediation and recovery efforts.

Unfortunately, in this incident, Darktrace RESPOND™ was not fully deployed within their environment, hindering its ability to autonomously counter emerging threats. Had RESPOND been fully operational here, it would have effectively contained the attack in its early stages, avoiding the eventual detonation of the ALPHV ransomware.

Figure 1: Timeline of the ALPHV ransomware attack.

In mid-October, a member of the IT team at a US-based Darktrace customer attempted to install the network traffic analysis software, Wireshark, onto their desktop. Due to the customer’s configuration, Darktrace's visibility over this device was limited to its internal traffic, despite this it was still able to identify and alert for a string of suspicious activity conducted by the device.

Initially, Darktrace observed the device making type A DNS requests for 'wiki.wireshark[.]org' immediately before making type A DNS requests for the domain names 'www.googleadservices[.]com', 'allpcsoftware[.]com', and 'wireshhark[.]com' (note the two 'h's). This pattern of activity indicates that the device’s user was redirected to the website, wireshhark[.]com, as a result of the user's interaction with a sponsored Google Search result pointing to allpcsoftware[.]com.

At the time of analysis, navigating to wireshhark[.]com directly from the browser search bar led to a YouTube video of Rick Astley's song "Never Gonna Give You Up". This suggests that the website, wireshhark[.]com, had been configured to redirect users to this video unless they had arrived at the website via the relevant sponsored Google Search result [8].

Although it was not possible to confirm this with certainty, it is highly likely that users who visited the website via the appropriate sponsored Google Search result were led to a fake website (wireshhark[.]com) posing as the legitimate website, wireshark[.]com. It seems that the actors who set up this fake version of wireshark[.]com were inspired by the well-known bait-and-switch technique known as 'rickrolling', where users are presented with a desirable lure (typically a hyperlink of some kind) which unexpectedly leads them to a music video of Rick Astley's "Never Gonna Give You Up".

After being redirected to wireshhark[.]com, the user unintentionally installed a malware sample which dropped what appears to be Cobalt Strike onto their device. The presence of Cobalt Strike on the user's desktop was evidenced by the subsequent type A DNS requests which the device made for the domain name 'pse[.]ac'. These DNS requests were responded to with the likely Cobalt Strike C2 server address, 194.169.175[.]132. Given that Darktrace only had visibility over the device’s internal traffic, it did not observe any C2 connections to this Cobalt Strike endpoint. However, the desktop's subsequent behavior suggests that a malicious actor had gained 'hands-on-keyboard' control of the device via an established C2 channel.

Figure 2: Advanced Search data showing an customer device being tricked into visiting the fake website, wireshhark[.]com.

Since the malicious actor had gained control of an IT member's device, they were able to abuse the privileged account credentials to spread Python payloads across the network via SMB and the Windows Management Instrumentation (WMI) service. The actor was also seen distributing the Windows Sys-Internals tool, PsExec, likely in an attempt to facilitate their lateral movement efforts. It was normal for this IT member's desktop to distribute files across the network via SMB, which meant that this malicious SMB activity was not, at first glance, out of place.

Figure 3: Advanced Search data showing that it was normal for the IT member's device to distribute files over SMB.

However, Darktrace DETECT recognized that the significant spike in file writes being performed here was suspicious, even though, on the surface, it seemed ‘normal’ for the device. Furthermore, Darktrace identified that the executable files being distributed were attempting to masquerade as a different file type, potentially in an attempt to evade the detection of traditional security tools.

Figure 4: Event Log data showing several Model Breaches being created in response to the IT member's DEVICE's SMB writes of Python-based executables.

An addition to DETECT’s identification of this unusual activity, Darktrace’s Cyber AI Analyst launched an autonomous investigation into the ongoing compromise and was able to link the SMB writes and the sharing of the executable Python payloads, viewing the connections as one lateral movement incident rather than a string of isolated events. After completing its investigation, Cyber AI Analyst was able to provide a detailed summary of events on one pane of glass, ensuring the customer could identify the affected device and begin their remediation.

Figure 5: Cyber AI Analyst investigation summary highlighting the IT member's desktop’s lateral movement activities.

C2 Activity

The Python payloads distributed by the IT member’s device were likely related to the Nitrogen malware, as evidenced by the payloads’ names and by the network behaviours which they engendered.  

Figure 6: Advanced Search data showing the affected device reaching out to the C2 endpoint, pse[.]ac, and then distributing Python-based executable files to an internal domain controller.

The internal devices to which these Nitrogen payloads were distributed immediately went on to contact C2 infrastructure associated with Cobalt Strike. These C2 connections were made over SSL on ports 443 and 8443.  Darktrace identified the attacker moving laterally to an internal SQL server and an internal domain controller.

Figure 7: Advanced Search data showing an internal SQL server contacting the Cobalt Strike C2 endpoint, 194.180.48[.]169, after receiving Python payloads from the IT member’s device.
Figure 8: Event Log data showing several DETECT model breaches triggering in response to an internal SQL server’s C2 connections to 194.180.48[.]169.

Once more, Cyber AI Analyst launched its own investigation into this activity and was able to successfully identify a series of separate SSL connections, linking them together into one wider C2 incident.

Figure 9: Cyber AI Analyst investigation summary highlighting C2 connections from the SQL server.

Darktrace observed the attacker using their 'hands-on-keyboard' access to these systems to elevate their privileges, conduct network reconnaissance (primarily port scanning), spread Python payloads further across the network, exfiltrate data from the domain controller and transfer a payload from GitHub to the domain controller.

Figure 10: Cyber AI Analyst investigation summary an IP address scan carried out by an internal domain controller.
Figure 12: Event Log data showing an internal domain controller contacting GitHub around the time that it was in communication with the C2 endpoint, 194.180.48[.]169.
Figure 13: Event Log data showing a DETECT model breach being created in response to an internal domain controller's large data upload to the C2 endpoint, 194.180.48[.]169.

After conducting extensive reconnaissance and lateral movement activities, the attacker was observed detonating ransomware with the organization's VMware environment, resulting in the successful encryption of the customer’s VMware vCenter server and VMware virtual machines. In this case, the attacker took around 24 hours to progress from initial access to ransomware detonation.  

If the targeted organization had been signed up for Darktrace's Proactive Threat Notification (PTN) service, they would have been promptly notified of these suspicious activities by the Darktrace Security Operations Center (SOC) in the first instance, allowing them to quickly identify affected devices and quarantine them before the compromise could escalate.

Additionally, given the quantity of high-severe alerts that triggered in response to this attack, Darktrace RESPOND would, under normal circumstances, have inhibited the attacker's activities as soon as they were identified by DETECT. However, due to RESPOND not being configured to act on server devices within the customer’s network, the attacker was able to seamlessly move laterally through the organization's server environment and eventually detonate the ALPHV ransomware.

Nevertheless, Darktrace was able to successfully weave together multiple Cyber AI Analyst incidents which it generated into a thread representing the chain of behavior that made up this attack. The thread of Incident Events created by Cyber AI Analyst provided a substantial account of the attack and the steps involved in it, which significantly facilitated the customer’s post-incident investigation efforts.  

Figure 14: Darktrace's AI Analyst weaved together 33 of the Incident Events it created together into a thread representing the attacker’s chain of behavior.

Conclusion

It is expected for malicious cyber actors to revise and upgrade their methods to evade organizations’ improving security measures. The continued improvement of email security tools, for example, has likely created a need for attackers to develop new means of Initial Access, such as the use of Microsoft Teams-based malware delivery.

This fast-paced ALPHV ransomware attack serves as a further illustration of this trend, with the actor behind the attack using malvertising to convince an unsuspecting user to download the Python-based malware, Nitrogen, from a fake Wireshark site. Unbeknownst to the user, this stealthy malware dropped a C2 implant onto the user’s device, giving the malicious actor the ‘hands-on-keyboard’ access they needed to move laterally, conduct network reconnaissance, and ultimately detonate ALPHV ransomware.

Despite the non-traditional initial access methods used by this ransomware actor, Darktrace DETECT was still able to identify the unusual patterns of network traffic caused by the attacker’s post-compromise activities. The large volume of alerts created by Darktrace DETECT were autonomously investigated by Darktrace’s Cyber AI Analyst, which was able to weave together related activities of different devices into a comprehensive timeline of the attacker’s operation. Given the volume of DETECT alerts created in response to this ALPHV attack, it is expected that Darktrace RESPOND would have autonomously inhibited the attacker’s operation had the capability been appropriately configured.

As the first post-compromise activities Darktrace observed in this ALPHV attack were seemingly performed by a member of the customer’s IT team, it may have looked normal to a human or traditional signature and rules-based security tools. To Darktrace’s Self-Learning AI, however, the observed activities represented subtle deviations from the device’s normal pattern of life. This attack, and Darktrace’s detection of it, is therefore a prime illustration of the value that Self-Learning AI can bring to the task of detecting anomalies within organizations’ digital estates.

Credit to Sam Lister, Senior Cyber Analyst, Emma Foulger, Principal Cyber Analyst

Appendices

Darktrace DETECT Model Breaches

- Compliance / SMB Drive Write

- Compliance / High Priority Compliance Model Breach

- Anomalous File / Internal / Masqueraded Executable SMB Write

- Device / New or Uncommon WMI Activity

- Anomalous Connection / New or Uncommon Service Control

- Anomalous Connection / High Volume of New or Uncommon Service Control

- Device / New or Uncommon SMB Named Pipe

- Device / Multiple Lateral Movement Model Breaches

- Device / Large Number of Model Breaches  

- SMB Writes of Suspicious Files (Cyber AI Analyst)

- Suspicious Remote WMI Activity (Cyber AI Analyst)

- Suspicious DCE-RPC Activity (Cyber AI Analyst)

- Compromise / Connection to Suspicious SSL Server

- Compromise / High Volume of Connections with Beacon Score

- Anomalous Connection / Suspicious Self-Signed SSL

- Anomalous Connection / Anomalous SSL without SNI to New External

- Compromise / Suspicious TLS Beaconing To Rare External

- Compromise / Beacon to Young Endpoint

- Compromise / SSL or HTTP Beacon

- Compromise / Agent Beacon to New Endpoint

- Device / Long Agent Connection to New Endpoint

- Compromise / SSL Beaconing to Rare Destination

- Compromise / Large Number of Suspicious Successful Connections

- Compromise / Slow Beaconing Activity To External Rare

- Anomalous Server Activity / Outgoing from Server

- Device / Multiple C2 Model Breaches

- Possible SSL Command and Control (Cyber AI Analyst)

- Unusual Repeated Connections (Cyber AI Analyst)

- Device / ICMP Address Scan

- Device / RDP Scan

- Device / Network Scan

- Device / Suspicious Network Scan Activity

- Scanning of Multiple Devices (Cyber AI Analyst)

- ICMP Address Scan (Cyber AI Analyst)

- Device / Anomalous Github Download

- Unusual Activity / Unusual External Data Transfer

- Device / Initial Breach Chain Compromise

MITRE ATT&CK Mapping

Resource Development techniques:

- Acquire Infrastructure: Malvertising (T1583.008)

Initial Access techniques:

- Drive-by Compromise (T1189)

Execution techniques:

- User Execution: Malicious File (T1204.002)

- System Services: Service Execution (T1569.002)

- Windows Management Instrumentation (T1047)

Defence Evasion techniques:

- Masquerading: Match Legitimate Name or Location (T1036.005)

Discovery techniques:

- Remote System Discovery (T1018)

- Network Service Discovery (T1046)

Lateral Movement techniques:

- Remote Services: SMB/Windows Admin Shares

- Lateral Tool Transfer (T1570)

Command and Control techniques:

- Application Layer Protocol: Web Protocols (T1071.001)

- Encrypted Channel: Asymmetric Cryptography (T1573.002)

- Non-Standard Port (T1571)

- Ingress Tool Channel (T1105)

Exfiltration techniques:

- Exfiltration Over C2 Channel (T1041)

Impact techniques:

- Data Encrypted for Impact (T1486)

List of Indicators of Compromise

- allpcsoftware[.]com

- wireshhark[.]com

- pse[.]ac • 194.169.175[.]132

- 194.180.48[.]169

- 193.42.33[.]14

- 141.98.6[.]195

References  

[1] https://darktrace.com/threat-report-2023

[2] https://www.microsoft.com/en-us/security/blog/2022/05/09/ransomware-as-a-service-understanding-the-cybercrime-gig-economy-and-how-to-protect-yourself/

[3] https://www.bleepingcomputer.com/news/security/alphv-blackcat-this-years-most-sophisticated-ransomware/

[4] https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-353a

[5] https://www.justice.gov/opa/pr/justice-department-disrupts-prolific-alphvblackcat-ransomware-variant

[6] https://www.state.gov/u-s-department-of-state-announces-reward-offers-for-criminal-associates-of-the-alphv-blackcat-ransomware-variant/

[7] https://www.bleepingcomputer.com/news/security/blackcat-alphv-ransomware-linked-to-blackmatter-darkside-gangs/

[8] https://www.trendmicro.com/en_us/research/23/f/malvertising-used-as-entry-vector-for-blackcat-actors-also-lever.html

[9] https://news.sophos.com/en-us/2023/07/26/into-the-tank-with-nitrogen/

[10] https://www.esentire.com/blog/persistent-connection-established-nitrogen-campaign-leverages-dll-side-loading-technique-for-c2-communication

[11] https://www.esentire.com/blog/nitrogen-campaign-2-0-reloads-with-enhanced-capabilities-leading-to-alphv-blackcat-ransomware

[12] https://www.esentire.com/blog/the-notorious-alphv-blackcat-ransomware-gang-is-attacking-corporations-and-public-entities-using-google-ads-laced-with-malware-warns-esentire

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.
Written by
Sam Lister
Specialist Security Researcher

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June 15, 2026

Hola VPN Abuse: From Proxy Traffic to Malware and Cryptomining

hola vpn malware cryptominingDefault blog imageDefault blog image

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.

Cyber AI Analyst investigation highlighting Hola VPN service activity potentially associated with subsequent HTTP command-and-control (C2) connections.
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.

Variation in URLs over time within the same hola[.]org domain, indicating the use of dynamically changing endpoints.
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.

 File transfer event showing the download of an executable  from the rare external endpoint 188.241.219[.]55.
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:

  1. Blocked connections to the associated ports and external endpoints
  2. Prevented all outgoing network connections from the device
  3. 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.

 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.
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)

Appendices

References

[1] https://www.virustotal.com/gui/ip-address/188.241.219.55

[2]  https://www.virustotal.com/gui/ip-address/188.241.218.111

[3] https://www.sophos.com/en-us/blog/you-do-surprise-me-exe-an-unexpected-executable-in-hola-browser

[4] https://www.virustotal.com/gui/file/d275abca286cd75af971d0459fdf1df37c7b19c514abafae5d0b04bf42ccfb45/detection

[5] https://bazaar.abuse.ch/sample/d275abca286cd75af971d0459fdf1df37c7b19c514abafae5d0b04bf42ccfb45/

[6] https://any.run/report/4cdeb5df217764a8b6a20d518b76ccb30cbe623365a13d9dcd40900950f1ed99/de3a756a-3101-4369-8922-52c586c939fb

[7] https://www.virustotal.com/gui/file/e3541caf708c075f0bb22fc68b03acd8457fea7cf0732ea935b1eb016d1c7721/community

[8] https://bitcoinwiki.org/wiki/stratum

Darktrace Model Detections

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External Locations

·      Compromise / Crypto Currency Mining Activity

·      Compromise / High Priority Crypto Currency Mining (EM)

·      Device / New User Agent

·      Anomalous Connection / New User Agent to IP Without Hostname

·      Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

·      Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

·      Antigena / Network / External Threat / Antigena Tor Block

·      Antigena / Network / External Threat / Antigena File then New Outbound Block

·      Antigena / Network / External Threat / Antigena Suspicious Activity Block

·      Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

·      Antigena / Network / External threat / Antigena Suspicious File Block

Indicators of Compromise (IoCs)

IoC –Type -Description + Confidence

188.241.219[.]55 - IP Address - Malware distribution source

188.241.218[.]111 - IP Address -Malware distribution source

hxxp://188.241.218[.]111:8080/me[.]exe - URI - Malicious payload

hxxp://188.241.219[.]55:9000/proxy-peer-windows-amd64[.]exe - URI - Malicious payload

hxxp://188.241.219[.]55:9000/peer[.]exe - URI - Malicious payload

C8088f3c8bc3542eb1ad78a7cc5306d866c8ac81 - SHA1 - Malicious payload, me[.]exe

b595a6de0f6a18975b29e6f8ebe604956a173478 - SHA1 - Malicious payload, me[.]exe

e9139a2e0839e8b9e5c9787ea936347ae56e5460 - SHA1 - Possible malicious payload

c2e80073e4cafe757d5643bd8fd45f28ad89bff9 - SHA1 - Possible malicious payload

695355eceedcdd337d8fcbd35e6a531cda75b847 - SHA1 - Possible malicious payload

f0b0d8068a1b9ab5d68a8a46842d72b870b292e7 - SHA1 - Possible malicious payload

a21c8b8cabc7670ea45bc175e185a0f9bfcf4733 - SHA1 - Malicious payload, me[.]exe

0353ca44b9f397d8f492db0b2f7a1d00a9e4406a - SHA1 - Possible malicious payload

56824c8a110e35ab303dc27a6c758cd50c36174c - SHA1 - Malicious payload, peer[.]exe

c141fa0fa505fe7f9ad5dd21d9d4d6d411739682 - SHA1 - Malicious payload, peer[.]exe

0417ec988b16f1267065185a6eea98f0bd2e17cd - SHA1 - Possible malicious payload

c54f7eaaeb3e0b528cd2584bdcb3a4b13cc0f8a2 - SHA1 - Malicious payload, peer[.]exe

11c78f15fafd53f8cc5a52b828d7cbf2a99e0b09 - SHA1 - Malicious payload, peer[.]exe

0258bf7dbb0123247db29e8799991140bbdbd9bb - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

b46043a06dd9bbd63e4214d5fbc7fd56e1ff0618 - SHA1 - Possible malicious payload

753afdecd9f5402d004e8e5f768170ae9a468ca5 - SHA1 - Possible malicious payload

8f533c7cb1524b00f7b0311c2ea8603298d6b2ca - SHA1 - Possible malicious payload

3a3bc6a5b4db1a4e961abcb002d26fe9d5e5c349 - SHA1 - Possible malicious payload

897f70eb41d302b045fcb05ed0693675e778ce57 - SHA1 - Possible malicious payload

6ddd5644809606e3dc1e2cc06059c3f5e6176f85 - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

68a94f7cdcaf8853ea99251c1ecc67ae9b32eba8 - SHA1 - Malicious payload, proxy-peer-windows-amd64[.]exe

MITRE ATT&CK Mapping

T1659 -Initial Access, Command and Control -Content Injection

T1588.001 -Resource Development -Malware

T1189 -Initial Access -Drive-by Compromise

T1105 -Command and Control -Ingress Tool Transfer

T1657 -Impact -Financial Theft

T1497.001 -Impact -Compute Hijacking

T1496 -Impact -Resource Hijacking

T1210 -Lateral Movement -Exploitation of Remote Services

T1036.012 -Stealth -Browser Fingerprint

T1071.001 -Command and Control -Web Protocols

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About the author
Min Kim
Cyber Security Analyst

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June 12, 2026

Cybersecurity for the Sports Sector: The Threats Facing a Digitized Industry in 2026

Sports Stadium cybersecurityDefault blog imageDefault blog image

Securing sporting events in 2026

When you walk into a stadium on game day, you are entering a small smart city. Ticketing, turnstiles, payments, public Wi-Fi for tens of thousands of fans, CCTV, lighting, even the HVAC all run on connected systems. The experience for fans has become unmatched, but that dependency has created a much larger attack surface than people may realize.

Our latest threat research backs that up. In the past year, a survey that Darktrace commissioned found that 84% of respondents from professional sports organizations had at least one cyber incident, and 57% were hit more than once. For a sector that relies on the impact of the live moment, those numbers translate directly into operational risk.

Why sports is a target for cyber attacks

Sport is a highly visible target with fixed timelines, so attackers know exactly when disruption will have the most impact. It also holds valuable data, athlete medical records, contracts, sponsorship deals, which carry financial, reputational, and regulatory risk if exposed. At the same time, delivery depends on a wide set of third parties: ticketing providers, broadcasters, cloud services, stadium technology. Any of those connections can become an entry point. Put visibility, timing, data, and dependency together, and you get an environment where even a small foothold can turn into a visible, time-critical incident.

How attackers target email and identity

Email and identity remain the front door. From October 2025 through March 2026, Darktrace / EMAIL™ detected more than 116,000 phishing emails aimed at sports organizations across our customer base, and our sports customers received 19% more phishing emails than organizations in other sectors. The numbers tell the story:

BY THE NUMBERS

  • 21% of phishing emails were aimed at VIPs.
  • 37% used novel social engineering.
  • 84% of malicious emails passed DMARC authentication

A large proportion of these emails passed authentication checks, which means traditional security controls are no longer a reliable barrier. Attackers are not relying on spoofed domains – they're using legitimate infrastructure and trusted platforms. Behavior matters. Once an account is compromised, the behavior shifts quickly. Login patterns change, inbox rules are created to hide responses, and accounts start being used for internal discovery or further phishing. These aren’t high-noise events. They sit in normal workflows, which is why they’re often missed.

Ransomware tells a similar story. In one case inside a sports deployment, attackers had quietly been moving data to an outside server for a full two weeks before they triggered encryption. By the time the ransom note appeared, the outcome was already set. That sequence shows up consistently is access first, movement next, disruption last. If detection starts at encryption, it’s already too late.

Why AI is an emerging blind spot in sports

The increasing adoption of AI is expanding the potential attack surface. 72% of the security professionals we surveyed expect AI to increase their cyber risk over the next year, and yet 35% are already using or planning to use it in stadium operations, the most critical functions to protect. In addition to prompt injection and AI build risks, shadow AI is becoming a more immediate issue. Staff are already putting sensitive data—performance metrics, scouting reports, contracts, health data—into tools with little or no governance. The upside is clear, but so is the exposure—and it is happening before most organizations have any visibility or control. At the same time, attackers are using the same technology to scale phishing and social engineering. The net effect is simple: more exposure, at higher speed.

How can cybersecurity professionals prepare

Across high profile events, Darktrace’s experience shows that effective cyber defense includes preparation, real‑time visibility, and the ability to respond dynamically and decisively when timing, complexity, and public exposure converge.

There are a few strategic implications for cybersecurity teams:

  • Get behavioral visibility across IT and OT, not just corporate systems.
  • Treat identity as your control plane. Most attacks in this sector start with credentials, not malware. MFA with behavioral detection helps solve that challenge.
  • Control third party and AI access the same way you control your own environment.
  • Rehearse response for live conditions, where decisions happen in minutes. Detection and response need to account for non-ideal conditions when engineers are under pressure and time constrained. In sport, timing is what turns small issues into major incidents. The same activity that would be manageable midweek becomes critical during a live event.

Why 2026 raises the cybersecurity stakes for sports

With the 2026 World Cup about to stretch across three countries and dozens of host cities, the attack surface is wide and the schedule is unforgiving.

Geopolitical signaling is raising the threat profile further. Previous international sporting events have demonstrated that nation‑state actors use the cyber domain to signal intent, influence narratives, or retaliate symbolically. In the context of the 2026 World Cup, Russia’s continued exclusion from international sport, the ongoing conflict in Ukraine, US defensive support to Ukraine, and Iran’s likely participation in the tournament introduce additional motivations for state‑aligned and non‑traditional affiliated actors to operate below the threshold of armed conflict. This doesn’t require new techniques—just the right timing and visibility.

In practice, this comes down to preparation: knowing what normal looks like across IT and OT, controlling third-party access, and spotting when behavior shifts.

In sport, disruption does not build slowly—it happens in real time and in public. By that point, the groundwork has already been set, long before the whistle goes.

About this research

Findings are based on Darktrace threat-research telemetry across sports-sector customer deployments (Q4 2025–Q1 2026) and a survey of 875 IT cybersecurity professionals in the US, UK, Australia, and Germany, fielded by Opinion Matters between May 28 and June 3, 2026. Read the full report for complete methodology, incident analysis, and strategic recommendations.

[related-resource]

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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