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October 11, 2017

Stealth Attacks: The ‘Matrix Banker’ Reloaded

Over the last few weeks, Darktrace has confidently identified traces of the resurgence of a stealthy attack targeting Latin American companies. Learn more!
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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11
Oct 2017

Overview

Over the last few weeks, Darktrace has confidently identified traces of the resurgence of a stealthy attack targeting Latin American companies. This targeted campaign was first observed between March and June this year. Arbor Networks initially labelled the malware used in the campaign ‘Matrix Banker’. The name used by Proofpoint is ‘Win32/RediModiUpd’. The malware used by the attackers appeared to be still under development when the last report came out in June 2017.

Darktrace has observed an attack wave targeting Mexican companies in August and September 2017. Some of the TTPs (tools, techniques, procedures) observed bear close resemblance to those seen in the ‘Matrix Banker’ attacks earlier this year. The campaign is crafted to be particularly stealthy and to blend into certain networks in Latin America, confirming the suspicion of its targeted nature. Darktrace’s machine learning and AI algorithms were able to identify the infected devices almost instantaneously, despite apparent efforts by the malware author to be covert and stealthy.

Between August and October 2017, Darktrace detected highly anomalous behavior on five seemingly unrelated networks in Mexico. Unlike the original strain of this attack, which was believed to target financial institutions almost exclusively, this latest variant affected customers across a number of industry verticals, suggesting that the threat actors are diversifying their targets. Darktrace has seen the attack hit companies in the healthcare, telecommunications, food and retail sectors.

Infection process

The initial infection vector appears to be phishing emails. The users downloaded the initial piece of malware from compromised Mexican websites. The infected files were Windows executables masqueraded as .mp3 and .gif files. Example downloads are listed below. Darktrace instantly detected the highly anomalous behavior of these downloads, which occurred from 100% rare external domains for the networks, and alerted the respective security teams.

hxxp://gorrasbaratas.com[.]mx/images/sss/sound.mp3 [1]
hxxp://inseltech.com[.]mx/inicio/wp-includes/kk/sound.mp3 [2]

The actual file names of the downloads are ‘logo.gif’.

The ‘Matrix Bankers’ attack tried to conceal malware downloads using masqueraded files in previous attacks. What is interesting about the hacked websites serving the malware is that they are using the .mx top level domain. This localised and targeted technique is used to conceal the traffic and make it blend in with normal network traffic on networks in Mexico.

Following the initial infection, in some cases a second stage malware was downloaded. Darktrace detected this as more anomalous activity since the downloads took place from more 100% rare external destinations:

hxxp://dackdack[.]club/APIv3/modules/nn_grabber_x64.dll [3]
hxxp://dackdack[.]club/APIv3/modules/nn_grabber_x32.dll [4]

Successful second stage downloads were seen to be followed by suspicious HTTP POST beaconing behavior, resembling command and control communication to various domains:

hxxp://kuxkux[.]bit/APIv3/api.php
hxxp://drdrfdd[.]cat/forum/logout.php
hxxp://eaxsess[.]cat/forum/logout.php

Not all targeted companies were seen to receive a second-stage malware download. This might indicate a sophisticated attack plan where the initial generic, covert backdoor is followed by a targeted second-stage payload that is chosen based on the victim and its potential value to the cyber criminals (long term data exfiltration, ransomware, banking Trojan…). Customers reported that infected devices had their anti-virus disabled, or removed by the malware. This showcases that companies cannot solely rely on signature based systems to catch novel, evolving threats.

The beaconing behavior to these 100% unusual external domains was immediately detected as it represented a strong deviation from the devices’ normal ‘pattern of life’. The use of domains hosted on .cat (top level domain used for the Catalan culture and language) indicates that the attackers are highly aware of the cultural context of their target victims and try to make the malware communication blend in with network traffic.

Compromised machines made further repeated DNS requests to the domains below:

dackdack[.]tech
dackdack[.]online
kuykuy[.]bit

At the time of our investigation, the domains below resolved to the following IP address:

142.44.188[.]42
dackdack[.]club
eaxsess[.]cat
kuxkux[.]bit
drdrfdd[.]cat

Closing thoughts

Although final attribution is impossible, the evidence strongly suggests that the campaign described here is similar to the ‘Matrix Banker’ campaign observed in March and June 2017 and might be a continuation of it.

The initial malware was concealing its file types by using different file extensions than their MIME type. More precisely, the use of ‘logo.gif’ has been seen in previous ‘Matrix Banker’ attacks.

There are 3,000 deployments of Darktrace’s AI technology across 70 countries, but all identified instances of this type of compromise are in Latin American organizations.

The ‘Matrix Bankers’ have used Catalan top-level domains in past attacks. In fact, some of the domains used previously are very similar to domains observed here. One domain seen in September was the exact same domain as seen in an earlier attack – just with an additional ‘s’ appended:

Example domains from March/June 2017

trtr44[.]cat
lalax[.]cat
eaxses[.]cat

Example domains from August/October 2017

drdrfdd[.]cat
kuxkux[.]bit
eaxsess[.]cat
kuykuy[.]bit
dackdack[.]tech

Although the domains appear to be randomly generated, a closer look reveals that the ‘Matrix Bankers’ seem to favor generating domain names by using keys that are physically close together on a keyboard, or by repeating phrases one might type in a hurry, when lacking creativity for naming a temporary download (e.g. asdasd.jpeg). We saw this pattern for domain name generation in the March - June ‘Matrix Bankers’ campaign as well as here.

Darktrace’s AI technology was able to detect these stealthy and sophisticated attacks because the way in which they manifest themselves represents a sharp deviation from the normal ‘pattern of life’ within an organization. The threat actors applied a number of techniques to blend into the normal noise of networks, but the self-learning algorithms were quick in detecting the anomalous behavior automatically and in real time.

Footnotes

List of IoCs

dackdack[.]club
dackdack[.]tech
dackdack[.]online
eaxsess[.]cat
kuxkux[.]bit
kuykuy[.]bit
drdrfdd[.]cat
inseltech.com[.]mx
gorrasbaratas.com[.]mx
142.44.188[.]42

[1] VirusTotal analysis of this file
[2] SHA-1: 88f3bdc84908c1fb844b337c535eef2d2b31e1dc
[3] VirusTotal analysis of this file
[4] VirusTotal analysis of this file

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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April 21, 2026

How a Compromised eScan Update Enabled Multi‑Stage Malware and Blockchain C2

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The rise of supply chain attacks

In recent years, the abuse of trusted software has become increasingly common, with supply chain compromises emerging as one of the fastest growing vectors for cyber intrusions. As highlighted in Darktrace’s Annual Threat Report 2026, attackers and state-actors continue to find significant value in gaining access to networks through compromised trusted links, third-party tools, or legitimate software. In January 2026, a supply chain compromise affecting MicroWorld Technologies’ eScan antivirus product was reported, with malicious updates distributed to customers through the legitimate update infrastructure. This, in turn, resulted in a multi‑stage loader malware being deployed on compromised devices [1][2].

An overview of eScan exploitation

According to eScan’s official threat advisory, unauthorized access to a regional update server resulted in an “incorrect file placed in the update distribution path” [3]. Customers associated with the affected update servers who downloaded the update during a two-hour window on January 20 were impacted, with affected Windows devices subsequently have experiencing various errors related to update functions and notifications [3].

While eScan did not specify which regional update servers were affected by the malicious update, all impacted Darktrace customer environments were located in the Europe, Middle East, and Africa (EMEA) region.

External research reported that a malicious 32-bit executable file , “Reload.exe”, was first installed on affected devices, which then dropped the 64-bit downloader, “CONSCTLX.exe”. This downloader establishes persistence by creating scheduled tasks such as “CorelDefrag”, which are responsible for executing PowerShell scripts. Subsequently, it evades detection by tampering with the Windows HOSTS file and eScan registry to prevent future remote updates intended for remediation. Additional payloads are then downloaded from its command-and-control (C2) server [1].

Darktrace’s coverage of eScan exploitation

Initial Access and Blockchain as multi-distributed C2 Infrastructure

On January 20, the same day as the aforementioned two‑hour exploit window, Darktrace observed multiple devices across affected networks downloading .dlz package files from eScan update servers, followed by connections to an anomalous endpoint, vhs.delrosal[.]net, which belongs to the attackers’ C2 infrastructure.

The endpoint contained a self‑signed SSL certificate with the string “O=Internet Widgits Pty Ltd, ST=SomeState, C=AU”, a default placeholder commonly used in SSL/TLS certificates for testing and development environments, as well as in malicious C2 infrastructure [4].

Utilizing a multi‑distributed C2 infrastructure, the attackers also leveraged domains linked with the Solana open‑source blockchain for C2 purposes, namely “.sol”. These domains were human‑readable names that act as aliases for cryptocurrency wallet addresses. As browsers do not natively resolve .sol domains, the Solana Naming System (formerly known as Bonfida, an independent contributor within the Solana ecosystem) provides a proxy service, through endpoints such as sol-domain[.]org, to enable browser access.

Darktrace observed devices connecting to blackice.sol-domain[.]org, indicating that attackers were likely using this proxy to reach a .sol domain for C2 activity. Given this behavior, it is likely that the attackers leveraged .sol domains as a dead drop resolver, a C2 technique in which threat actors host information on a public and legitimate service, such as a blockchain. Additional proxy resolver endpoints, such as sns-resolver.bonfida.workers[.]dev, were also observed.

Solana transactions are transparent, allowing all activity to be viewed publicly. When Darktrace analysts examined the transactions associated with blackice[.]sol, they observed that the earliest records dated November 7, 2025, which coincides with the creation date of the known C2 endpoint vhs[.]delrosal[.]net as shown in WHOIS Lookup information [4][5].

WHOIS Look records of the C2 endpoint vhs[.]delrosal[.]net.
Figure 1: WHOIS Look records of the C2 endpoint vhs[.]delrosal[.]net.
 Earliest observed transaction record for blackice[.]sol on public ledgers.
Figure 2: Earliest observed transaction record for blackice[.]sol on public ledgers.

Subsequent instructions found within the transactions contained strings such as “CNAME= vhs[.]delrosal[.]net”, indicating attempts to direct the device toward the malicious endpoint. A more recent transaction recorded on January 28 included strings such as “hxxps://96.9.125[.]243/i;code=302”, suggesting an effort to change C2 endpoints. Darktrace observed multiple alerts triggered for these endpoints across affected devices.

Similar blockchain‑related endpoints, such as “tumama.hns[.]to”, were also observed in C2 activities. The hns[.]to service allows web browsers to access websites registered on Handshake, a decentralized blockchain‑based framework designed to replace centralized authorities and domain registries for top‑level domains. This shift toward decentralized, blockchain‑based infrastructure likely reflects increased efforts by attackers to evade detection.

In outgoing connections to these malicious endpoints across affected networks, Darktrace / NETWORK recognized that the activity was 100% rare and anomalous for both the devices and the wider networks, likely indicative of malicious beaconing, regardless of the underlying trusted infrastructure. In addition to generating multiple model alerts to capture this malicious activity across affected networks, Darktrace’s Cyber AI Analyst was able to compile these separate events into broader incidents that summarized the entire attack chain, allowing customers’ security teams to investigate and remediate more efficiently. Moreover, in customer environments where Darktrace’s Autonomous Response capability was enabled, Darktrace took swift action to contain the attack by blocking beaconing connections to the malicious endpoints, even when those endpoints were associated with seemingly trustworthy services.

Conclusion

Attacks targeting trusted relationships continue to be a popular strategy among threat actors. Activities linked to trusted or widely deployed software are often unintentionally whitelisted by existing security solutions and gateways. Darktrace observed multiple devices becoming impacted within a very short period, likely because tools such as antivirus software are typically mass‑deployed across numerous endpoints. As a result, a single compromised delivery mechanism can greatly expand the attack surface.

Attackers are also becoming increasingly creative in developing resilient C2 infrastructure and exploiting legitimate services to evade detection. Defenders are therefore encouraged to closely monitor anomalous connections and file downloads. Darktrace’s ability to detect unusual activity amidst ever‑changing tactics and indicators of compromise (IoCs) helps organizations maintain a proactive and resilient defense posture against emerging threats.

Credit to Joanna Ng (Associate Principal Cybersecurity Analyst) and Min Kim (Associate Principal Cybersecurity Analyst) and Tara Gould (Malware Researcher Lead)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

  • Anomalous File::Zip or Gzip from Rare External Location
  • Anomalous Connection / Suspicious Self-Signed SSL
  • Anomalous Connection / Rare External SSL Self-Signed
  • Anomalous Connection / Suspicious Expired SSL
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device

List of Indicators of Compromise (IoCs)

  • vhs[.]delrosal[.]net – C2 server
  • tumama[.]hns[.]to – C2 server
  • blackice.sol-domain[.]org – C2 server
  • 96.9.125[.]243 – C2 Server

MITRE ATT&CK Mapping

  • T1071.001 - Command and Control: Web Protocols
  • T1588.001 - Resource Development
  • T1102.001 - Web Service: Dead Drop Resolver
  • T1195 – Supple Chain Compromise

References

[1] https://www.morphisec.com/blog/critical-escan-threat-bulletin/

[2] https://www.bleepingcomputer.com/news/security/escan-confirms-update-server-breached-to-push-malicious-update/

[3] hxxps://download1.mwti.net/documents/Advisory/eScan_Security_Advisory_2026[.]pdf

[4] https://www.virustotal.com/gui/domain/delrosal.net

[5] hxxps://explorer.solana[.]com/address/2wFAbYHNw4ewBHBJzmDgDhCXYoFjJnpbdmeWjZvevaVv

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About the author
Joanna Ng
Associate Principal Analyst

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April 17, 2026

Why Behavioral AI Is the Answer to Mythos

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How AI is breaking the patch-and-prevent security model

The business world was upended last week by the news that Anthropic has developed a powerful new AI model, Claude Mythos, which poses unprecedented risk because of its ability to expose flaws in IT systems.  

Whether it’s Mythos or OpenAI’s GPT-5.4-Cyber, which was just announced on Tuesday, supercharged AI models in the hands of hackers will allow them to carry out attacks at machine speed, much faster than most businesses can stop them.  

This news underscores a stark reality for all leaders: Patching holes alone is not a sufficient control against modern cyberattacks. You must assume that your software is already vulnerable right now. And while LLMs are very good at spotting vulnerabilities, they’re pretty bad at reliably patching them.

Project Glasswing members say it could take months or years for patches to be applied. While that work is done, enterprises must be protected against Zero-Day attacks, or security holes that are still undiscovered.  

Most cybersecurity strategies today are built like a daily multivitamin: broad, preventative, and designed to keep the system generally healthy over time. Patch regularly. Update software. Reduce known vulnerabilities. It’s necessary, disciplined, and foundational. But it’s also built for a world where the risks are well known and defined, cycles are predictable, and exposure unfolds at a manageable pace.

What happens when that model no longer holds?

The AI cyber advantage: Behavioral AI

The vulnerabilities exposed by AI systems like Mythos aren’t the well-understood risks your “multivitamin” was designed to address. They are transient, fast-emerging entry points that exist just long enough to be exploited.

In that environment, prevention alone isn’t enough. You don’t need more vitamins—you need a painkiller. The future of cybersecurity won’t be defined by how well you maintain baseline health. It will be defined by how quickly you respond when something breaks and every second counts.

That’s why behavioral AI gives businesses a durable cyber advantage. Rather than trying to figure out what the attacker looks like, it learns what “normal” looks like across the digital ecosystem of each individual business.  

That’s exactly how behavioral AI works. It understands the self, or what's normal for the organization, and then it can spot deviations in from normal that are actually early-stage attacks.

The Darktrace approach to cybersecurity

At Darktrace, we’ve been defending our 10,000 customers using behavioral AI cybersecurity developed in our AI Research Centre in Cambridge, U.K.

Darktrace was built on the understanding that attacks do not arrive neatly labeled, and that the most damaging threats often emerge before signatures, indicators, or public disclosures can catch up.  

Our AI algorithms learn in real time from your personalized business data to learn what’s normal for every person and every asset, and the flows of data within your organization. By continuously understanding “normal” across your entire digital ecosystem, Darktrace identifies and contains threats emerging from unknown vulnerabilities and compromised supply chain dependencies, autonomously curtailing attacks at machine speed.  

Security for novel threats

Darktrace is built for a world where AI is not just accelerating attacks, but fundamentally reshaping how they originate. What makes our AI so unique is that it's proven time and again to identify cyber threats before public vulnerability disclosures, such as critical Ivanti vulnerabilities in 2025 and SAP NetWeaver exploitations tied to nation-state threat actors.  

As AI reshapes how vulnerabilities are found and exploited, cybersecurity must be anchored in something more durable than a list of known flaws. It requires a real-time understanding of the business itself: what belongs, what does not, and what must be stopped immediately.

What leaders should do right now

The leadership priority must shift accordingly.

First, stop treating unknown vulnerabilities as an edge case. AI‑driven discovery makes them the norm. Security programs built primarily around known flaws, signatures, and threat intelligence will always lag behind an attacker that is operating in real time.

Second, insist on an understanding of what is actually normal across the business. When threats are novel, labels are useless. The earliest and most reliable signal of danger is abnormal behavior—systems, users, or data flows that suddenly depart from what is expected. If you cannot see that deviation as it happens, you are effectively blind during the most critical window.

Finally, assume that the next serious incident will occur before remediation guidance is available. Ask what happens in those first minutes and hours. The organizations that maintain resilience are not the ones waiting for disclosure cycles to catch up—they are the ones that can autonomously identify and contain emerging threats as they unfold.

This is the reality of cybersecurity in an AI‑shaped world. Patching and prevention remain important foundations, but the advantage now belongs to those who can respond instantly when the unpredictable occurs.

Behavioral AI is security designed not just for known threats, but for the ones that AI will discover next.

[related-resource]

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About the author
Ed Jennings
President and CEO
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