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
Andrew Lawrence
VP, Threat Analysis, Americas
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07
Nov 2021
Darktrace has discovered a significant number of cases involving a successful exploit of GitLab servers — a common open source software used by developers. The vulnerability, tracked as CVE-2021-22205, allows an unauthenticated, remote attacker to execute arbitrary commands as the ‘git’ user, giving them full access to the repository, including deleting, modifying, and exfiltrating source code.
In each case discovered by Darktrace AI, attackers successfully exploited servers and ran crypto-mining malware. However, this vulnerability opens the door into a wider range of possibilities, including data exfiltration, ransomware, and supply chain attacks.
The flaw was fixed on April 14, 2021, but recent research has revealed that this vulnerability is still exploitable with over 30,000 GitLab servers remaining unpatched.
The vulnerability has affected customers in every corner of the world, with Darktrace customers in the US, EMEA and APAC all targeted. Affected industries include technology, transportation, and education.
Attack details
The cases detailed below generally follow the same pattern. First, user accounts with admin privileges are registered on a publicly accessible GitLab server belonging to an unnamed customer. This is followed by a remote execution of commands that grant the rogue accounts elevated permissions.
Figure 1: Multiple model breaches firing on an unusual data egress event on October 30, which resulted in a Proactive Threat Notification model breach.
After multiple model breaches on malicious EXE downloads and command and control (C2) activities with the TOR network, the organization received a Proactive Threat Notification (PTN) from Darktrace that immediately alerted them to the issue. This enabled the customer to remove the compromised device from the network.
The next day, Darktrace discovered cryptocurrency mining occurring on a compromised server that was communicating on a non-standard port. This triggered alerts to the customer through Darktrace’s Proactive Threat Notification service, immediately escalating the threat to their security team.
Figure 2: Multiple cryptocurrency mining model breaches from the same server firing on November 3.
The related breaches include scripts from rare external locations and rare endpoints (endpoints that have never been contacted by the breach devices in the past). Not surprisingly, the endpoints in question are crypto-mining pools.
It is important to note that this GitLab vulnerability represents only the initial attack vector, which could result in a number of scenarios. In the customer environment detailed above, crypto-mining has occurred; however, exploitation of this vulnerability could serve as the first stage of a more destructive ransomware attack, or result in stolen intellectual property.
Lastly, throughout the compromises identified across Darktrace’s customer base, it appears that the Interactsh tool was leveraged by the threat actors in the attack. Interactsh is an open-source tool for out of band data transfers and validation of security flaws, and it is commonly used by both researchers and hackers. Darktrace was easily able to identify this tool as part of the larger threat.
Cyber AI Analyst investigates
Darktrace’s Cyber AI Analyst launched an immediate investigation, stitching together different events across a five-day period and revealing four stages of the attack. This presented the security team with all the information they needed to perform effective investigation and clean up, including isolating the infected devices.
Figure 3: Cyber AI Analyst automatically investigates, piecing together the events into a single narrative.
In another customer environment, Cyber AI Analyst was again able to piece together multiple security events to present a coherent security narrative, determining that the suspicious file downloads likely contained malicious software, and recommending immediate attention from security staff.
Figure 4: In a different case, Cyber AI Analyst surfaces a summary and key metrics around the suspicious file downloads.
Cyber AI Analyst made stellar detections and Proactive Threat Notification alerted affected clients ASAP. Clients were then supported through Ask the Expert (ATE) services. There has been no evidence of ransomware thus far, but these types of attacks typically gain a foothold on Internet-exposed servers and then pivot internally to deploy ransomware.
In a third example with a separate customer, Cyber AI Analyst stitched together six different security events into a single security narrative. Here, Darktrace’s technology was able to connect the dots between C2 behavior, suspicious file downloads, unusual connections, and Tor activity, eventually leading to its discovery of cryptocurrency mining.
Cyber AI Analyst specifically identified GitLab in the suspicious file downloads from a rare external endpoint. The fact that Darktrace was able to identify this in the context of a holistic view of threatening activity across this organization’s digital ecosystem — stretching from suspicious SSL connections to the eventual crypto-mining activity — presents a remarkable picture of Cyber AI Analyst in action.
Figure 5: Cyber AI Analyst identifying the GitLab activity in the context of the wider security narrative.
Concluding thoughts
Though the patch was released in April, over 50% of deployments remain unpatched. There are potential reasons why they remain unpatched — overworked security staff, or simply negligence.
Even when CVEs are mapped and patched promptly, however, novel and never-before-seen attacks can still slip through the cracks. Before the Gitlab flaw was publicly disclosed and fixed, this vulnerability was a zero-day.
And so, rather than wait for CVEs to be publicly disclosed, organizations would be prudent to adopt technologies that can detect and respond to emerging attacks at their earliest stages — regardless of whether they are exploiting known or unknown vulnerabilities.
At Darktrace we talk a lot about the problems novel and unknown threats pose for traditional security solutions. This case shows that even when a threat is known for over six months, difficulties in implementing and rolling out patching mean it can still cause issues.
Thanks to Darktrace’s AI continuously monitoring the behavior of our customer’s devices, they were able to identify the threat at its earliest stages, before it could develop into something more disruptive like ransomware. And had the customers had Darktrace Antigena configured, the technology would have responded autonomously to contain the malicious behavior before the attackers could get past stage one.
Thanks to Darktrace analyst Waseem Akhter for his insights on the above threat find.
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
Darktrace’s analysis of Hola VPN-related activity across multiple environments reveals how peer-to-peer proxy functionality can enable malware delivery, command-and-control traffic, and cryptomining. Darktrace data highlights consistent patterns, suspicious downloads from rare endpoints, and post-compromise behavior, demonstrating how seemingly benign software can obscure threats and facilitate further malicious activity.
How Attackers Abuse the Chinese Nezha Monitoring Tool
This blog examines how attackers abuse the Chinese-developed Nezha monitoring tool, a legitimate open-source platform with remote access capabilities. Darktrace analysis of a honeypot intrusion highlights malicious deployment via containers, demonstrating how dual-use software enables stealthy operations, trust inversion, and detection challenges in dynamic cloud and enterprise environments modern.
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)
