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January 9, 2019

Insider Analysis of Emotet Malware

Uncover the secrets of Emotet with our latest Darktrace expert analysis. Learn how to identify and understand trojan horse attacks.
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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09
Jan 2019

While both traditional security tools and the attacks against them continue to improve, advanced cyber-criminals are increasingly exploiting the weakness inherent to any organization’s security posture: its employees. Designed to mislead such employees into compromising their devices, computer trojans are now rapidly on the rise. In 2018, Darktrace detected a 239% year-on-year uptick in incidents related specifically to banking trojans, which use deception to harvest the credentials of online banking customers from infected machines. And one banking trojan in particular, Emotet, is among the costliest and most destructive malware variants currently imperilling governments and companies worldwide.

Emotet is a highly sophisticated malware with a modular architecture, installing its main component first before delivering additional payloads. Further increasing its subtlety is the fact that Emotet is considered to be ‘polymorphic malware’, since it constantly changes its identifiable features to evade detection by antivirus products. And, as will be subsequently discussed in greater detail, Emotet has advanced persistence techniques and worm-like self-propagation abilities, which render it uniquely resilient and dangerous.

Since its launch in 2014, Emotet has been adapted and repurposed on numerous occasions as its targets have diversified. Initially, Emotet’s primary victims were German banks, from which the malware was designed to steal financial information by intercepting network traffic. By this past year’s end, Emotet had spread far and wide while shifting focus to U.S. targets, resulting in permanently lost files, costly business interruptions, and serious reputational harm.

How Emotet works

(Image courtesy of US-CERT)

Emotet is spread by targeting Windows-based systems via sophisticated phishing campaigns, employing social engineering techniques to fool users into believing that the malware-laden emails are legitimate. For instance, the latest versions of Emotet were delivered by way of Thanksgiving-related emails, which invited their American recipients to open an apparently innocuous Thanksgiving card:

These emails contain Microsoft Word documents that are either linked or attached directly. The Word files, in turn, act as vectors for malicious macros, which must be explicitly enabled by the user to be executed. For security reasons, running macros by default is disabled in most of the latest Microsoft application versions, meaning that the cyber-criminals responsible must resort to tricking users in order to enable them — in this case, by enticing them with the Thanksgiving card.

Once the macros are enabled, the Word file is executed and a PowerShell command is activated to retrieve the main Emotet component from compromised servers. The trojan payload is then downloaded and executed into the victim’s system. As mentioned above, Emotet payloads are polymorphic, often allowing them to slip past conventional security tools undetected.

How Emotet persists and propagates

Once Emotet has been executed on the victim’s device, it begins deploying itself with two main objectives: (1) achieving persistence and (2) spreading to more machines. To achieve the first aim, which involves resisting a reboot and various attempts at removal, Emotet does the following:

  • Creates scheduled tasks and registry key entries, ensuring its automatic execution during every system start-up.
  • Registers itself by creating files that have randomly generated names in system root directories, which are run as Windows services.
  • Typically stores payloads in paths located off AppData\Local and AppData\Roaming directories that it masks with names that appear legitimate, such as ‘flashplayer.exe’.

Emotet’s second key goal is that of spreading across local networks and beyond in order to infect as many machines as possible. To this end, Emotet first gathers information on both the victim’s system itself and the operating system it uses. Following this reconnaissance stage, it establishes encrypted command and control communications (C2) with its parent infrastructure before determining which payloads it will deliver. After reporting a new infection, Emotet downloads modules from the C2 servers, including:

  • WebBrowserPassView: A tool that steals passwords from most common web browsers like Chrome, Safari, Firefox and Internet Explorer.
  • NetPass.exe: A legitimate tool that recovers all the network passwords stored on the system for the current logged-on user.
  • MailPassView: A tool that reveals passwords and account details for popular email clients, such as Hotmail, Gmail, Microsoft Outlook, and Yahoo! Mail.
  • Outlook PST scraper: A module that searches Outlook’s messages to obtain names and email addresses from the victim’s Outlook account.
  • Credential enumerator: A module that enumerates network resources and attempts to gain access to other machines via SMB enumeration and brute-forcing connections.
  • Banking trojans: These include Dridex, IceID, Zeus Panda, Trickbot and Qakbot, all of which harvest banking account information via browser monitoring routines.

Whilst the WebBrowserPassView, NetPass.exe and MailPassView modules are able to steal the compromised user’s credentials, the PST scraper module can ransack the user’s contact list of friends, family members, colleagues and clients, enabling Emotet to self-propagate by sending phishing emails to those contacts. And because such emails are sent from the hijacked accounts of known acquaintances and loved ones, their recipients are more likely to open their infected attachments and links.

Emotet’s other self-propagation method is via brute-forcing credentials using various password lists, with the intent of gaining access to other machines within the network. When unsuccessful, the malware’s repeated failed login attempts can cause users to become locked out of their accounts, and when successful, the victims may become infected without even clicking on a malicious link or attachment. These tactics have collectively made Emotet remarkably durable and widespread. Indeed, in line with Darktrace’s discovery that incidents related to banking trojans have increased by 239% from 2017 to 2018, Emotet alone recorded a 39% increase, and the worst may be yet to come.

How AI fights back

Emotet presents significant challenges for traditional security tools, both because it exploits the ubiquitous vulnerability of human error, and because it is designed specifically to bypass endpoint solutions. Yet unlike such traditional tools, Darktrace leverages unsupervised machine learning algorithms to detect cyber-threats that have already infiltrated the network. Modelled after the human immune system, Darktrace AI works by learning the individual ‘pattern of life’ of every user, device, and network that it safeguards. From this ever-evolving sense of ‘self,’ Darktrace can differentiate between normal and anomalous behavior, allowing it to identify cyber-attacks in much the same way that our immune system spots harmful germs.

Recently, Darktrace’s AI models managed to detect a machine on a clients’ network that was experiencing active signs of an Emotet infection. The device was observed downloading a suspicious file and, shortly thereafter, began beaconing to a rare external destination, likely reporting the infection to a C2 server.

The device was then observed moving laterally across the network by performing brute force activities. In fact, Darktrace detected thousands of Kerberos failed logins, including to administrative accounts, as well as multiple SMB session failures that used a range of common usernames, such as ‘admin’ and ‘exchange’. Below is a graph showing the SMB and Kerberos brute-force activity on the breached device:

In addition to the brute-forcing activity performed by the credential enumerator module, Darktrace also detected another payload that was potentially functioning as an email spammer. The infected machine started to make a high number of outgoing connections over common email ports. This activity is consistent with Emotet’s typical spreading behavior, which revolves around sending emails to the victim’s hijacked email contacts. Below is an image of Darktrace models breached during the reported Emotet infection:

By forming a comprehensive understanding of normalcy, Darktrace can flag even the most minute anomalies in real time, thwarting subtle threats like Emotet that have already circumvented the network perimeter. To counter such advanced banking trojans, cyber AI defenses like Darktrace have become an organizational necessity.

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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October 21, 2026

Beyond the Fridge: Exposed Industrial Control Systems

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Introduction

In October 2026, hundreds of supermarkets across France were reportedly affected by a cyberattack targeting their refrigeration systems. According to public reports, the attackers gained access to internet-connected controllers and modified their settings, forcing refrigeration units into high and extremely low temperatures and causing significant disruption. Total losses have been reported to reach several million euros.

The incident drew attention to the security of industrial refrigeration systems, particularly older devices with exposed management interfaces and known vulnerabilities.

The exact details of how the attackers gained access have not been confirmed, however it is speculated that the public-facing web server could be accessed with the username “admin” and password being the current date. Researchers from Claroty’s Team82 identified a vulnerability in Danfoss AK-SM 8xxA Series in 2025, “CVE-2025-41450", allowing for administrative access using a time-based “code-of-the-day" for authentication [1].

Default credentials, outdated firmware and publicly accessible administrative interfaces have been an issue for years. And yet these systems continue to appear in internet scans and require little sophistication to gain admin access. A simple search of internet-connected devices shows a range of exposed devices from Programmable Logic Controllers (PLCs) and remote gateways, to, yes, more fridges.

Darktrace malware researchers identified hundreds of IoT and OT devices publicly exposed on the internet, many of which are configured with no authentication, weak credentials, or default credentials. As shown in Figure 1, an exposed Copeland XWEB web interface, used to manage Dixell equipment, including refrigerators, can provide the ability to remotely control connected devices.

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Figure 1: Copeland XWEB interface used to control systems including refrigerators and air conditioning.

However, it’s not only fridges; web management interfaces for industrial controls are also publicly accessible, often protected only by weak credentials. Figure 2 shows an example of a publicly exposed WAGO interface. The WAGO management interface is a configuration and administration panel for controllers. Through it, a logged-in user can view device details and diagnostics, manage user accounts and passwords, update firmware, back up or restore the full configuration, set the clock, and manage SD card storage. While it typically doesn’t manage switch outputs or read live process values, someone with access could still disrupt operation by changing network or Modbus settings, loading bad firmware or configurations, or by resetting passwords and enabling insecure services.

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Figure 2: WAGO Management interface.

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The issue extends beyond management interfaces. During our research, we identified publicly accessible web interfaces belonging to PLCs, including Rockwell Automation Allen-Bradley devices. In each case, the risk arises from how the devices have been deployed and configured, not from a flaw in the vendors’ products.

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Figure 3: Web interface for Allen-Bradley CompactLogix controller.

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Figure 3 shows the web interface of an Allen-Bradley CompactLogix controller. Unlike the previous examples, this interface provides a view into the controller's operation, including running tasks, network connections, memory usage and diagnostic information. Rockwell Automation's CompactLogix controllers are commonly used to automate industrial processes, from manufacturing and packaging lines to water treatment and material handling systems. Depending on their configuration, these controllers can operate motors, pumps, valves and other physical equipment.

Although in this example the interface shown does not provide direct control over the PLC's logic, it exposes information about the model, firmware, network layout, and connections, which is useful reconnaissance for a bigger attack.

The concern is not necessarily that every exposed interface allows an attacker to immediately manipulate industrial processes. However, these devices are accessible from the public internet in the first place, providing information about systems that may be responsible for controlling physical equipment.

The attacks on refrigeration systems in France highlight the consequences of poorly secured industrial devices. As demonstrated throughout this research, publicly accessible management interfaces are not limited to refrigeration controllers but extend to PLCs and other systems used to manage industrial equipment.

Finding these devices requires little effort. Internet scanners make it possible to identify publicly exposed interfaces quickly, and in some cases these interfaces rely on publicly documented default credentials or have no authentication at all. As attackers increasingly use AI tools, such attacks may be identified and carried out faster and at a greater scale.

The consequences of this access depend on the device and the functionality exposed. While some interfaces provide little more than diagnostic information, others allow users to modify configurations, disrupt communications, or change settings that affect physical equipment. In industrial environments, this can result in operational downtime, damaged equipment and financial losses.

Weak passwords, default credentials and exposed administrative interfaces are not new security issues. Yet their continued presence on internet-connected industrial systems demonstrates how easily preventable weaknesses can leave physical operations vulnerable to attack.

Exposed devices often come from third-party suppliers and contractors, who frequently install refrigeration controllers, gateways and PLCs without the organization ever being aware they are connected to the internet. Asset visibility remains an important foundation, but organizations should assume that some unknown assets and services will exist. Effective security therefore also depends on continuous monitoring, anomaly detection, network segmentation and rapid response, so that risky exposure and unusual activity can be identified and addressed before it becomes disruptive.

Darktrace helps security teams gain visibility across IT and OT and respond to risks: https://www.darktrace.com/products/operational-technology.

Copeland, Dixell, WAGO, Rockwell Automation, Allen-Bradley and CompactLogix are trademarks of their respective owners. Their use here is for identification only and does not imply any affiliation with or endorsement by those owners.

References

1.        https://claroty.com/team82/research/freeze-the-controller-defrost-the-food-uncovering-vulnerabilities-in-danfoss-refrigeration-controllers

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About the author
Tara Gould
Malware Research Lead

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October 20, 2026

Why Kubernetes Security Needs A Behavioral Approach

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Kubernetes has become a foundational technology for modern cloud-native infrastructure. It can orchestrate everything from customer-facing applications to data pipelines and, increasingly, AI workloads. This is due to the highly scalable nature of Kubernetes, making it cost efficient and reliable.

Gartner projects that by 2028, 95% of new AI deployments will run on Kubernetes, up from less than 30% today1. However, the same qualities that make Kubernetes powerful also make it uniquely challenging to secure, leaving many organizations with limited visibility into their clusters. This lack of visibility increases risk - it’s difficult to secure what you can’t see.

However, this risk is often looked at as purely a posture management challenge. While Cloud Security Posture Management (CSPM) solutions can tell you how a cluster should be configured, they don’t tell you what it is doing in real time. This requires runtime behavioral monitoring of clusters to detect active threats.

Yet existing approaches to securing Kubernetes by most Cloud Detection and Response (CDR) and Network Detection and Response (NDR) vendors often have limited coverage or require a third-party integration to forward traffic and metadata. This means that capabilities can be limited and detections can lack the context of the wider environment, which slows down triage, investigation and response.

Why Kubernetes is hard to secure

Traditional security tooling was built for persistent, long-term infrastructure such as servers and virtual machines with fixed identities that last months or years. Some elements of Kubernetes environments can have a much shorter lifespan, breaking many assumptions those tools rely on:

  1. Increased complexity
    1. Kubernetes environments can be complex in comparison to traditional infrastructure, with many different elements to consider such as nodes, clusters, containers, pods and the workloads themselves.
  2. Ephemeral by design
    1. Kubernetes pods can spin up and terminate in seconds. A container that was compromised may no longer exist by the time a deeper investigation is required, leaving no artifact to analyze. This poses a unique challenge for security operations.
  3. Weaker workload boundaries
    1. Kubernetes containers use lightweight, shared-kernel isolation rather than separate virtual machines, improving efficiency but reducing separation between workloads and the host. As a result, a compromised container may have a greater potential impact on the node and neighboring workloads than a compromised virtual machine (VM).2
    2. Recent vulnerabilities have shown that threat actors can escape a container and gain access to the underlying host.3, 4
  4. Everything is API-driven
    1. Dozens or hundreds of microservices communicate over internal APIs, and the Kubernetes control plane itself is an API. This creates a large and dynamic attack surface, making it difficult to distinguish legitimate activity from malicious behavior.
    2. Without identity-aware runtime visibility, threat actors can move laterally between pods and exploit misconfigured role-based access controls (RBAC) with little chance of detection.

Rising to the challenge of securing Kubernetes

With the release of Darktrace 7.2, Darktrace / HYBRID NETWORK can extend behavioral security into Kubernetes, delivering visibility, detection and response capabilities across runtime activity. Darktrace addresses the challenges of securing Kubernetes environments in several ways.

Behavioral detection, not static rules

Many Kubernetes runtime tools rely on predefined rules and signatures - a fixed list of “bad” behaviors to match against. That approach struggles with novel attacks and generates noise at scale. Darktrace takes a different approach: our Adaptive AI builds a Unique Behavioral Profile for each organization from the ground up, learning the normal behavior of every workload, node and identity in your cluster. It then uses this understanding to detect meaningful deviations from expected behavior, which serves as the basis for surfacing suspicious and malicious activity.  

A pod reaching out to an unfamiliar destination, an unexpected executable launching inside a workload, or an identity performing an action it has never performed, all surface as anomalies without anyone having to write a rule in advance.

Darktrace surfaces unexpected Kubernetes activity that does not align with an organization’s Unique Behavioral Profile

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Visibility across the network, the process, and the control plane

Darktrace monitors Kubernetes at three levels that together provide comprehensive runtime visibility:

  1. Network-layer
    1. Traffic between pods, across nodes and out to the internet. This helps to detect lateral movement and unusual connectivity, whether internal or external.
  2. Process-layer
    1. What is executing inside your containers. If a process reads a sensitive credential file or spawns an unexpected shell inside a production pod, Darktrace can identify that activity and use it as context for further detections.
  3. Control plane
    1. Activity against the Kubernetes API itself: someone executes a command in a running pod, RBAC changes, or unusual administrative actions. Many high-impact security events involve the control plane, an area that is often less visible to solutions that only analyze or scan network-layer telemetry.

Misconfiguration and vulnerability awareness

Runtime detection is strongest when combined with posture and exposure management. Darktrace surfaces risky pod configurations, such as privileged containers, host networking, and over-permissive service accounts as workloads are deployed, and links CVEs from images to the workloads running them. Security teams can go beyond simply knowing that a vulnerability exists and see exactly where it is in the cluster. When combined with behavior-based detections, this provides invaluable context that can help prioritize incidents and proactively identify areas that need further attention. For example, anomalous workload behavior on its own might not be very interesting to a security team at first, but if that same workload is known to host sensitive data, is known to have critical vulnerabilities associated with it, or is publicly accessible, then this narrative becomes much more suspicious and demanding of their attention.

Darktrace highlights Kubernetes misconfigurations such as privileged containers, host networking, and missing security controls, helping teams prioritize runtime alerts with valuable exposure and risk context

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Response, tailored to Kubernetes

Detection is most effective when paired with response. Darktrace can take precise actions on Kubernetes workloads, including blocking a malicious connection at the pod level, helping to safely contain threats while security teams investigate. Response actions can be taken fully autonomously or via a human-in-the-loop process, which is completely configurable depending on the desired level of autonomy and scope. Darktrace is expanding the number of Kubernetes-native response actions to give customers even more flexibility when containing threats.

Forensics that survive the workload

Because containers are so short-lived, evidence disappears fast and this can make it very difficult to answer questions related to the initial detections. Darktrace solves this with Forensic Acquisition & Investigation capabilities that extend to Kubernetes containers, allowing investigations to be triggered manually by an analyst, or automatically from an alert, preserving evidence before the workload is gone.

For incident responders, this addresses a key challenge in container security: being able to answer “what actually happened?” after an ephemeral workload has vanished.

One platform, not another silo

Kubernetes doesn't exist in isolation, and neither should its security. Darktrace correlates cluster activity with the surrounding cloud environment, including AWS, Azure and GCP identities, network activity and cloud assets, allowing analysts to pivot between a Kubernetes workload and its wider context. A single anomalous action can be traced from the control plane to the pod, and from the pod to the cloud identity behind it, all within a single investigation workflow. This can also include alerts from other areas of hybrid infrastructure or third-party sources where relevant, adding more context to investigations and speeding up workflows for human analysts.

Built to deploy easily

Operational complexity can be a barrier to security adoption. Darktrace / HYBRID NETWORK for Kubernetes installs with a single command, and for teams that manage their infrastructure as code, it supports raw manifests so the deployment can live in a Git repository and be managed through existing GitOps workflows. The same deployment approach works across Kubernetes environments in AWS, Azure, Google Cloud, and on-premises infrastructure. Early adopters of Darktrace’s Kubernetes capabilities have highlighted the simplicity of deployment and time to value.

Darktrace can deploy across your Kubernetes environment and wider hybrid infrastructure, while also providing visibility into Kubernetes deployment structure and status

Next steps

Kubernetes environments are dynamic, ephemeral and API-driven in ways that challenge traditional security tooling. With Kubernetes usage increasing due to widespread AI adoption, it is essential to secure Kubernetes environments as part of securing what AI can do. Darktrace / HYBRID NETWORK addresses these challenges by learning normal behavior in Kubernetes environments across network, process and control-plane layers, identifying meaningful threats and enabling targeted response. Combined with the context of wider hybrid infrastructure and forensic investigation capabilities, Darktrace provides visibility into both emerging threats and active incidents.

To see how Darktrace can help secure your Kubernetes environment and your wider hybrid infrastructure, get in touch to arrange your free trial. Existing customers can contact their Darktrace Account Team for more information about deployment options and licensing.

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Sources

[1] Gartner, Container Management research, 2025: "By 2028, 95% of new AI deployments will use Kubernetes, up from less than 30% today."  https://www.gartner.com/en/documents/7913109. (quote reproduced under Gartner reprint at https://www.kubermatic.com/magic-quadrant-and-critical-capabilities-for-container-management/)

[2] Palo Alto Networks (Unit 42) – Container Breakouts: Escape Techniques in Cloud Environments. https://unit42.paloaltonetworks.com/container-escape-techniques/

[3] runC container-escape vulnerabilities (CVE-2025-31133, CVE-2025-52565, CVE-2025-52881), disclosed by a SUSE researcher on November 5, 2025. https://www.securityweek.com/runc-vulnerabilities-can-be-exploited-to-escape-containers/

[4] NVIDIAScape (CVE-2025-23266), CVSS 9.0. https://nvidia.custhelp.com/app/answers/detail/a_id/5659

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About the author
Elise Longhurst
Senior Product Executive
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