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Exposing a Demonic Threat: Darktrace’s Fight Against Malware Targeting Brazilian Organizations

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13
Oct 2023
13
Oct 2023
This blog details how Darktrace DETECT identified a banking trojan known to target organizations in Brazil before it was able to steal any sensitive customer data. Following the initial detection, Darktrace’s global SOC were able to investigate the incident and inform the customer for swift mitigation.

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

DANS LE SOC
Darktrace sont des experts de classe mondiale en matière de renseignement sur les menaces, de chasse aux menaces et de réponse aux incidents. Ils fournissent une assistance SOC 24 heures sur 24 et 7 jours sur 7 à des milliers de clients Darktrace dans le monde entier. Inside the SOC est exclusivement rédigé par ces experts et fournit une analyse des cyberincidents et des tendances en matière de menaces, basée sur une expérience réelle sur le terrain.
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Roberto Romeu
Senior SOC Analyst
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Lost in Translation: Darktrace Blocks Non-English Phishing Campaign Concealing Hidden Payloads

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15
May 2024

Email – the vector of choice for threat actors

In times of unprecedented globalization and internationalization, the enormous number of emails sent and received by organizations every day has opened the door for threat actors looking to gain unauthorized access to target networks.

Now, increasingly global organizations not only need to safeguard their email environments against phishing campaigns targeting their employees in their own language, but they also need to be able to detect malicious emails sent in foreign languages too [1].

Why are non-English language phishing emails more popular?

Many traditional email security vendors rely on pre-trained English language models which, while function adequately against malicious emails composed in English, would struggle in the face of emails composed in other languages. It should, therefore, come as no surprise that this limitation is becoming increasingly taken advantage of by attackers.  

Darktrace/Email™, on the other hand, focuses on behavioral analysis and its Self-Learning AI understands what is considered ‘normal’ for every user within an organization’s email environment, bypassing any limitations that would come from relying on language-trained models [1].

In March 2024, Darktrace observed anomalous emails on a customer’s network that were sent from email addresses belonging to an international fast-food chain. Despite this seeming legitimacy, Darktrace promptly identified them as phishing emails that contained malicious payloads, preventing a potentially disruptive network compromise.

Attack Overview and Darktrace Coverage

On March 3, 2024, Darktrace observed one of the customer’s employees receiving an email which would turn out to be the first of more than 50 malicious emails sent by attackers over the course of three days.

The Sender

Darktrace/Email immediately understood that the sender never had any previous correspondence with the organization or its employees, and therefore treated the emails with caution from the onset. Not only was Darktrace able to detect this new sender, but it also identified that the emails had been sent from a domain located in China and contained an attachment with a Chinese file name.

The phishing emails detected by Darktrace sent from a domain in China and containing an attachment with a Chinese file name.
Figure 1: The phishing emails detected by Darktrace sent from a domain in China and containing an attachment with a Chinese file name.

Darktrace further detected that the phishing emails had been sent in a synchronized fashion between March 3 and March 5. Eight unique senders were observed sending a total of 55 emails to 55 separate recipients within the customer’s email environment. The format of the addresses used to send these suspicious emails was “12345@fastflavor-shack[.]cn”*. The domain “fastflavor-shack[.]cn” is the legitimate domain of the Chinese division of an international fast-food company, and the numerical username contained five numbers, with the final three digits changing which likely represented different stores.

*(To maintain anonymity, the pseudonym “Fast Flavor Shack” and its fictitious domain, “fastflavor-shack[.]cn”, have been used in this blog to represent the actual fast-food company and the domains identified by Darktrace throughout this incident.)

The use of legitimate domains for malicious activities become commonplace in recent years, with attackers attempting to leverage the trust endpoint users have for reputable organizations or services, in order to achieve their nefarious goals. One similar example was observed when Darktrace detected an attacker attempting to carry out a phishing attack using the cloud storage service Dropbox.

As these emails were sent from a legitimate domain associated with a trusted organization and seemed to be coming from the correct connection source, they were verified by Sender Policy Framework (SPF) and were able to evade the customer’s native email security measures. Darktrace/Email; however, recognized that these emails were actually sent from a user located in Singapore, not China.

Darktrace/Email identified that the email had been sent by a user who had logged in from Singapore, despite the connection source being in China.
Figure 2: Darktrace/Email identified that the email had been sent by a user who had logged in from Singapore, despite the connection source being in China.

The Emails

Darktrace/Email autonomously analyzed the suspicious emails and identified that they were likely phishing emails containing a malicious multistage payload.

Darktrace/Email identifying the presence of a malicious phishing link and a multistage payload.
Figure 3: Darktrace/Email identifying the presence of a malicious phishing link and a multistage payload.

There has been a significant increase in multistage payload attacks in recent years, whereby a malicious email attempts to elicit recipients to follow a series of steps, such as clicking a link or scanning a QR code, before delivering a malicious payload or attempting to harvest credentials [2].

In this case, the malicious actor had embedded a suspicious link into a QR code inside a Microsoft Word document which was then attached to the email in order to direct targets to a malicious domain. While this attempt to utilize a malicious QR code may have bypassed traditional email security tools that do not scan for QR codes, Darktrace was able to identify the presence of the QR code and scan its destination, revealing it to be a suspicious domain that had never previously been seen on the network, “sssafjeuihiolsw[.]bond”.

Suspicious link embedded in QR Code, which was detected and extracted by Darktrace.
Figure 4: Suspicious link embedded in QR Code, which was detected and extracted by Darktrace.

At the time of the attack, there was no open-source intelligence (OSINT) on the domain in question as it had only been registered earlier the same day. This is significant as newly registered domains are typically much more likely to bypass gateways until traditional security tools have enough intelligence to determine that these domains are malicious, by which point a malicious actor may likely have already gained access to internal systems [4]. Despite this, Darktrace’s Self-Learning AI enabled it to recognize the activity surrounding these unusual emails as suspicious and indicative of a malicious phishing campaign, without needing to rely on existing threat intelligence.

The most commonly used sender name line for the observed phishing emails was “财务部”, meaning “finance department”, and Darktrace observed subject lines including “The document has been delivered”, “Income Tax Return Notice” and “The file has been released”, all written in Chinese.  The emails also contained an attachment named “通知文件.docx” (“Notification document”), further indicating that they had been crafted to pass for emails related to financial transaction documents.

 Darktrace/Email took autonomous mitigative action against the suspicious emails by holding the message from recipient inboxes.
Figure 5: Darktrace/Email took autonomous mitigative action against the suspicious emails by holding the message from recipient inboxes.

Conclusion

Although this phishing attack was ultimately thwarted by Darktrace/Email, it serves to demonstrate the potential risks of relying on solely language-trained models to detect suspicious email activity. Darktrace’s behavioral and contextual learning-based detection ensures that any deviations in expected email activity, be that a new sender, unusual locations or unexpected attachments or link, are promptly identified and actioned to disrupt the attacks at the earliest opportunity.

In this example, attackers attempted to use non-English language phishing emails containing a multistage payload hidden behind a QR code. As traditional email security measures typically rely on pre-trained language models or the signature-based detection of blacklisted senders or known malicious endpoints, this multistage approach would likely bypass native protection.  

Darktrace/Email, meanwhile, is able to autonomously scan attachments and detect QR codes within them, whilst also identifying the embedded links. This ensured that the customer’s email environment was protected against this phishing threat, preventing potential financial and reputation damage.

Credit to: Rajendra Rushanth, Cyber Analyst, Steven Haworth, Head of Threat Modelling, Email

Appendices  

List of Indicators of Compromise (IoCs)  

IoC – Type – Description

sssafjeuihiolsw[.]bond – Domain Name – Suspicious Link Domain

通知文件.docx – File - Payload  

References

[1] https://darktrace.com/blog/stopping-phishing-attacks-in-enter-language  

[2] https://darktrace.com/blog/attacks-are-getting-personal

[3] https://darktrace.com/blog/phishing-with-qr-codes-how-darktrace-detected-and-blocked-the-bait

[4] https://darktrace.com/blog/the-domain-game-how-email-attackers-are-buying-their-way-into-inboxes

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The State of AI in Cybersecurity: The Impact of AI on Cybersecurity Solutions

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13
May 2024

About the AI Cybersecurity Report

Darktrace surveyed 1,800 CISOs, security leaders, administrators, and practitioners from industries around the globe. Our research was conducted to understand how the adoption of new AI-powered offensive and defensive cybersecurity technologies are being managed by organizations.

This blog continues the conversation from “The State of AI in Cybersecurity: Unveiling Global Insights from 1,800 Security Practitioners” which was an overview of the entire report. This blog will focus on one aspect of the overarching report, the impact of AI on cybersecurity solutions.

To access the full report, click here.

The effects of AI on cybersecurity solutions

Overwhelming alert volumes, high false positive rates, and endlessly innovative threat actors keep security teams scrambling. Defenders have been forced to take a reactive approach, struggling to keep pace with an ever-evolving threat landscape. It is hard to find time to address long-term objectives or revamp operational processes when you are always engaged in hand-to-hand combat.                  

The impact of AI on the threat landscape will soon make yesterday’s approaches untenable. Cybersecurity vendors are racing to capitalize on buyer interest in AI by supplying solutions that promise to meet the need. But not all AI is created equal, and not all these solutions live up to the widespread hype.  

Do security professionals believe AI will impact their security operations?

Yes! 95% of cybersecurity professionals agree that AI-powered solutions will level up their organization’s defenses.                                                                

Not only is there strong agreement about the ability of AI-powered cybersecurity solutions to improve the speed and efficiency of prevention, detection, response, and recovery, but that agreement is nearly universal, with more than 95% alignment.

This AI-powered future is about much more than generative AI. While generative AI can help accelerate the data retrieval process within threat detection, create quick incident summaries, automate low-level tasks in security operations, and simulate phishing emails and other attack tactics, most of these use cases were ranked lower in their impact to security operations by survey participants.

There are many other types of AI, which can be applied to many other use cases:

Supervised machine learning: Applied more often than any other type of AI in cybersecurity. Trained on attack patterns and historical threat intelligence to recognize known attacks.

Natural language processing (NLP): Applies computational techniques to process and understand human language. It can be used in threat intelligence, incident investigation, and summarization.

Large language models (LLMs): Used in generative AI tools, this type of AI applies deep learning models trained on massively large data sets to understand, summarize, and generate new content. The integrity of the output depends upon the quality of the data on which the AI was trained.

Unsupervised machine learning: Continuously learns from raw, unstructured data to identify deviations that represent true anomalies. With the correct models, this AI can use anomaly-based detections to identify all kinds of cyber-attacks, including entirely unknown and novel ones.

What are the areas of cybersecurity AI will impact the most?

Improving threat detection is the #1 area within cybersecurity where AI is expected to have an impact.                                                                                  

The most frequent response to this question, improving threat detection capabilities in general, was top ranked by slightly more than half (57%) of respondents. This suggests security professionals hope that AI will rapidly analyze enormous numbers of validated threats within huge volumes of fast-flowing events and signals. And that it will ultimately prove a boon to front-line security analysts. They are not wrong.

Identifying exploitable vulnerabilities (mentioned by 50% of respondents) is also important. Strengthening vulnerability management by applying AI to continuously monitor the exposed attack surface for risks and high-impact vulnerabilities can give defenders an edge. If it prevents threats from ever reaching the network, AI will have a major downstream impact on incident prevalence and breach risk.

Where will defensive AI have the greatest impact on cybersecurity?

Cloud security (61%), data security (50%), and network security (46%) are the domains where defensive AI is expected to have the greatest impact.        

Respondents selected broader domains over specific technologies. In particular, they chose the areas experiencing a renaissance. Cloud is the future for most organizations,
and the effects of cloud adoption on data and networks are intertwined. All three domains are increasingly central to business operations, impacting everything everywhere.

Responses were remarkably consistent across demographics, geographies, and organization sizes, suggesting that nearly all survey participants are thinking about this similarly—that AI will likely have far-reaching applications across the broadest fields, as well as fewer, more specific applications within narrower categories.

Going forward, it will be paramount for organizations to augment their cloud and SaaS security with AI-powered anomaly detection, as threat actors sharpen their focus on these targets.

How will security teams stop AI-powered threats?            

Most security stakeholders (71%) are confident that AI-powered security solutions are better able to block AI-powered threats than traditional tools.

There is strong agreement that AI-powered solutions will be better at stopping AI-powered threats (71% of respondents are confident in this), and there’s also agreement (66%) that AI-powered solutions will be able to do so automatically. This implies significant faith in the ability of AI to detect threats both precisely and accurately, and also orchestrate the correct response actions.

There is also a high degree of confidence in the ability of security teams to implement and operate AI-powered solutions, with only 30% of respondents expressing doubt. This bodes well for the acceptance of AI-powered solutions, with stakeholders saying they’re prepared for the shift.

On the one hand, it is positive that cybersecurity stakeholders are beginning to understand the terms of this contest—that is, that only AI can be used to fight AI. On the other hand, there are persistent misunderstandings about what AI is, what it can do, and why choosing the right type of AI is so important. Only when those popular misconceptions have become far less widespread can our industry advance its effectiveness.  

To access the full report, click here.

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