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March 20, 2019

The Invisible Threat: How AI Catches the Ursnif Trojan

The cyber AI approach successfully detected the Ursnif infections even though the new variant of this malware was unknown to security vendors at the time.
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
Max Heinemeyer
Global Field CISO
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20
Mar 2019

Over the past few months, I’ve analyzed some of the world’s stealthiest trojan attacks like Emotet, which employ deception to bypass traditional security tools that rely on rules and signatures. Guest contributor Keith Siepel also explained how cyber AI defenses managed to catch a zero-day trojan on his firm’s network for which no such rules or signatures yet exist. Indeed, with the incidence of banking trojans having increased by 239% among our customer base last year, it appears that this kind of subterfuge is the new normal.

However, one particularly sophisticated trojan, Ursnif, takes deception a step further evidence of which we are still seeing emerge. Rather than writing executable files that contain malicious code, some of its variants instead exploit vulnerabilities inherent to a user’s own applications, essentially turning the victim’s computer against them. The result of this increasingly common technique is that — once the victim has been tricked into clicking a malicious link or duped into opening an attachment via a phishing email — Ursnif begins to ‘live off the land’, blending into the victim’s environment. And by exploiting Microsoft Office and Windows features, such as document macros, PsExec, and PowerShell scripts, Ursnif can execute commands directly from the computer’s RAM.

One of the most prevalent and destructive strains of the Gozi banking malware, Ursnif was recently placed at the center of a new campaign that saw it dramatically expand its functionality. Originally created to infect hosts with spyware in order to steal sensitive banking information and user credentials, it can now also deploy advanced ransomware like GandCrab. These new functions are aided by the elusive trojan’s aforementioned file-less capabilities, which render it invisible to many security tools and allow it to hide in plain sight within legitimate, albeit corrupted applications. Shining a light on Ursnif therefore requires AI tools that can learn to spot when these applications act abnormally:

Cyber AI detects Ursnif on multiple client networks

First campaign: February 4, 2019

Darktrace detected the initial Ursnif compromise on a customer’s network when it caught several devices connecting to a highly unusual endpoint and subsequently downloading masqueraded files, causing Darktrace’s “Anomalous File / Masqueraded File Transfer” model to breach. Such files are often masqueraded as other file types not only to bypass traditional security measures but also to deceive users — for instance, with the intention of tricking a user into executing a file received in a malicious email by disguising it as a document.

As it happens, this Ursnif variant was a zero-day at the time Darktrace detected it, meaning that its files were unknown to antivirus vendors. But while the never-before-seen files bypassed the customer’s endpoint tools, Darktrace AI leveraged its understanding of the unique ‘pattern of life’ for every user and device in the customer’s network to flag these file downloads as threatening anomalies — without relying on signatures.

A sample of the masqueraded files initially downloaded:

File: xtex13.gas
File MIME type: application/x-dosexec
Size: 549.38 KB
Connection UID: C8SlueG1mT7VdcJ00

File: zyteb17.gas
File MIME type: application/x-dosexec
SHA-1 hash: 4ed60393575d6b47bd82eeb03629bdcb8876a73f
Size: 276.48 KB

File: File: adnaz2.gas
File MIME type: application/x-dosexec
Size: 380.93 KB
Connection UID: CmPOzP1AC4tzuuuW00

A sample of the endpoints detected:

kieacsangelita[.]city · 209.141.60[.]214
muikarellep[.]band · 46.29.167[.]73
cjasminedison[.]com · 185.120.58[.]13

Following the initial suspicious downloads, the compromised devices were further observed making regular connections to multiple rare destinations not previously seen on the affected network in a pattern of beaconing connectivity. In some cases, Darktrace marked these external destinations as suspicious when it recognized the hostnames they queried as algorithm-generated domains. High volumes of DNS requests for such domains is a common characteristic of malware infections, which use this tactic to maintain communication with C2 servers in spite of domain black-listing. In other cases, the endpoints were deemed suspicious because of their use of self-signed SSL certificates, which cyber-criminals often use because they do not require verification by a trusted authority.

In fact, the large volume of anomalous connections commonly triggered a number of Darktrace’s behavioral models, including:

Compromise / DGA Beacon
Anomalous Connection / Suspicious Self-Signed SSL
Compromise / High Volume of Connections with Beacon Score
Compromise / Beaconing Activity To Rare External Endpoint

Beaconing is a method of communication frequently seen when a compromised device attempts to relay information to its control infrastructure in order to receive further instructions. This behavior is characterized by persistent external connections to one or multiple endpoints, a pattern that was repeatedly observed for those devices that had previously downloaded malicious files from the endpoints later associated with the Ursnif campaign. While beaconing behavior to unusual destinations is not necessarily always indicative of infection, Darktrace AI concluded that, in combination with the suspicious file downloads, this type of activity represented a clear indication of compromise.

Figure 1: A device event log that shows the device had connected to internal mail servers shortly before downloading the malicious files.

Lateral movement and file-less capabilities

In the wake of the initial compromise, Darktrace AI also detected Ursnif’s lateral movement and file-less capabilities in real time. In the case of one infected device, an “Anomalous Connection / High Volume of New Service Control” model breach was triggered following the aforementioned suspicious activities. The device in question was flagged after making anomalous SMB connections to at least 47 other internal devices, and after accessing file shares which it had not previously connected. Subsequently, the device was observed writing to the other devices’ service control pipe – a channel used for the remote control of services. The anomalous use of these remote-control channels represent compelling examples of how Ursnif leverages its file-less capabilities to facilitate lateral movement.

Figure 2: Volume of SMB writes made to the service control pipe on internal devices by one of the infected devices, as shown on the Darktrace UI.

Although network administrators often use remote control channels for legitimate purposes, Darktrace AI considered this particular usage highly suspicious, particularly as both devices had previously breached a number of behavioral models as a result of infection.

Second campaign: March 18, 2019

A second Ursnif campaign was detected just this week. At the time of detection, no OSINT was available for the C2 servers nor the malware samples.

On a US manufacturer’s network, the initial malware download took place from: xqzuua1594[.]com/loq91/10x.php?l=mow1.jad hosted on IP 94.154.10[.]62.
Every single malware download is unique. This is indicating auto-patching or a malware factory working in the background.
Darktrace immediately identified this as another Anomalous File / Masqueraded File Transfer.

Directly after this, initial C2 was observed with the following parameters:

HTTP GET to: vwdlpknpsierra[.]email
Destination IP: 162.248.225[.]14
URI: /images/CKicJCsNNNfaJwX6CJ/0Ohp3OUfj/pI_2FszUK7ybqh33Qdwz/bOUeatCG2Qfks5DTzzO/H6SeiL8YozEYXKfornjfVt/hBgfcPVPCOf1H/2qo12IGl/L3B18ld4ZSx37TbdTUpALih/A5dl8FVHel/jMPIKnQfd/H.avi
User Agent: Mozilla/5.0 (Windows NT 10.0; WOW64; Trident/7.0; rv:11.0) like Gecko

What’s interesting here is that the C2 server provides a Sufee Admin login page:

This C2 appears to have bad operational security (OPSEC) as browsing random URIs on the server reveals some of the dashboard’s contents:

The initial C2 communication was followed by sustained TCP beaconing to ksylviauudaren[.]band on 185.180.198[.]245 over port 443 with SSL encryption using a self-signed certificate. Darktrace highlighted this C2 behavior as Compromise / Sustained TCP Beaconing Activity To Rare Endpoint and Anomalous Connection / Repeated Rare External SSL Self-Signed IP.

As of the writing of this article, the domain ksylviauudaren[.]band was still not recognized in OSINT as malicious – highlighting again Darktrace’s independence of signatures and rules to catch previously unknown threats.

Conclusion

The cyber AI approach successfully detected the Ursnif infections even though the new variant of this malware was unknown to security vendors at the time. Moreover, it even managed to catch Ursnif’s file-less capabilities for lateral movement through its modelling of expected patterns of connectivity. In terms of the wider security context, the ease with which cyber AI flagged such sophisticated malware — malware which takes action by corrupting a computer’s own applications — further demonstrates that AI anomaly detection is the only way to navigate a threat landscape increasingly populated by near-invisible trojans.

IoCs

kieacsangelita[.]city · 209.141.60[.]214
muikarellep[.]band · 46.29.167[.]73
cjasminedison[.]com · 185.120.58[.]13
xqzuua1594[.]com · 94.154.10.[6]2
vwdlpknpsierra[.]email · 162.248.225[.]14
ksylviauudaren[.]band · 185.180.198[.]245

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
Max Heinemeyer
Global Field CISO

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January 30, 2026

ClearFake: From Fake CAPTCHAs to Blockchain-Driven Payload Retrieval

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What is ClearFake?

As threat actors evolve their techniques to exploit victims and breach target networks, the ClearFake campaign has emerged as a significant illustration of this continued adaptation. ClearFake is a campaign observed using a malicious JavaScript framework deployed on compromised websites, impacting sectors such as e‑commerce, travel, and automotive. First identified in mid‑2023, ClearFake is frequently leveraged to socially engineer victims into installing fake web browser updates.

In ClearFake compromises, victims are steered toward compromised WordPress sites, often positioned by attackers through search engine optimization (SEO) poisoning. Once on the site, users are presented with a fake CAPTCHA. This counterfeit challenge is designed to appear legitimate while enabling the execution of malicious code. When a victim interacts with the CAPTCHA, a PowerShell command containing a download string is retrieved and executed.

Attackers commonly abuse the legitimate Microsoft HTML Application Host (MSHTA) in these operations. Recent campaigns have also incorporated Smart Chain endpoints, such as “bsc-dataseed.binance[.]org,” to obtain configuration code. The primary payload delivered through ClearFake is typically an information stealer, such as Lumma Stealer, enabling credential theft, data exfiltration, and persistent access [1].

Darktrace’s Coverage of ClearFake

Darktrace / ENDPOINT first detected activity likely associated with ClearFake on a single device on over the course of one day on November 18, 2025. The system observed the execution of “mshta.exe,” the legitimate Microsoft HTML Application Host utility. It also noted a repeated process command referencing “weiss.neighb0rrol1[.]ru”, indicating suspicious external activity. Subsequent analysis of this endpoint using open‑source intelligence (OSINT) indicated that it was a malicious, domain generation algorithm (DGA) endpoint [2].

The process line referencing weiss.neighb0rrol1[.]ru, as observed by Darktrace / ENDPOINT.
Figure 1: The process line referencing weiss.neighb0rrol1[.]ru, as observed by Darktrace / ENDPOINT.

This activity indicates that mshta.exe was used to contact a remote server, “weiss.neighb0rrol1[.]ru/rpxacc64mshta,” and execute the associated HTA file to initiate the next stage of the attack. OSINT sources have since heavily flagged this server as potentially malicious [3].

The first argument in this process uses the MSHTA utility to execute the HTA file hosted on the remote server. If successful, MSHTA would then run JavaScript or VBScript to launch PowerShell commands used to retrieve malicious payloads, a technique observed in previous ClearFake campaigns. Darktrace also detected unusual activity involving additional Microsoft executables, including “winlogon.exe,” “userinit.exe,” and “explorer.exe.” Although these binaries are legitimate components of the Windows operating system, threat actors can abuse their normal behavior within the Windows login sequence to gain control over user sessions, similar to the misuse of mshta.exe.

EtherHiding cover

Darktrace also identified additional ClearFake‑related activity, specifically a connection to bsc-testnet.drpc[.]org, a legitimate BNB Smart Chain endpoint. This activity was triggered by injected JavaScript on the compromised site www.allstarsuae[.]com, where the script initiated an eth_call POST request to the Smart Chain endpoint.

Example of a fake CAPTCHA on the compromised site www.allstarsuae[.]com.
Figure 2: Example of a fake CAPTCHA on the compromised site www.allstarsuae[.]com.

EtherHiding is a technique in which threat actors leverage blockchain technology, specifically smart contracts, as part of their malicious infrastructure. Because blockchain is anonymous, decentralized, and highly persistent, it provides threat actors with advantages in evading defensive measures and traditional tracking [4].

In this case, when a user visits a compromised WordPress site, injected base64‑encoded JavaScript retrieved an ABI string, which was then used to load and execute a contract hosted on the BNB Smart Chain.

JavaScript hosted on the compromised site www.allstaruae[.]com.
Figure 3: JavaScript hosted on the compromised site www.allstaruae[.]com.

Conducting malware analysis on this instance, the Base64 decoded into a JavaScript loader. A POST request to bsc-testnet.drpc[.]org was then used to retrieve a hex‑encoded ABI string that loads and executes the contract. The JavaScript also contained hex and Base64‑encoded functions that decoded into additional JavaScript, which attempted to retrieve a payload hosted on GitHub at “github[.]com/PrivateC0de/obf/main/payload.txt.” However, this payload was unavailable at the time of analysis.

Darktrace’s detection of the POST request to bsc-testnet.drpc[.]org.
Figure 4: Darktrace’s detection of the POST request to bsc-testnet.drpc[.]org.
Figure 5: Darktrace’s detection of the executable file and the malicious hostname.

Autonomous Response

As Darktrace’s Autonomous Response capability was enabled on this customer’s network, Darktrace was able to take swift mitigative action to contain the ClearFake‑related activity early, before it could lead to potential payload delivery. The affected device was blocked from making external connections to a number of suspicious endpoints, including 188.114.96[.]6, *.neighb0rrol1[.]ru, and neighb0rrol1[.]ru, ensuring that no further malicious connections could be made and no payloads could be retrieved.

Autonomous Response also acted to prevent the executable mshta.exe from initiating HTA file execution over HTTPS from this endpoint by blocking the attempted connections. Had these files executed successfully, the attack would likely have resulted in the retrieval of an information stealer, such as Lumma Stealer.

Autonomous Response’s intervention against the suspicious connectivity observed.
Figure 6: Autonomous Response’s intervention against the suspicious connectivity observed.

Conclusion

ClearFake continues to be observed across multiple sectors, but Darktrace remains well‑positioned to counter such threats. Because ClearFake’s end goal is often to deliver malware such as information stealers and malware loaders, early disruption is critical to preventing compromise. Users should remain aware of this activity and vigilant regarding fake CAPTCHA pop‑ups. They should also monitor unusual usage of MSHTA and outbound connections to domains that mimic formats such as “bsc-dataseed.binance[.]org” [1].

In this case, Darktrace was able to contain the attack before it could successfully escalate and execute. The attempted execution of HTA files was detected early, allowing Autonomous Response to intervene, stopping the activity from progressing. As soon as the device began communicating with weiss.neighb0rrol1[.]ru, an Autonomous Response inhibitor triggered and interrupted the connections.

As ClearFake continues to rise, users should stay alert to social engineering techniques, including ClickFix, that rely on deceptive security prompts.

Credit to Vivek Rajan (Senior Cyber Analyst) and Tara Gould (Malware Research Lead)

Edited by Ryan Traill (Analyst Content Lead)

Appendices

Darktrace Model Detections

Process / New Executable Launched

Endpoint / Anomalous Use of Scripting Process

Endpoint / New Suspicious Executable Launched

Endpoint / Process Connection::Unusual Connection from New Process

Autonomous Response Models

Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block

List of Indicators of Compromise (IoCs)

  • weiss.neighb0rrol1[.]ru – URL - Malicious Domain
  • 188.114.96[.]6 – IP – Suspicious Domain
  • *.neighb0rrol1[.]ru – URL – Malicious Domain

MITRE Tactics

Initial Access, Drive-by Compromise, T1189

User Execution, Execution, T1204

Software Deployment Tools, Execution and Lateral Movement, T1072

Command and Scripting Interpreter, T1059

System Binary Proxy Execution: MSHTA, T1218.005

References

1.        https://www.kroll.com/en/publications/cyber/rapid-evolution-of-clearfake-delivery

2.        https://www.virustotal.com/gui/domain/weiss.neighb0rrol1.ru

3.        https://www.virustotal.com/gui/file/1f1aabe87e5e93a8fff769bf3614dd559c51c80fc045e11868f3843d9a004d1e/community

4.        https://www.packetlabs.net/posts/etherhiding-a-new-tactic-for-hiding-malware-on-the-blockchain/

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Vivek Rajan
Cyber Analyst

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January 30, 2026

The State of Cybersecurity in the Finance Sector: Six Trends to Watch

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The evolving cybersecurity threat landscape in finance

The financial sector, encompassing commercial banks, credit unions, financial services providers, and cryptocurrency platforms, faces an increasingly complex and aggressive cyber threat landscape. The financial sector’s reliance on digital infrastructure and its role in managing high-value transactions make it a prime target for both financially motivated and state-sponsored threat actors.

Darktrace’s latest threat research, The State of Cybersecurity in the Finance Sector, draws on a combination of Darktrace telemetry data from real-world customer environments, open-source intelligence, and direct interviews with financial-sector CISOs to provide perspective on how attacks are unfolding and how defenders in the sector need to adapt.  

Six cybersecurity trends in the finance sector for 2026

1. Credential-driven attacks are surging

Phishing continues to be a leading initial access vector for attacks targeting confidentiality. Financial institutions are frequently targeted with phishing emails designed to harvest login credentials. Techniques including Adversary-in-The-Middle (AiTM) to bypass Multi-factor Authentication (MFA) and QR code phishing (“quishing”) are surging and are capable of fooling even trained users. In the first half of 2025, Darktrace observed 2.4 million phishing emails within financial sector customer deployments, with almost 30% targeted towards VIP users.  

2. Data Loss Prevention is an increasing challenge

Compliance issues – particularly data loss prevention -- remain a persistent risk. In October 2025 alone, Darktrace observed over 214,000 emails across financial sector customers that contained unfamiliar attachments and were sent to suspected personal email addresses highlighting clear concerns around data loss prevention. Across the same set of customers within the same time frame, more than 351,000 emails containing unfamiliar attachments were sent to freemail addresses (e.g. gmail, yahoo, icloud), highlighting clear concerns around DLP.  

Confidentiality remains a primary concern for financial institutions as attackers increasingly target sensitive customer data, financial records, and internal communications.  

3. Ransomware is evolving toward data theft and extortion

Ransomware is no longer just about locking systems, it’s about stealing data first and encrypting second. Groups such as Cl0p and RansomHub now prioritize exploiting trusted file-transfer platforms to exfiltrate sensitive data before encryption, maximizing regulatory and reputational fallout for victims.  

Darktrace’s threat research identified routine scanning and malicious activity targeting internet-facing file-transfer systems used heavily by financial institutions. In one notable case involving Fortra GoAnywhere MFT, Darktrace detected malicious exploitation behavior six days before the CVE was publicly disclosed, demonstrating how attackers often operate ahead of patch cycles

This evolution underscores a critical reality: by the time a vulnerability is disclosed publicly, it may already be actively exploited.

4. Attackers are exploiting edge devices, often pre-disclosure.  

VPNs, firewalls, and remote access gateways have become high-value targets, and attackers are increasingly exploiting them before vulnerabilities are publicly disclosed. Darktrace observed pre-CVE exploitation activity affecting edge technologies including Citrix, Palo Alto, and Ivanti, enabling session hijacking, credential harvesting, and privileged lateral movement into core banking systems.  

Once compromised, these edge devices allow adversaries to blend into trusted network traffic, bypassing traditional perimeter defenses. CISOs interviewed for the report repeatedly described VPN infrastructure as a “concentrated focal point” for attackers, especially when patching and segmentation lag behind operational demands.

5. DPRK-linked activity is growing across crypto and fintech.  

State-sponsored activity, particularly from DPRK-linked groups affiliated with Lazarus, continues to intensify across cryptocurrency and fintech organizations. Darktrace identified coordinated campaigns leveraging malicious npm packages, previously undocumented BeaverTail and InvisibleFerret malware, and exploitation of React2Shell (CVE-2025-55182) for credential theft and persistent backdoor access.  

Targeting was observed across the United Kingdom, Spain, Portugal, Sweden, Chile, Nigeria, Kenya, and Qatar, highlighting the global scope of these operations.  

6. Cloud complexity and AI governance gaps are now systemic risks.  

Finally, CISOs consistently pointed to cloud complexity, insider risk from new hires, and ungoverned AI usage exposing sensitive data as systemic challenges. Leaders emphasized difficulty maintaining visibility across multi-cloud environments while managing sensitive data exposure through emerging AI tools.  

Rapid AI adoption without clear guardrails has introduced new confidentiality and compliance risks, turning governance into a board-level concern rather than a purely technical one.

Building cyber resilience in a shifting threat landscape

The financial sector remains a prime target for both financially motivated and state-sponsored adversaries. What this research makes clear is that yesterday’s security assumptions no longer hold. Identity attacks, pre-disclosure exploitation, and data-first ransomware require adaptive, behavior-based defenses that can detect threats as they emerge, often ahead of public disclosure.

As financial institutions continue to digitize, resilience will depend on visibility across identity, edge, cloud, and data, combined with AI-driven defense that learns at machine speed.  

Learn more about the threats facing the finance sector, and what your organization can do to keep up in The State of Cybersecurity in the Finance Sector report here.  

Acknowledgements:

The State of Cybersecurity in the Finance sector report was authored by Calum Hall, Hugh Turnbull, Parvatha Ananthakannan, Tiana Kelly, and Vivek Rajan, with contributions from Emma Foulger, Nicole Wong, Ryan Traill, Tara Gould, and the Darktrace Threat Research and Incident Management teams.

[related-resource]  

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