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October 13, 2023

Protecting Brazilian Organizations from Malware

Discover how Darktrace DETECT thwarted a banking trojan targeting Brazilian organizations, preventing data theft and informing the customer.
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
Roberto Romeu
Senior SOC Analyst
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13
Oct 2023

Nationally Targeted Cyber Attacks

As the digital world becomes more and more interconnected, the threat of cyber-attacks transcends borders and presents a significant concern to security teams worldwide. Yet despite this, some malicious actors have shown a tendency to focus their attacks on specific countries. By employing highly tailored tactics, techniques, and procedures (TTPs) to target users and organizations from one nation, rather than launching more widespread campaigns, threat actors are able to maximize the efficiency and efficacy of their attacks.

What is Guildma and how does it work?

One example can be seen in the remote access trojan (RAT) and information stealer, Guildma. Guildma, also known by the demonic moniker, Astaroth, first appeared in the wild in 2017 and is a Latin America-based banking trojan known to primarily target organizations in Brazil, although has more recently been observed in North America and Europe too [1].

By concentrating their efforts on Brazil, Guildma is able to launch attacks with a high degree of specificity, focussing their language on Brazilian norms, referencing Brazilian institutions, and tailoring their social engineering accordingly. Moreover, considering that Brazilian customers likely represent a relatively small portion of security vendors’ clientele, there may be a limited pool of available indicators of compromise (IoCs). This limitation could significantly impact the efficacy of traditional security measures that rely on signature-based detection methods in identifying emerging threats.

Darktrace vs. Guildma

In June 2023, Darktrace observed a Guildma compromise on the network of a Brazilian customer in the manufacturing sector. The anomaly-based detection capabilities of Darktrace DETECT™ allowed it to identify suspicious activity surrounding the compromise, agnostic of any IoCs or specific signatures of a threat actor. Following the successful detection of the malware, the Darktrace Security Operations Center (SOC) carried out a thorough investigation into the compromise and brought it to the attention of the customer’s security team, allowing them to quickly react and prevent any further escalation.

This early detection by Darktrace effectively shut down Guildma operations on the network before any sensitive data could be gathered and stolen by malicious actors.

Attack Overview

In the case of the Guildma RAT detected by Darktrace, the affected system was a desktop device, ostensibly used by one employee. The desktop was first observed on the customer’s network in April 2023; however, it is possible that the initial compromise took place before Darktrace had visibility over the network. Guildma compromises typically start with phishing campaigns, indicating that the initial intrusion in this case likely occurred beyond the scope of Darktrace’s monitoring [2].

Early indicators

On June 23, 2023, Darktrace DETECT observed the first instance of unusual activity being performed by the affected desktop device, namely regular HTTP POST requests to a suspicious domain, indicative of command-and-control (C2) beaconing activity. The domain used an unusual Top-Level Domain (TLD), with a plausibly meaningful (in Portuguese) second-level domain and a seemingly random 11-character third-level domain, “dn00x1o0f0h.puxaofolesanfoneiro[.]quest”.

Throughout the course of this attack, Darktrace observed additional connections like this, representing something of a signature of the attack. The suspicious domains were typically registered within six months of observation, featured an uncommon TLD, and included a seemingly randomized third-level domain of 6-11 characters, followed by a plausibly legitimate second-level domain with a minimum of 15 characters. The connections to these unusual endpoints all followed a similar two-hour beaconing period, suggesting that Guildma may rotate its C2 infrastructure, using the Multi-Stage Channels TTP (MITRE ID T1104) to evade restrictions by firewalls or other signature-based security tools that rely on static lists of IoCs and “known bads”.

Figure 1: Model Breach Event Log for the “Compromise / Agent Beacon (Long Period)”. The connections at two-hour intervals, including at unreasonably late hours, is consistent with beaconing for C2.

Living-off-the-land with BITS abuse

A week later, on June 30, 2023, the affected device was observed making an unusual Microsoft BITS connection. BitsAdmin is a deprecated administrative tool available on most Windows devices and can be leveraged by attackers to transfer malicious obfuscated payloads into and around an organization’s network. The domain observed during this connection, "cwiufv.pratkabelhaemelentmarta[.]shop”, follows the previously outlined domain naming convention. Multiple open-source intelligence (OSINT) sources indicated that the endpoint had links to malware and, when visited, redirected users to the Brazilian versions of WhatsApp and Zoom. This is likely a tactic employed by threat actors to ensure users are unaware of suspicious domains, and subsequent malware downloads, by redirected them to a trusted source.

Figure 2: A screenshot of the Model Breach log summary of the “Unusual BITS Activity” model breach. The breach log contains key details such as the ASN, hostname, and user agent used in the breaching connection.

Obfuscated Tooling Downloads

Within one minute of the suspicious BITS activity, Darktrace detected the device downloading a suspicious file from the aforementioned endpoint, (cwiufv.pratkabelhaemelentmarta[.]shop). The file in question appeared to be a ZIP file with the 17-digit numeric name query, namely “?37627343830628786”, with the filename “zodzXLWwaV.zip”.

However, Darktrace DETECT recognized that the file extension did not match its true file type and identified that it was, in fact, an executable (.exe) file masquerading as a ZIP file. By masquerading files downloads, threat actors are able to make their malicious files seem legitimate and benign to security teams and traditional security tools, thereby evading detection. In this case, the suspicious file in question was indeed identified as malicious by multiple OSINT sources.

Following the initial download of this masqueraded file, Darktrace also detected subsequent downloads of additional executable files from the same endpoint.  It is possible that these downloads represented Guildma actors attempting to download additional tooling, including the information-stealer widely known as Astaroth, in order to begin its data collection and exfiltration operations.

Figure 3: A screenshot of a graph produced by the Threat Visualizer of the affected device's external connections. The visual aid marks breaches with red and orange dots, creating a more intuitive explanation of observed behavior.

Darktrace SOC

The successful detection of the masqueraded file transfer triggered an Enhanced Monitoring model breach, a high-fidelity model designed to detect activity that is more likely indicative of an ongoing compromise.  

This breach was immediately escalated to the Darktrace SOC for analysis by Darktrace’s team of expert analysts who were able to complete a thorough investigation and notify the customer’s security team of the compromise in just over half an hour. The investigation carried out by Darktrace’s analysts confirmed that the activity was, indeed, malicious, and provided the customer’s security team with details around the extent of the compromise, the specific IoCs, and risks this compromise posed to their digital environment. This information empowered the customer’s security team to promptly address the issue, having a significant portion of the investigative burden reduced and resolved by the round-the-clock Darktrace analyst team.

In addition to this, Cyber AI Analyst™ launched an investigation into the ongoing compromise and was able to connect the anomalous HTTP connections to the subsequent suspicious file downloads, viewing them as one incident rather than two isolated events. AI Analyst completed its investigation in just three minutes, upon which it provided a detailed summary of events of the activity, further aiding the customer’s remediation process.

Figure 4: CyberAI Analyst summary of the suspicious activity. A prose summary of the breach activity and the meaning of the technical details is included to maintain an easily digestible stream of information.

Conclusion

While the combination of TTPs observed in this Guildma RAT compromise is not uncommon globally, the specificity to targeting organizations in Brazil allows it to be incredibly effective. By focussing on just one country, malicious actors are able to launch highly specialized attacks, adapting the language used and tailoring the social engineering effectively to achieve maximum success. Moreover, as Brazil likely represents a smaller segment of security vendors’ customers, therefore leading to a limited pool of IoCs, attackers are often able to evade traditional signature-based detections.

Darktrace DETECT’s anomaly-based approach to threat detection allows for effective detection, mitigation, and response to emerging threats, regardless of the specifics of the attack and without relying on threat intelligence or previous IoCs. Ultimately in this case, Darktrace was able to identify the suspicious activity surrounding the Guildma compromise and swiftly bring it to the attention of the customer’s security team, before any data gathering, or exfiltration activity took place.

Darktrace’s threat detection capabilities coupled with its expert analyst team and round-the-clock SOC response is a highly effective addition to an organization’s defense-in-depth, whether in Brazil or anywhere else around the world.

Credit to Roberto Romeu, Senior SOC Analyst, Taylor Breland, Analyst Team Lead, San Francisco

References

https://malpedia.caad.fkie.fraunhofer.de/details/win.astaroth

https://www.welivesecurity.com/2020/03/05/guildma-devil-drives-electric/  

Appendices

Darktrace DETECT Model Breaches

  • Compromise / Agent Beacon (Long Period)
  • Device / Unusual BITS Activity
  • Anomalous File / Anomalous Octet Stream (No User Agent)
  • Anomalous File / Masqueraded File Transfer (Enhanced Monitoring Model)
  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Multiple EXE from Rare External Locations

List of IoCs

IoC Type - Description + Confidence

5q710e1srxk.broilhasoruikaliventiladorrta[.]shop - Domain - Likely C2 server

m2pkdlse8md.roilhasohlcortinartai[.]hair - Domain - Likely C2 server

cwiufv.pratkabelhaemelentmarta[.]shop - Domain - C2 server

482w5pct234.jaroilcasacorkalilc[.]ru[.]com - Domain - C2 server

dn00x1o0f0h.puxaofolesanfoneiro[.]quest - Domain - Likely C2 server

10v7mybga55.futurefrontier[.]cyou - Domain - Likely C2 server

f788gbgdclp.growthgenerator[.]cyou - Domain - Likely C2 server

6nieek.satqabelhaeiloumelsmarta[.]shop - Domain - Likely C2 server

zodzXLWwaV.zip (SHA1 Hash: 2a4062e10a5de813f5688221dbeb3f3ff33eb417 ) - File hash - Malware

IZJQCAOXQb.zip (SHA1 Hash: eaec1754a69c50eac99e774b07ef156a1ca6de06 ) - File hash - Likely malware

MITRE ATT&CK Mapping

ATT&CK Technique - Technique ID

Multi-Stage Channels - T1104

BITS Jobs - T1197

Application Layer Protocol: Web Protocols - T1071.001

Acquire Infrastructure: Web Services - T1583.006

Obtain Capabilities: Malware - T1588.001

Masquerading - T1036

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
Roberto Romeu
Senior SOC Analyst

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May 12, 2026

Resilience at the Speed of AI: Defending the Modern Campus with Darktrace

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Why higher education is a different cybersecurity battlefield

After four decades in IT, now serving as both CIO and CISO, I’ve learned one simple truth: cybersecurity is never “done.” It’s a constant game of cat and mouse. Criminals evolve. Technologies advance. Regulations expand. But in higher education, the challenge is uniquely complex.

Unlike a bank or a military installation, we can’t lock down networks to a narrow set of approved applications. Higher education environments are open by design. Students collaborate globally, faculty conduct cutting-edge research, and administrators manage critical operations, all of which require seamless access to the internet, global networks, cloud platforms, and connected systems.

Combine that openness with expanding regulatory mandates and tight budgets, and the balancing act becomes clear.

Threat actors don’t operate under the same constraints. Often well-funded and sponsored by nation-states with significant resources, they’re increasingly organized, strategic, and innovative.

That sophistication shows up in the tactics we face every day, from social engineering and ransomware to AI-driven impersonation attacks. We’re dealing with massive volumes of data, countless signals, and a very small window between detection and damage.

No human team, no matter how talented or how numerous, can manually sift through that noise at the speed required.

Discovering a force multiplier

Nothing in cybersecurity is 100% foolproof. I never “set it and forget it.” But for institutions balancing rising threats and finite resources, the Darktrace ActiveAI Security Platform™ offers something incredibly valuable: peace of mind through speed and scale.

It closes the gap between detection and response in a way humans can’t possibly match. At the speed of light, it can quarantine, investigate, and contain anomalous activity.

I’ve purchased and deployed Darktrace three separate times at three different institutions because I’ve seen firsthand what it can do and what it enables teams like mine to achieve.

I first encountered Darktrace while serving as CIO for a large multi-campus college system. What caught my attention was Darktrace's Self-Learning AI, and its ability to learn what "normal" looked like across our network. Instead of relying solely on static signatures or rigid rules, Darktrace built a behavioral baseline unique to our environment and alerted us in real time when something simply didn’t look right.

In higher education, where strict lockdowns aren’t realistic, that behavioral model made all the difference. We deployed it across five campuses, and the impact was immediate. Operating 24/7, Darktrace surfaced threats in ways our team couldn’t replicate manually.

Over time, the Darktrace platform evolved alongside the changing threat landscape, expanding into intrusion prevention, cloud visibility, and email security. At subsequent institutions, including Washington College, Darktrace was one of my first strategic investments.

Revealing the hidden threat other tools missed

One of the most surprising investigations of my career involved a data leak. Leadership suspected sensitive information from high-level meetings was being exposed, but our traditional tools couldn’t provide any answers.

Using Darktrace’s deep network visibility, down to packet-level data, we traced unusual connections to our CCTV camera system, which had been configured with a manufacturer’s default password. A small group of employees had hacked into the CCTV cameras, accessed audio-enabled recordings from boardroom meetings, and stored copies locally.

No other tool in our environment could have surfaced those connections the way Darktrace did. It was a clear example of why using AI to deeply understand how your organization, systems, and tools normally behave, matters: threats and risks don’t always look the way we expect.

Elevating a D-rating into a A-level security program

When I arrived at my last CISO role, the institution had recently experienced a significant ransomware attack. Attackers located  data  which informed their setting  ransom demands to an amount they knew would likely result in payment. It was a sobering example of how calculated and strategic modern cybercriminals have become.

Third-party cyber ratings reflected that reality, with a  D rating.

To raise the bar, we implemented a comprehensive security program and integrated layered defenses; -deploying state of the art tools and methods-  across the environment, with Darktrace at its core.

After a 90-day learning period to establish our behavioral baseline, we transitioned the platform into fully autonomous mode. In a single 30-day span, Darktrace conducted more than 2,500 investigations and autonomously resolved 92% of all false positives.

For a small team, that’s transformative. Instead of drowning in alerts, my staff focused on less than  200 meaningful cases that warranted human review.

Today, we maintain a perfect A rating from third-party assessors and have remained cybersafe.

Peace of mind isn’t about complacency

The effect of Darktrace as a force multiplier has a real human impact.

With the time reclaimed through automation, we expanded community education programs and implemented simulated phishing exercises. Through sustained training and awareness efforts, we reduced social engineering susceptibility from nearly 45% to under 5%.

On a personal level, Darktrace allows me to sleep better at night and take time off knowing we have intelligent systems monitoring and responding around the clock. For any CIO or CISO carrying institutional risk on their shoulders, that matters.

The next era: AI vs. AI

A new chapter in cybersecurity is unfolding as adversaries leverage AI to enhance scale, speed, and believability. Phishing campaigns are more personalized, impersonation attempts are more precise, and deepfake video technology, including live video, is disturbingly authentic. At the same time, organizations are rapidly adopting AI across their own environments —from GenAI assistants to embedded tools to autonomous agents. These systems don’t operate within fixed rules. They act across email, cloud, SaaS, and identity systems, often with broad permissions, and their behavior can evolve over time in ways that are difficult to predict or control.

That creates a new kind of security challenge. It’s not just about defending against AI-powered threats but understanding and governing how AI behaves within your environment, including what it can access, how it acts, and where risk begins to emerge.

From my perspective, this is a natural next step for Darktrace.

Darktrace brings a level of maturity and behavioral understanding uniquely suited to the complexity of AI environments. Self-Learning AI learns the normal patterns of each business to interpret context, uncover subtle intent, and detect meaningful deviations without relying on predefined rules or signatures. Extending into securing AI by bringing real-time visibility and control to GenAI assistants, AI agents, development environments and Shadow AI, feels like the logical evolution of what Darktrace already does so well.

Just as importantly, Darktrace is already built for dynamic, cross-domain environments where risk doesn’t sit in a single tool or control plane. In higher education, activity already spans multiple systems and, with AI, that interconnection only accelerates.

Having deployed Darktrace multiple times, I have confidence it’s uniquely positioned to lead in this space and help organizations adopt AI with greater visibility and control.

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Since authoring this blog, Irving Bruckstein has transitioned to the role of Chief Executive Officer of the Cyberaigroup.

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About the author
Irving Bruckstein
CEO CyberAIgroup

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May 11, 2026

The Next Step After Mythos: Defending in a World Where Compromise is Expected

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Is Anthropic’s Mythos a turning point for cybersecurity?

Anthropic’s recent announcements around their Mythos model, alongside the launch of Project Glasswing, have generated significant interest across the cybersecurity industry.

The closed-source nature of the Mythos model has understandably attracted a degree of skepticism around some of the claims being made. Additionally, Project Glasswing was initially positioned as a way for software vendors to accelerate the proactive discovery of vulnerabilities in their own code; however, much of the attention has focused on the potential for AI to identify exploitable vulnerabilities for those with malicious intent.

Putting questions around the veracity of those claims to one side – which, for what it’s worth, do appear to be at least partially endorsed by independent bodies such as the UK’s AI Security Institute – this should not be viewed as a critical turning point for the industry. Rather, it reflects the natural direction of travel.

How Mythos affects cybersecurity teams  

At Darktrace, extolling the virtues of AI within cybersecurity is understandably close to our hearts. However, taking a step back from the hype, we’d like to consider what developments like this mean for security teams.

Whether it’s Mythos or another model yet to be released, it’s worth remembering that there is no fundamental difference between an AI discovered vulnerability and one discovered by a human. The change is in the pace of discovery and, some may argue, the lower the barrier to entry.

In the hands of a software developer, this is unquestionably positive. Faster discovery enables earlier remediation and more proactive security. But in the hands of an attacker, the same capability will likely lead to a greater number of exploitable vulnerabilities being used in the wild and, critically, vulnerabilities that are not yet known to either the vendor or the end user.

That said, attackers have always been able to find exploitable vulnerabilities and use them undetected for extended periods of time. The use of AI does not fundamentally change this reality, but it does make the process faster and, unfortunately, more likely to occur at scale.

While tools such as Darktrace / Attack Surface Management and / Proactive Exposure Management  can help security teams prioritize where to patch, the emergence of AI-driven vulnerability discovery reinforces an important point: patching alone is not a sufficient control against modern cyber-attacks.

Rethinking defense for a world where compromise is expected

Rather than assuming vulnerabilities can simply be patched away, defenders are better served by working from the assumption that their software is already vulnerable - and always will be -and build their security strategy accordingly.

Under that assumption, defenders should expect initial access, particularly across internet exposed assets, to become easier for attackers. What matters then is how quickly that foothold is detected, contained, and prevented from expanding.

For defenders, this places renewed emphasis on a few core capabilities:

  • Secure-by-design architectures and blast radius reduction, particularly around identity, MFA, segmentation, and Zero Trust principles
  • Early, scalable detection and containment, favoring behavioral and context-driven signals over signatures alone
  • Operational resilience, with the expectation of more frequent early-stage incidents that must be managed without burning out teams

How Darktrace helps organizations proactively defend against cyber threats

At Darktrace, we support security teams across all three of these critical capabilities through a multi-layered AI approach. Our Self-Learning AI learns what’s normal for your organization, enabling real-time threat detection, behavioral prediction, incident investigation and autonomous response. - all while empowering your security team with visibility and control.

To learn more about Darktrace’s application of AI to cybersecurity download our White Paper here.  

Reducing blast radius through visibility and control

Secure-by-design principles depend on understanding how users, devices, and systems behave. By learning the normal patterns of identity and network activity, Darktrace helps teams identify when access is being misused or when activity begins to move beyond expected boundaries. This makes it possible to detect and contain lateral movement early, limiting how far an attacker can progress even after initial access.

Detecting and containing threats at the earliest stage  

As AI accelerates vulnerability discovery, defenders need to identify exploitation before it is formally recognized. Darktrace’s behavioral understanding approach enables detection of subtle deviations from normal activity, including those linked to previously unknown vulnerabilities.

A key example of this is our research on identifying cyber threats before public CVE disclosures, demonstrating that assessing activity against what is normal for a specific environment, rather than relying on predefined indicators of compromise, enables detection of intrusions exploiting previously unknown vulnerabilities days or even weeks before details become publicly available.

Additionally, our Autonomous Response capability provides fast, targeted containment focused on the most concerning events, while allowing normal business operations to continue. This has consistently shown that even when attackers use techniques never seen before, Darktrace’s Autonomous Response can contain threats before they have a chance to escalate.

Scaling response without increasing operational burden

As early-stage incidents become more frequent, the ability to investigate and respond efficiently becomes critical. Darktrace’s Cyber AI Analyst’s AI-driven investigation capabilities automatically correlate activity across the environment, prioritizing the most significant threats and reducing the need for manual triage. This allows security teams to respond faster and more consistently, without increasing workload or burnout.

What effective defense looks like in an AI-accelerated landscape

Developments like Mythos highlight a reality that has been building for some time: the window between exposure and exploitation is shrinking, and in many cases, it may disappear entirely. In that environment, relying on patching alone becomes increasingly reactive, leaving little room to respond once access has been established.

The more durable approach is to assume that compromise will occur and focus on controlling what happens next. That means identifying early signs of misuse, containing threats before they spread, and maintaining visibility across the environment so that isolated signals can be understood in context.

AI plays a role on both sides of this equation. While it enables attackers to move faster, it also gives defenders the ability to detect subtle changes in behavior, prioritize what matters, and respond in real time. The advantage will not come from adopting AI in isolation, but from applying it in a way that reduces the gap between detection and action.

AI may be accelerating parts of the attack lifecycle, but the fundamentals of defense, detection, and containment still apply. If anything, they matter more than ever – and AI is just as powerful a tool for defenders as it is for attackers.

To learn more about Darktrace and Mythos read more on our blog: Mythos vs Ethos: Defending in an Era of AI‑Accelerated Vulnerability Discovery

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About the author
Toby Lewis
Head of Threat Analysis
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