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August 25, 2020

Emotet Resurgence: Email & Network Defense Insights

Explore how Darktrace's defense in depth strategy combats Emotet's resurgence in email and network layers, ensuring robust cybersecurity.
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
Written by
Dan Fein
VP, Product
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25
Aug 2020

The Emotet banking malware first emerged in 2014, and has since undergone multiple iterations. Emotet seeks to financially profit from a range of organizations by spreading rapidly from device to device and stealing sensitive financial information.

Darktrace’s AI has detected the return of this botnet after a five month absence. The new Spamware campaign has hit multiple industries through highly sophisticated phishing emails, containing either URLs linking to the download of a macro-containing Microsoft Word document or an attachment of the document itself. This iteration uses new variants of infrastructure and malware that were unknown to threat intelligence lists – thus easily bypassing static, rule-based defenses.

In this blog post, we investigate the attack from two angles. The first documents a case where Emotet successfully infiltrated a company’s network, where it was promptly detected and alerted on by the Enterprise Immune System. We then explore two customers who had extended Darktrace’s Cyber AI coverage to the inbox. While these organizations were also targeted by this latest Emotet campaign, the malicious email containing the Emotet payload was identified and blocked by Antigena Email.

Case study one: Detecting Emotet in the network

Figure 1: A timeline of the attack

This first case study looks at a large European organization spanning multiple industries, including healthcare, pharmaceuticals, and manufacturing. Darktrace’s AI was monitoring over 2500 devices when the organization became a victim of this new wave of Emotet.

The attack entered the business via a phishing email that fell outside of Darktrace’s scope in this particular deployment, as the customer had not yet activated Antigena Email. Either a malicious link or a macro-embedded Word document in the email directed a device to the malicious payload.

Darktrace’s Enterprise Immune System witnessed SSL connections to a 100% rare external IP address, and detected a Kernel crash on the device shortly afterwards, indicating potential exploitation.

Following these actions, the desktop began to beacon to multiple external endpoints using self-signed or invalid SSL certificates. The observed endpoints had previously been associated with Trickbot C2 servers and the Emotet malware. The likely overall dwell time – that is the length of time an attacker has free reign in an environment before they are eradicated – was in this instance around 24 hours, with most of the activity taking place on July 23.

The device then made a large number of new and unusual internal connection attempts over SMB (port 445) to 97 internal devices during a one-hour period. The goal was likely lateral movement, possibly with the intention to infect other devices, download additional malware, and send out more spam emails.

Darktrace’s AI had promptly alerted the security team to the initial rare connections, but when the device attempted lateral movement it escalated the severity of the alert. The security team was able to remediate the situation before further damage was done, taking the desktop offline.

This overview of the infected device shows the extent of the anomalous behavior, with over a dozen Darktrace detections firing in quick succession.

Figure 2: A graph showing unusual activity in combination with the large number of model breaches on July 23

Figure 3: A list of all model breaches occurring over a small time on the compromised device

Case study two: Catching Emotet in the email environment

While Darktrace’s Enterprise Immune System allows us to visualize the attack within the network, Antigena Email has also identified the Emotet phishing campaign in many other customer environments and stopped the attack before the payload could be downloaded.

One European organization was hit by multiple phishing emails associated with Emotet. These emails use a number of tactics, including personalized subject lines, malicious attachments, and hidden malicious URLs. However, Darktrace’s AI recognized the emails as highly anomalous for the organization and prevented them from reaching employees’ inboxes.

Figure 4: A snapshot of Antigena Email’s user interface. The subject line reads ‘Notice of transfer.’

Despite claiming to be from CaixaBank, a Spanish financial services company, Antigena Email revealed that the email was actually sent from a Brazilian domain. The email also contained a link that was hidden behind text suggesting it would lead to a CaixaBank domain, but Darktrace recognized this as a deliberate attempt to mislead the recipient. Antigena Email is unique in its ability to gather insights from across the broader business, and it leveraged this ability to reveal that the link in fact led to a WordPress domain that Darktrace’s AI identified as 100% rare for the business. This would not have been possible without a unified security platform analyzing and comparing data across different parts of the organization.

Figure 5: The malicious links contained in the email

The three above links surfaced by Darktrace are all associated with the Emotet malware, and prompt the user to download a Word file. This document contains a macro with instructions for downloading the actual virus payload.

Another email targeting the same organization contained a header suggesting it was from Vietnam. The sender had never been in any previous correspondence across the business, and the single, isolated link within the email was also revealed to be a 100% rare domain. The website displayed when visiting the domain imitates a legitimate printing business, but appears hastily made and contained a similar malicious payload.

In both cases, Darktrace’s AI recognized these as phishing attempts due to its understanding of normal communication patterns and behavior for the business and held the emails back from the inbox, preventing Emotet from entering the next phase of the attack life cycle.

Case study three: A truly global campaign

Darktrace has seen Emotet in attacks targeting customers around the world, with one of the most recent campaigns aimed at a food production and distribution company in Japan. This customer received six Emotet emails across July 29 and July 30. The senders spoofed Japanese names and some existing Japanese companies, including Mitsubishi. Antigena Email successfully detected and actioned these emails, recognizing the spoofing indicators, ‘unspoofing’ the emails, and converting the attachments.

Figure 6: A second Emotet email targeting an organization in Japan

Revealing a phish

Both the subject line and the filename translate to “Regarding the invoice,” followed by a number and the date. The email imitated a well-known Japanese company (三菱食品(株)), with ‘藤沢 昭彦’ as a common Japanese name and the appended ‘様’ serving a similar function to ‘Sir’ or ‘Dr,’ in a clear attempt to mimic a legitimate business email.

A subsequent investigation revealed that the sender’s location was actually Portugal, and the hash values of Microsoft Word attachments were consistent with Emotet. Crucially, at the time of the attack, these file hashes were not publicly associated with any malicious behavior and so could not have been used for initial detection.

Figure 7: Antigena Email shows critical metrics revealing the true source of the email

Surfacing further key metrics behind the email, Antigena Email revealed that the true sender was using a GMO domain name. GMO is a Japanese cloud-hosting company that offers cheap web email services.

Figure 8: Antigena Email reveals the anomalous extensions and mimes

The details of the attachment show that both the extension and mime type is anomalous in comparison to documents this customer commonly exchanges by email.

Figure 9: Antigena Email detects the attempt at inducement

Antigena Email’s models are able to recognize topic anomalies and inducement attempts in emails, regardless of the language they are written in. Despite this email being written in Japanese, Darktrace’s AI was still able to reveal the attempt at inducement, giving the email a high score of 85.

Figure 10: The six successive Emotet emails

The close proximity in which these emails were sent and the fact they all contained URLs consistent with Emotet suggests that they are likely part of the same campaign. Different recipients received the emails from different senders in an attempt to bypass traditional security tools, which are trained to deny-list an individual sender once it is recognized as bad.

A defense in depth

This new campaign and the comeback of the Emotet malware has shown the need for defense in depth – or having multiple layers of security across the different areas of a business, including email, network, cloud and SaaS, and beyond.

Historically, defense in depth has led companies to adopt myriad point solutions, which can be both expensive and challenging to manage. Security leaders are increasingly abandoning point solutions in favor of a single security platform, which not only makes handling the security stack easier and more efficient, but creates synergies between different parts of the platform. Data can be analyzed across different sources and insights drawn from different areas of the organization, helping detect sophisticated attacks that might attempt to exploit a business’ siloed approach to security.

A single platform ultimately reduces the friction for security teams while allowing for effective, company-wide incident investigation. And when a platform approach leverages AI to understand normal behavior rather than looking for ‘known bad’, it can detect unknown and emerging threats – and help prevent damage from being done.

Thanks to Darktrace analyst Beverly McCann for her insights on the above threat find.

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
Written by
Dan Fein
VP, Product

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November 19, 2025

Securing Generative AI: Managing Risk in Amazon Bedrock with Darktrace / CLOUD

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Security risks and challenges of generative AI in the enterprise

Generative AI and managed foundation model platforms like Amazon Bedrock are transforming how organizations build and deploy intelligent applications. From chatbots to summarization tools, Bedrock enables rapid agent development by connecting foundation models to enterprise data and services. But with this flexibility comes a new set of security challenges, especially around visibility, access control, and unintended data exposure.

As organizations move quickly to operationalize generative AI, traditional security controls are struggling to keep up. Bedrock’s multi-layered architecture, spanning agents, models, guardrails, and underlying AWS services, creates new blind spots that standard posture management tools weren’t designed to handle. Visibility gaps make it difficult to know which datasets agents can access, or how model outputs might expose sensitive information. Meanwhile, developers often move faster than security teams can review IAM permissions or validate guardrails, leading to misconfigurations that expand risk. In shared-responsibility environments like AWS, this complexity can blur the lines of ownership, making it critical for security teams to have continuous, automated insight into how AI systems interact with enterprise data.

Darktrace / CLOUD provides comprehensive visibility and posture management for Bedrock environments, automatically detecting and proactively scanning agents and knowledge bases, helping teams secure their AI infrastructure without slowing down expansion and innovation.

A real-world scenario: When access goes too far

Consider a scenario where an organization deploys a Bedrock agent to help internal staff quickly answer business questions using company knowledge. The agent was connected to a knowledge base pointing at documents stored in Amazon S3 and given access to internal services via APIs.

To get the system running quickly, developers assigned the agent a broad execution role. This role granted access to multiple S3 buckets, including one containing sensitive customer records. The over-permissioning wasn’t malicious; it stemmed from the complexity of IAM policy creation and the difficulty of identifying which buckets held sensitive data.

The team assumed the agent would only use the intended documents. However, they did not fully consider how employees might interact with the agent or how it might act on the data it processed.  

When an employee asked a routine question about quarterly customer activity, the agent surfaced insights that included regulated data, revealing it to someone without the appropriate access.

This wasn’t a case of prompt injection or model manipulation. The agent simply followed instructions and used the resources it was allowed to access. The exposure was valid under IAM policy, but entirely unintended.

How Darktrace / CLOUD prevents these risks

Darktrace / CLOUD helps organizations avoid scenarios like unintended data exposure by providing layered visibility and intelligent analysis across Bedrock and SageMaker environments. Here’s how each capability works in practice:

Configuration-level visibility

Bedrock deployments often involve multiple components: agents, guardrails, and foundation models, each with its own configuration. Darktrace / CLOUD indexes these configurations so teams can:

  1. Inspect deployed agents and confirm they are connected only to approved data sources.
  2. Track evaluation job setups and their links to Amazon S3 datasets, uncovering hidden data flows that could expose sensitive information.
  3. Maintain full awareness of all AI components, reducing the chance of overlooked assets introducing risk.

By unifying configuration data across Bedrock, SageMaker, and other AWS services, Darktrace / CLOUD provides a single source of truth for AI asset visibility. Teams can instantly see how each component is configured and whether it aligns with corporate security policies. This eliminates guesswork, accelerates audits, and helps prevent misaligned settings from creating data exposure risks.

 Agents for bedrock relationship views.
Figure 1: Agents for bedrock relationship views

Architectural awareness

Complex AI environments can make it difficult to understand how components interact. Darktrace / CLOUD generates real-time architectural diagrams that:

  1. Visualize relationships between agents, models, and datasets.
  1. Highlight unintended data access paths or risk propagation across interconnected services.

This clarity helps security teams spot vulnerabilities before they lead to exposure. By surfacing these relationships dynamically, Darktrace / CLOUD enables proactive risk management, helping teams identify architectural drift, redundant data connections, or unmonitored agents before attackers or accidental misuse can exploit them. This reduces investigation time and strengthens compliance confidence across AI workloads.

Figure 2: Full Bedrock agent architecture including lambda and IAM permission mapping
Figure 2: Full Bedrock agent architecture including lambda and IAM permission mapping

Access & privilege analysis

IAM permissions apply to every AWS service, including Bedrock. When Bedrock agents assume IAM roles that were broadly defined for other workloads, they often inherit excessive privileges. Without strict least-privilege controls, the agent may have access to far more data and services than required, creating avoidable security exposure. Darktrace / CLOUD:

  1. Reviews execution roles and user permissions to identify excessive privileges.
  2. Flags anomalies that could enable privilege escalation or unauthorized API actions.

This ensures agents operate within the principle of least privilege, reducing attack surface. Beyond flagging risky roles, Darktrace / CLOUD continuously learns normal patterns of access to identify when permissions are abused or expanded in real time. Security teams gain context into why an action is anomalous and how it could affect connected assets, allowing them to take targeted remediation steps that preserve productivity while minimizing exposure.

Misconfiguration detection

Misconfigurations are a leading cause of cloud security incidents. Darktrace / CLOUD automatically detects:

  1. Publicly accessible S3 buckets that may contain sensitive training data.
  2. Missing guardrails in Bedrock deployments, which can allow inappropriate or sensitive outputs.
  3. Other issues such as lack of encryption, direct internet access, and root access to models.  

By surfacing these risks early, teams can remediate before they become exploitable. Darktrace / CLOUD turns what would otherwise be manual reviews into automated, continuous checks, reducing time to discovery and preventing small oversights from escalating into full-scale incidents. This automated assurance allows organizations to innovate confidently while keeping their AI systems compliant and secure by design.

Configuration data for Anthropic foundation model
Figure 3: Configuration data for Anthropic foundation model

Behavioral anomaly detection

Even with correct configurations, behavior can signal emerging threats. Using AWS CloudTrail, Darktrace / CLOUD:

  1. Monitors for unusual data access patterns, such as agents querying unexpected datasets.
  2. Detects anomalous training job invocations that could indicate attempts to pollute models.

This real-time behavioral insight helps organizations respond quickly to suspicious activity. Because it learns the “normal” behavior of each Bedrock component over time, Darktrace / CLOUD can detect subtle shifts that indicate emerging risks, before formal indicators of compromise appear. The result is faster detection, reduced investigation effort, and continuous assurance that AI-driven workloads behave as intended.

Conclusion

Generative AI introduces transformative capabilities but also complex risks that evolve alongside innovation. The flexibility of services like Amazon Bedrock enables new efficiencies and insights, yet even legitimate use can inadvertently expose sensitive data or bypass security controls. As organizations embrace AI at scale, the ability to monitor and secure these environments holistically, without slowing development, is becoming essential.

By combining deep configuration visibility, architectural insight, privilege and behavior analysis, and real-time threat detection, Darktrace gives security teams continuous assurance across AI tools like Bedrock and SageMaker. Organizations can innovate with confidence, knowing their AI systems are governed by adaptive, intelligent protection.

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Adam Stevens
Senior Director of Product, Cloud | Darktrace

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November 19, 2025

Unmasking Vo1d: Inside Darktrace’s Botnet Detection

Unmasking Vo1d: Inside Darktrace’s Botnet DetectionDefault blog imageDefault blog image

What is Vo1d APK malware?

Vo1d malware first appeared in the wild in September 2024 and has since evolved into one of the most widespread Android botnets ever observed. This large-scale Android malware primarily targets smart TVs and low-cost Android TV boxes. Initially, Vo1d was identified as a malicious backdoor capable of installing additional third-party software [1]. Its functionality soon expanded beyond the initial infection to include deploying further malicious payloads, running proxy services, and conducting ad fraud operations. By early 2025, it was estimated that Vo1d had infected 1.3 to 1.6 million devices worldwide [2].

From a technical perspective, Vo1d embeds components into system storage to enable itself to download and execute new modules at any time. External researchers further discovered that Vo1d uses Domain Generation Algorithms (DGAs) to create new command-and-control (C2) domains, ensuring that regardless of existing servers being taken down, the malware can quickly reconnect to new ones. Previous published analysis identified dozens of C2 domains and hundreds of DGA seeds, along with new downloader families. Over time, Vo1d has grown increasingly sophisticated with clear signs of stronger obfuscation and encryption methods designed to evade detection [2].

Darktrace’s coverage

Earlier this year, Darktrace observed a surge in Vo1d-related activity across customer environments, with the majority of affected customers based in South Africa. Devices that had been quietly operating as expected began exhibiting unusual network behavior, including excessive DNS lookups. Open-source intelligence (OSINT) has long highlighted South Africa as one of the countries most impacted by Vo1d infections [2].

What makes the recent activity particularly interesting is that the surge observed by Darktrace appears to be concentrated specifically in South African environments. This localized spike suggests that a significant number of devices may have been compromised, potentially due to vulnerable software, outdated firmware, or even preloaded malware. Regions with high prevalence of low-cost, often unpatched devices are especially susceptible, as these everyday consumer electronics can be quietly recruited into the botnet’s network. This specifically appears to be the case with South Africa, where public reporting has documented widespread use of low-cost boxes, such as non-Google-certified Android TV sticks, that frequently ship with outdated firmware [3].

The initial triage highlighted the core mechanism Vo1d uses to remain resilient: its use of DGA. A DGA deterministically creates a large list of pseudo-random domain names on a predictable schedule. This enables the malware to compute hundreds of candidate domains using the same algorithm, instead of using a hard-coded single C2 hostname that defenders could easily block or take down. To ensure reproducible from the infected device’s perspective, Vo1d utilizes DGA seeds. These seeds might be a static string, a numeric value, or a combination of underlying techniques that enable infected devices to generate the same list of candidate domains for a time window, provided the same DGA code, seed, and date are used.

Interestingly, Vo1d’s DGA seeds do not appear to be entirely unpredictable, and the generated domains lack fully random-looking endings. As observed in Figure 1, there is a clear pattern in the names generated. In this case, researchers identified that while the first five characters would change to create the desired list of domain names, the trailing portion remained consistent as part of the seed: 60b33d7929a, which OSINT sources have linked to the Vo1d botnet. [2]. Darktrace’s Threat Research team also identified a potential second DGA seed, with devices in some cases also engaging in activity involving hostnames matching the regular expression /[a-z]{5}fc975904fc9\.(com|top|net). This second seed has not been reported by any OSINT vendors at the time of writing.

Another recurring characteristic observed across multiple cases was the choice of top-level domains (TLDs), which included .com, .net, and .top.

Figure 1: Advanced Search results showing DNS lookups, providing a glimpse on the DGA seed utilized.

The activity was detected by multiple models in Darktrace / NETWORK™, which triggered on devices making an unusually large volume of DNS requests for domains uncommon across the network.

During the network investigation, Darktrace analysts traced Vo1d’s infrastructure and uncovered an interesting pattern related to responder ASNs. A significant number of connections pointed to AS16509 (AMAZON-02). By hosting redirectors or C2 nodes inside major cloud environments, Vo1d is able to gain access to highly available and geographically diverse infrastructure. When one node is taken down or reported, operators can quickly enable a new node under a different IP within the same ASN. Another feature of cloud infrastructure that hardens Vo1d’s resilience is the fact that many organizations allow outbound connections to cloud IP ranges by default, assuming they are legitimate. Despite this, Darktrace was able to identify the rarity of these endpoints, identifying the unusualness of the activity.

Analysts further observed that once a generated domain successfully resolved, infected devices consistently began establishing outbound connections to ephemeral port ranges like TCP ports 55520 and 55521. These destination ports are atypical for standard web or DNS traffic. Even though the choice of high-numbered ports appears random, it is likely far from not accidental. Commonly used ports such as port 80 (HTTP) or 443 (HTTPS) are often subject to more scrutiny and deeper inspection or content filtering, making them riskier for attackers. On the other hand, unregistered ports like 55520 and 55521 are less likely to be blocked, providing a more covert channel that blends with outbound TCP traffic. This tactic helps evade firewall rules that focus on common service ports. Regardless, Darktrace was able to identify external connections on uncommon ports to locations that the network does not normally visit.

The continuation of the described activity was identified by Darktrace’s Cyber AI Analyst, which correlated individual events into a broader interconnected incident. It began with the multiple DNS requests for the algorithmically generated domains, followed by repeated connections to rare endpoints later confirmed as attacker-controlled infrastructure. Cyber AI Analyst’s investigation further enabled it to categorize the events as part of the “established foothold” phase of the attack.

Figure 2: Cyber AI Analyst incident illustrating the transition from DNS requests for DGA domains to connections with resolved attacker-controlled infrastructure.

Conclusion

The observations highlighted in this blog highlight the precision and scale of Vo1d’s operations, ranging from its DGA-generated domains to its covert use of high-numbered ports. The surge in affected South African environments illustrate how regions with many low-cost, often unpatched devices can become major hubs for botnet activity. This serves as a reminder that even everyday consumer electronics can play a role in cybercrime, emphasizing the need for vigilance and proactive security measures.

Credit to Christina Kreza (Cyber Analyst & Team Lead) and Eugene Chua (Principal Cyber Analyst & Team Lead)

Edited by Ryan Traill (Analyst Content Lead)

Appendices

Darktrace Model Detections

  • Anomalous Connection / Devices Beaconing to New Rare IP
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / DGA Beacon
  • Compromise / Domain Fluxing
  • Compromise / Fast Beaconing to DGA
  • Unusual Activity / Unusual External Activity

List of Indicators of Compromise (IoCs)

  • 3.132.75[.]97 – IP address – Likely Vo1d C2 infrastructure
  • g[.]sxim[.]me – Hostname – Likely Vo1d C2 infrastructure
  • snakeers[.]com – Hostname – Likely Vo1d C2 infrastructure

Selected DGA IoCs

  • semhz60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
  • ggqrb60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
  • eusji60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
  • uacfc60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
  • qilqxfc975904fc9[.]top – Hostname – Possible Vo1d C2 DGA endpoint

MITRE ATT&CK Mapping

  • T1071.004 – Command and Control – DNS
  • T1568.002 – Command and Control – Domain Generation Algorithms
  • T1568.001 – Command and Control – Fast Flux DNS
  • T1571 – Command and Control – Non-Standard Port

[1] https://news.drweb.com/show/?lng=en&i=14900

[2] https://blog.xlab.qianxin.com/long-live-the-vo1d_botnet/

[3] https://mybroadband.co.za/news/broadcasting/596007-warning-for-south-africans-using-specific-types-of-tv-sticks.html

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content.

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About the author
Christina Kreza
Cyber Analyst
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