Fusing Vulnerability and Threat Data: Enhancing the Depth of Attack Analysis
This blog highlights how vulnerability data, collected using Cado's new vulnerability discovery feature, can be fused with threat data to help deepen the understanding of an attack, as well as guide remediation efforts.
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
Paul Bottomley
Director of Product Management | Darktrace
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02
Apr 2025
Cado Security, recently acquired by Darktrace, is excited to announce a significant enhancement to its data collection capabilities, with the addition of a vulnerability discovery feature for Linux-based cloud resources. According to Darktrace’s Annual Threat Report 2024, the most significant campaigns observed in 2024 involved the ongoing exploitation of significant vulnerabilities in internet-facing systems. Cado’s new vulnerability discovery capability further deepens its ability to provide extensive context to security teams, enabling them to make informed decisions about threats, faster than ever.
Deep context to accelerate understanding and remediation
Context is critical when understanding the circumstances surrounding a threat. It can also take many forms – alert data, telemetry, file content, business context (for example asset criticality, core function of the resource), and risk context, such as open vulnerabilities.
When performing an investigation, it is common practice to understand the risk profile of the resource impacted, specifically determining open vulnerabilities and how they may relate to the threat. For example, if an analyst is triaging an alert related to an internet-facing Webserver running Apache, it would greatly benefit the analyst to understand open vulnerabilities in the Apache version that is running, if any of them are exploitable, whether a fix is available, etc. This dataset also serves as an invaluable source when developing a remediation plan, identifying specific vulnerabilities to be prioritised for patching.
Data acquisition in Cado
Cado is the only platform with the ability to perform full forensic captures as well as utilize instant triage collection methods, which is why fusing host-based artifact data with vulnerability data is such an exciting and compelling development.
The vulnerability discovery feature can be run as part of an acquisition – full or triage – as well as independently using a fast ‘Scan only’ mode.
Figure 1: A fast vulnerability scan being performed on the acquired evidence
Once the acquisition has completed, the user will have access to a ‘Vulnerabilities’ table within their investigation, where they are able to view and filter open vulnerabilities (by Severity, CVE ID, Resource, and other properties), as well as pivot to the full Event Timeline. In the Event Timeline, the user will be able to identify whether there is any malicious, suspicious or other interesting activity surrounding the vulnerable package, given the unified timeline presents a complete chronological dataset of all evidence and context collected.
Figure 2: Vulnerabilities discovered on the acquired evidence
Figure 3: Pivot from the Vulnerabilities table to the Event Timeline provides an in-depth view of file and process data associated with the vulnerable package selected. In this example, Apache2.
Future work
In the coming months, we’ll be releasing initial versions of highly anticipated integrations between Cado and Darktrace, including the ability to ingest Darktrace / CLOUD alerts which will automatically trigger a forensic capture (as well as a vulnerability discovery) of the impacted assets.
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.
Bringing Together SOC and IR teams with Automated Threat Investigations for the Hybrid World
Incident response often breaks down after detection due to fragmented tools and limited visibility. Darktrace unifies detection and investigation across cloud and on-premises environments, enabling automated evidence capture and faster, clearer response.
React2Shell Reflections: Cloud Insights, Finance Sector Impacts, and How Threat Actors Moved So Quickly
This blog breaks down how attackers rapidly weaponized the React2Shell vulnerability, with a particular focus on cloud‑native financial environments. Drawing on Darktrace’s honeypot research, it explores emerging threat actor tooling, exploitation timelines, and why behavioral‑anomaly‑led security is critical in today’s cloud landscape.
Advancing the Use of Frontier AI in Cybersecurity: Darktrace Joins the OpenAI Daybreak Cyber Partner Program to Explore Defensive AI Integrations
Darktrace joins the OpenAI Daybreak Cyber Partner Program
Today, we announced that Darktrace is joining the OpenAI Daybreak Cyber Partner Program. We’ll be partnering with OpenAI to explore how their cyber capabilities can be integrated within Darktrace products and services to bring new capabilities to our customers.
This partnership is an exciting opportunity to bring together Darktrace’s behavioral AI modelling of the organization with OpenAI’s advanced contextual capabilities to create a new level of understanding for security teams. To understand the impact, it’s helpful to start with how we think about the problem.
At Darktrace, we built our AI in support of the core belief that cybersecurity needs to understand the business it is defending. That's why our Self-Learning AI is designed to help organizations understand normal and abnormal behavior for each organization across their digital environment, including users and identities, networks and cloud, email and collaboration tools, and now AI systems and agents with the rollout of Darktrace / SECURE AI™.
Our goal was never simply to spot known attacks faster. It was to help defenders understand how their organization behaves, potential risks and impact, and where disruption could take hold so they could prepare for the unknown threats that they may not have seen or even imagined before.
That’s exactly what is happening across the threat landscape today. Attacks keep changing; techniques shift, infrastructure evolves, and attackers move with more speed, precision, and context. And now they have even more AI and automation on their side. Attackers are exploiting identities, trusted services, SaaS applications, and business workflows. They are not always breaking in; often, the threat may come from within the organization in the form of insider threat or even rogue agents.
In this reality, defenders need a combination of deep AI modelling of the organization and AI that can connect identified threats to concrete business context, translating this information into real world value, and allow action before risk becomes disruption.
That is the opportunity we see in partnering with OpenAI.
What is the OpenAI Daybreak Cyber Partner Program and why is Darktrace joining
The OpenAI Daybreak Cyber Partner Program is focused on advancing the safe use of AI for cybersecurity. As part of the program’s next phase, OpenAI is working with a select group of trusted partners including Darktrace on scoped product integrations, managed services, and partner-delivered defensive capabilities. We’ll be exploring how OpenAI’s advanced frontier AI capabilities can support defenders in the tools and workflows they already use each day.
For Darktrace, this is a natural extension of our expertise and the work we have been doing for a decade: safely and securely applying the most effective AI techniques in combination to understand organizations, detecting malicious activity at the earliest indicators, and helping cyber defenders act faster.
By using the advanced models and more precise safeguards available in the OpenAI Daybreak Cyber Partner Program, Darktrace and OpenAI will combine Darktrace’s real-time behavioral understanding of an organization's digital estate with OpenAI's ability to interpret wider business context.
This is a unique and powerful combination of insights that could give organizations deeper context on technical risk and help them prioritize workloads and investigations based on potential impact to revenue, operations, and resilience. It can also provide security teams and executives with intelligence into which events matter most to the business, why they matter, and what action to take. Not just finding, for instance, that an agent is compromised, but highlighting that the compromised agent could shut down order fulfilment within the next three hours.
Why the Darktrace and OpenAI partnership matters for defenders
Security teams today have more attack surface, more complex environments to protect, and an increasing volume of threats. The ability to act quickly is critical, but they also need to be able to focus on the risks that could have the greatest business impact.
That is especially important as attackers use AI to scale phishing, automate reconnaissance, find weaknesses, and blend into normal business activity. At the same time, organizations and their employees are using AI to innovate, which introduces an even broader attack surface and new set of risks. Defenders need AI that can operate across the same complexity, but safely, transparently, and in service of building more resilience. And they need a way to safely adopt, govern, and defend AI across their organizations.
Joining the OpenAI Daybreak Cyber Partner Program is another step in that direction. We are still early in this work, and we will take a careful, disciplined approach. But the direction is clear: protecting organizations requires AI that understands the business, not just the attack.
At Darktrace, that is exactly where we remain focused and why we are so excited about this partnership with OpenAI.
Hola VPN Abuse: From Proxy Traffic to Malware and Cryptomining
Introduction
In enterprise environments, non-compliant software traffic can introduce unexpected exposure by creating unmanaged paths for outbound connectivity. Hola VPN is a notable example because of its peer-to-peer design, which can effectively turn user devices into routing or exit nodes for other parties’ traffic, shifting the risk profile from that of a traditional virtual private network (VPN) to something closer to a distributed proxy.
As a result, the appearance of Hola-related activity, whether from prior installation or unintended background connections, should be treated with caution. Such activity may provide a foothold for malicious behavior, including lateral movement or command-and-control communication.
This blog explores how Hola-associated activity appeared as part of broader patterns of suspicious behavior observed across the Darktrace customer base.
The campaign
In February and March 2026, Darktrace observed similar anomalous activity across multiple customer environments, with affected devices showing consistent behavioral patterns. These included connections to multiple *.hola[.]org endpoints using Hola-related user agents, suggesting interaction with Hola infrastructure rather than isolated or incidental traffic.
Following these connections, affected customer environments showed downloads of suspicious executable files from rare external endpoints 188.241.219[.]55 and 184.241.218[.]111. Both endpoints have been flagged as potentially malicious by open-source intelligence (OSINT) [1][2].
These downloads were conducted using consistent user agents across impacted customers, specifically ‘Hola svc_js_win32/1.249.408’ and ‘Hola svc_js_win32/1.251.389’, suggesting a possible association with Hola-related activity.
Notably, this pattern aligns with recent reporting that, in some cases, Hola distributed an undeclared executable component, me[.]exe, which was later assessed to be a likely Monero-mining binary introduced via a compromised delivery pipeline [3].
Case Study 1
Darktrace first observed a new device on January 19, 2026, within a customer environment based in the Europe, Middle East, and Africa (EMEA) region. On the same day it appeared on the network, the device communicated with multiple pieces of Hola VPN-linked infrastructure before downloading a binary from a hola[.]org subdomain.
Figure 1: Cyber AI Analyst investigation highlighting Hola VPN service activity potentially associated with subsequent HTTP command-and-control (C2) connections.
Subsequent Darktrace telemetry revealed a recurring pattern of activity from the day the device was first observed through to March 4, 2026. During this period, the device repeatedly issued HTTP GET requests to the URI /bwfile?size=1048576, each returning a 200 OK response, indicating successful file retrieval.
This behavior was accompanied by a POST request to /bwfile, followed by an additional GET request for a significantly larger file at /bwfile?size=26214400, suggesting a deliberate and structured file transfer pattern.
Notably, the binary download activity was not tied to a single static host. Instead, it was observed across multiple URLs that changed over time while remaining within the same hola[.]org domain. This pattern suggests the use of rotating or distributed delivery infrastructure rather than a fixed endpoint.
Figure 2: Variation in URLs over time within the same hola[.]org domain, indicating the use of dynamically changing endpoints.
Across these events, the activity was consistently associated with the user agent Hola svc_js_win32/1.249.408, further linking the traffic to Hola-related service components. Amid these persistent and unusual connections, on February 22, Darktrace observed the device connecting to 188.241.219[.]55/proxy-peer-windows-amd64[.]exe, resulting in the download of an executable file.
Figure 3: File transfer event showing the download of an executable from the rare external endpoint 188.241.219[.]55.
Based on its file hash, the downloaded file was assessed as a likely Trojan downloader [4], with import hash (imphash) values showing similarities to samples linked to Vidar, Rhadamanthys, and Stealc according to OSINT [5]. Overall, this sequence of activity suggests that Hola-related connectivity may have been leveraged as part of a broader malware delivery chain.
Darktrace’s Autonomous Response
Due to the highly unusual activity observed, Darktrace Autonomous Response was triggered by the device’s behavior. However, as the customer deployment was configured in “Human Confirmation” mode, manual approval was required before any action could be taken.
Had the deployment been set to “Fully Autonomous” mode, Darktrace would have automatically:
Blocked connections to the associated ports and external endpoints
Prevented all outgoing network connections from the device
Enforced the device’s established ‘pattern of life’, allowing normal activity to continue while restricting any anomalous behavior
Figure 4: Example of a Darktrace Autonomous Response model highlighting the action that would have been taken, demonstrating how the system identifies anomalous behavior and applies targeted containment measures to restrict suspicious network activity.
Case Study 2
While the first case focused on anomalous activity from a newly observed device, Darktrace also identified cases in which devices had already been communicating with Hola-related endpoints prior to the suspected campaign. This may suggest pre-existing Hola usage within the environment, potentially increasing exposure and creating an avenue for subsequent suspicious activity.
One case involved three devices within a customer network based in the Americas (AMS). In this instance, a different payload was identified: me[.]exe, a potentially malicious cryptocurrency miner also referred to as HolaMonitorService[.]exe [6][7]. The downloads were observed from infrastructure similar to that seen in Case 1, including an IP address within the same 188.241.0.0/16 subnet.
Connections to *.hola[.]org, alongside the use of potential Hola-related user agents consistent with those in Case 1, were also identified, further suggesting a link between the observed activity and Hola-associated infrastructure.
Darktrace observed activity indicative of unusual VPN usage on the first affected device on February 2, followed by telemetry suggesting potential Tor usage. This was later followed by the download of me[.]exe on March 10 from 188.241.218[.]111. Notably, this device was the earliest among the three within the deployment to exhibit the presence of the suspicious executable.
Figure 5: Cyber AI Analyst detection highlighting the download of a suspicious executable from a similar external endpoint in a separate deployment.
On March 5, 2026, the second affected device exhibited a slightly different progression, initiating connections to http-test1[.]hola[.]org using the user agent ‘hola_get’. This activity was followed by the download of me[.]exe from the same endpoint on March 13, consistent with the broader pattern of Hola-related downloads observed across the environment.
Figure 6: Example of Hola VPN-related connectivity observed on the network prior to the suspected campaign, indicating pre-existing usage that may have contributed to subsequent activity.
The final affected device within this customer’s network demonstrated a more limited but related pattern, also downloading me[.]exe on March 17 using the same ‘hola_get’ user agent.
While the earlier Hola VPN usage observed across the deployment may not have been directly related to the suspected malware campaign, it may nonetheless have contributed to reduced visibility. The presence of pre-existing Hola-related traffic could have obscured malicious activity, making it more difficult to distinguish legitimate usage from attacker-driven behavior and, in turn, hindering the timely identification of the emerging compromise.
Darktrace’s Autonomous Response
For this deployment, the customer had their Autonomous Response capability configured in “Fully Autonomous” mode, allowing Darktrace to take action without human intervention. As a result, the system was able to autonomously disrupt the activity as soon as relevant events were identified through model detections.
Figure 7: Darktrace Autonomous Response actions taken against suspicious activity linked to Hola VPN.
Suspected cryptomining activity
As previously noted, some of the observed executable payloads appear to be linked to cryptomining malware. Across a subset of affected customer environments, this assessment was further supported by subsequent device activity consistent with Monero mining. Affected devices established follow-on connections to multiple external endpoints aligned with known mining infrastructure, indicating post-download execution.
Considering the broader sequence of activity, this pattern may point to a wider form of abuse in which legitimate VPN-related traffic is used to mask or facilitate malicious behavior following compromise.
On several devices, the download of executable files, including a newly observed peer[.]exe, was followed by alerts indicative of cryptocurrency mining activity. Mining-related credentials such as ‘x’ were observed using the Minergate protocol to communicate with endpoints within the 89.125.255.0/24 subnet and 188.241.218[.]111, the same endpoint involved in earlier download activity. Additional credentials appeared to reflect device-specific CPU identifiers, for example ‘12th Gen Intel(R) Core (TM) i5-1235U’.
Observed mining methods included login, submit, and job, consistent with active participation in a pool-based mining workflow rather than passive or incidental contact. The login method indicates that the host authenticated to the mining service as a worker, job reflects the assignment of computational tasks, and submit shows completed work being returned to the pool [8]. This sequence suggests that affected devices were actively contributing processing resources as part of an unauthorized distributed mining operation.
The presence of unauthorized cryptominers can lead to degraded system performance and reduced device stability. Beyond the immediate resource impact, such activity often serves as an indicator of a broader compromise rather than an isolated issue. This may increase the risk of further malware deployment, persistence mechanisms, and lateral movement, particularly in environments where the initial intrusion has not been fully contained.
Conclusion
Across affected environments, detections such as unusual VPN usage, connections to Hola infrastructure, anomalous HTTP activity, suspicious file downloads, and subsequent cryptomining behavior were linked into a single, evolving incident narrative. This aggregation provided a clearer view of attack progression, enabling security teams to understand not just isolated alerts, but the full sequence of compromise from initial contact through to post-exploitation.
Ultimately, these activities show that the risk posed by non-compliant software such as Hola VPN can extend far beyond simple policy violations. What began as traffic to Hola-related infrastructure was, in multiple cases, followed by behavior suggesting deliberate misuse, including suspicious executable downloads using Hola-related user agents and, in some instances, evidence of active cryptomining. These were not isolated anomalies, but elements of a broader pattern in which seemingly benign proxy or VPN-related communications may have created a pathway for malicious delivery and unauthorized resource exploitation.
The significance of this activity lies not only in the downloads or mining, but in what it reveals about an attacker’s ability to blend malicious operations into traffic associated with software that may already have a foothold in the environment. When unapproved software operates within an enterprise, it can reduce visibility, blur the distinction between legitimate and malicious traffic, and create opportunities to extend compromise in ways that are persistent and difficult to detect. Darktrace’s anomaly-based approach enables these behavioral distinctions to be identified, regardless of whether the device is new or long established within the network.
Credit to Min Kim (Associate Principal Analyst), Priya Thapa (Senior Cyber Analyst) Edited by Ryan Traill (Content Manager)
![Variation in URLs over time within the same hola[.]org domain, indicating the use of dynamically changing endpoints.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/6a304bdeff111215c0436931_Screenshot%202026-06-15%20at%2012.00.43%E2%80%AFPM.avif)
![File transfer event showing the download of an executable from the rare external endpoint 188.241.219[.]55.](https://cdn.prod.website-files.com/626ff4d25aca2edf4325ff97/6a304c2468d20aeff16a2721_Screenshot%202026-06-15%20at%2012.01.53%E2%80%AFPM.avif)
