Stopping Corp-Internal Phishing Attacks with Darktrace
Discover how Darktrace Email stopped a series of multi-language phishing attacks, including an Emotet campaign in Japanese. Learn how Darktrace can help!
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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.
Language is deceptive. In the realm of email security, language can deceive a recipient into clicking a link or completing a transaction, and it can trick a security tool into thinking an email is legitimate.
It is for this reason that Darktrace/Email is not reliant on language, but rather uses mathematics to develop an understanding of ‘normal’ for every email user in an organization. This enables it to neutralize anomalous emails indicative of a threat around the world, no matter in what format or language they come.
Natural language processing
When it comes to catching a compromised account or impersonation email, how can you teach a computer to understand intent or a change of tone, compared to the normal way a person corresponds?
One of the most common approaches in email security is natural language processing. NLP looks at how to program computers to analyze natural language, commonly by exposing them to a large volume of data.
The result is a computer capable of ‘understanding’ the contents of documents, including the nuances of the language within them. The technology can then extract information in the documents as well as categorize and organize the documents themselves.
Modern-day limitations
However, using NLP is limited in scope for email security as it will often misunderstand specific jargon or colloquialisms, as well as terms that had not been invented when the computer was programmed, unless it is trained on these too. Each additional language requires the computer to learn from zero every time. NLP only works on the regional languages it has been trained on, and it is not commercially viable to teach the technology to work in all small markets.
If a company hires an email security vendor based in America, therefore, it is probable that the security vendor has invested most of their time in detecting English-based phishing threats. That is fine if the company only communicates in English, but this is often not the case. In a 21st century globalized world, the need for security technology to be language-agnostic is more critical than ever.
Not all AI is the same: Unsupervised machine learning
Darktrace/Email relies on unsupervised machine learning, which can learn on the job and does not need to be fed large data sets. It can glean insights from NLP for good measure, but it does not depend on NLP for detection or understanding.
When working with AI it is crucial to understand how the AI learns: does it learn on the job or was it trained with a labeled data set? This is particularly important when looking to understand the intent behind an email, specifically to uncover solicitation attempts either through spoofing, phishing, impersonation of a supplier or any other form of email attack.
Rather than teaching a computer to understand language in an email, Darktrace Cyber AI dynamically assesses activity across inbound and outbound emails including senders, recipients, links, IP addresses, and attachment types. The movement of all these objects are then used by the AI to create the ‘patterns of life’ for every user across all communications, including communications with external users who frequently correspond with a given business.
By taking a mathematical approach, Darktrace/Email is able to understand ‘normal’ for any user regardless of the dialect they are corresponding in, uniquely interpreting all languages from Norwegian to Latin and Persian, and subsequently identifying subtle anomalies indicative of a phishing attack or an account takeover.
Catching Emotet in Japanese
Last year, Darktrace uncovered a sophisticated Spamware campaign which leveraged Emotet, the infamous banking malware. The campaign targeted various industries with highly sophisticated phishing emails.
At a food production company in Japan, Darktrace detected six phishing emails sent over a two-day period in July.
Figure 1: An email from the Emotet campaign.
In the email above, both the subject line and the filename translate to “Regarding the invoice,” followed by a number and the date. The attacker was clearly trying to imitate a legitimate business email here, spoofing a well-known Japanese company (三菱食品(株)) and a common Japanese name (‘藤沢 昭彦’).
Darktrace/Email revealed key metrics behind the email including that the real sender was using a domain name from GMO, a Japanese company which offers cheap web email services, and that the sender’s location was actually Portugal, not Japan.
Figure 2: Darktrace/Email detects the attempt at inducement.
Darktrace/Email’s models recognized the topic anomalies and inducement attempts in the emails, regardless of the language they had been written in – giving a high anomaly score of 85. Furthermore, Darktrace’s AI determined that the extension and the MIME type in the attachments were anomalous, when compared to the documents which the user normally exchanges via email.
Portuguese threat find
In another instance, a series of malicious emails were sent to an organization in Europe. These emails used several tactics to bypass the company’s security tools, including personalized subject lines and hidden malicious URLs.
Figure 3: An interactive snapshot of Darktrace/Email’s user interface. The subject line reads ‘Notice of transfer.’
As displayed above, the email contained a link that appeared to lead to a CaixaBank domain. However, Darktrace/Email recognized this as a deliberate attempt to mislead the recipient and revealed that the link in fact led to a WordPress domain, which Cyber AI identified as 100% rare for the business.
A closer inspection revealed that these emails were sent from Vietnam. The sender had never been in any previous correspondence with the business, and the isolated link within the email was also marked as a 100% rare domain. Darktrace/Email held these malicious emails back, protecting the organization from harm.
Universal defense
These two examples demonstrate the benefits of an unsupervised machine learning approach. An AI security solution which analyzes hundreds of different metrics and does not rely on pre-existing data is a groundbreaking advantage when faced with global phishing threats that now utilize a wide range of languages.
Email-based attacks are becoming more targeted and more convincing by the day. Targeted social engineering and spear phishing with advanced translation tools bombard companies daily, in all languages.
Whether it’s a phishing attack against a local office in Korea or a solicitation attempt in Arabic – even a malicious email written in Klingon from a Star Trek convention – or any of the thousands of email exchanges which occur in countless vernaculars and tones, Darktrace/Email can keep your company safe across the world, and beyond.
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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.
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)
