Blog
/
/
May 18, 2021

The Dangers of Double Extortion Ransomware Attacks

Learn about the latest trend in ransomware attacks known as double extortion. Discover how Darktrace can help protect your organization from this threat.
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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager
Default blog image
18
May 2021

A year and a half ago, ‘double extortion’ ransomware was being used by only one known threat actor. Now, over 16 ransomware groups actively utilize this tactic. So, what is it, and why has it become so popular?

What is double extortion ransomware?

The traditional story of ransomware was one of malicious code rapidly encrypting files with public-key RSA encryption, and then deleting those files if the victim did not pay the ransom.

However, after the infamous WannaCry and NotPetya ransomware campaigns over 2017, companies ramped up their cyber defense. More emphasis was placed on backups and restoration processes, so that even if files were destroyed, organizations had copies in place and could easily restore their data.

Yet in turn, cyber-criminals have also adapted their techniques. Now, rather than just encrypting files, double extortion ransomware exfiltrates the data first. This means that if the company refuses to pay up, information can be leaked online or sold to the highest bidder. Suddenly, all those backups and data recovery plans became worthless.

Maze ransomware and friends

In late 2019, Maze ransomware emerged as the first high-profile case of double extortion. Other strains soon followed, with the Sodinokibi attack — which crippled foreign exchange company Travelex — occurring on the final day of that year.

By mid-2020, hundreds of organizations were falling victim to double extortion attacks, various websites on the dark net were leaking company data, and the Ransomware-as-a-Service business was booming as developers sold and rented new types of malware.

Furthermore, cyber security regulations started being weaponized by cyber-criminals who could leverage the threat of having to pay a hefty compliance fine (CCPA, GDPR, NYSDFS regulations) to encourage their victims to keep quiet by offering them a ransom smaller than the penalty fee.

There were 1,200 double extortion ransomware incidents in 2020, across 63 countries, with over 60% of these aimed at the US and the UK.

Despite new legislation being written regularly to try and mitigate these attacks, they aren’t slowing down. According to a recent study by RUSI, there were 1,200 double extortion ransomware incidents in 2020 alone, across 63 different countries. 60% of these were aimed at organizations headquartered in the US, and the UK suffered the second highest number of breaches.

Last month, the cyber-criminal gang known as REvil released details about Apple’s new Macbook Pro on their site ‘Happy Blog’, threatening to release more blueprints and demanding a ransom of $50 million. And last week, Colonial Pipeline purportedly paid $5 million in bitcoin to recover from a devastating OT ransomware attack.

Anatomy of a double extortion ransomware attack

Darktrace has detected a huge upsurge in double extortion ransomware threats in the last year, most recently at an energy company based in Canada. The hackers had clearly done their homework, tailoring the attack to the company and moving quickly and stealthily once inside. Below is a timeline of this real-world incident, which was mostly carried out in the space of 24 hours.

Figure 1: A timeline of the attack

Darktrace detected every stage of the intrusion and notified the security team with high-priority alerts. If Darktrace Antigena had been active in the environment, the compromised server would have been isolated as soon as it began to behave anomalously, preventing the infection from spreading.

Encryption and exfiltration

The initial infection vector is not known, but the admin account was compromised most likely from a phishing link or a vulnerability exploit. This is indicative of a trend away from the widespread ‘spray and pray’ ransomware campaigns of the last decade, towards a more targeted approach.

Cyber AI identified an internal server engaging in unusual network scanning and attempted lateral movement using the Remote Desktop Protocol (RDP). Compromised admin credentials were used to spread rapidly from the server to another internal device, ‘serverps’.

The device ‘serverps’ initiated an outbound connection to TeamViewer, a legitimate file storage service, which was active for nearly 21 hours. This connection was used for remote control of the device and to facilitate the further stages of attack. Although TeamViewer was not in wide operation in the company’s digital environment, it was not blocked by any of the legacy defenses.

The device then connected to an internal file server and downloaded 1.95 TB of data, and uploaded the same volume of data to pcloud[.]com. This exfiltration took place during work hours to blend in with regular admin activity.

The device was also seen downloading Rclone software – an open source tool, which was likely applied to sync data automatically to the legitimate file storage service pCloud.

The compromised admin credential allowed the threat actor to move laterally during this time. Following the completion of the data exfiltration, the device ‘serverps’ finally began encrypting files on 12 devices with the extension *.06d79000.

As with the majority of ransomware incidents, the encryption happened outside of office hours – overnight in local time – to minimize the chance of the security team responding quickly.

AI-powered investigation

Cyber AI Analyst reported on four incidents related to the attack, highlighting the suspicious behavior to the security team and providing a report on the affected devices for immediate remediation. Such concise reporting allowed the security team to quickly identify the scope of the infection and respond accordingly.

Figure 2: Cyber AI Analyst incident tray for a week

Cyber AI Analyst investigates on demand

Following further analysis on March 13, the security team employed Cyber AI Analyst to conduct on-demand investigations into the compromised admin credential in Microsoft 365, as well as another device which was identified as a potential threat.

Cyber AI Analyst created an incident for this other device, which resulted in the identification of unusual port scanning during the time period of infection. The device was promptly removed from the network.

Figure 3: Cyber AI Analyst incident for a compromised device, detailing an unusual internal download

Double trouble

The use of legitimate tools and ‘Living off the Land’ techniques (using RDP and a compromised admin credential) allowed the threat actors to carry out the bulk of the attack in less than 24 hours. By exploiting TeamViewer as a legitimate file storage solution for the data exfiltration, as opposed to relying on a known ‘bad’ or recently registered domain, the hackers easily circumvented all the existing signature-based defenses.

If Darktrace had not detected this intrusion and immediately alerted the security team, the attack could have resulted not only in a ‘denial of business’ with employees locked out of their files, but also in sensitive data loss. The AI went a step further in saving the team vital time with automatic investigation and on-demand reporting.

There is so much more to lose from double extortion ransomware. Exfiltration provides another layer of risk, leading to compromised intellectual property, reputational damage, and compliance fines. Once a threat group has your data, they might easily ask for more payments down the line. It is important therefore to defend against these attacks before they happen, proactively implementing cyber security measures that can detect and autonomously respond to threats as soon as they emerge.

Learn more about double extortion ransomware.

Darktrace model detections:

  • Device / Suspicious Network Scan Activity
  • Device / RDP Scan
  • Device / Network Scan
  • Anomalous Connection / Unusual Admin SMB Session
  • Anomalous Connection / Unusual Admin RDP Session
  • Device / Multiple Lateral Movement Model Breaches
  • User / New Admin Credentials on Client
  • Anomalous Connection / Uncommon 1 GiB Outbound
  • Anomalous Connection / Low and Slow Exfiltration
  • Device / Anomalous SMB Followed By Multiple Model
  • Anomalous Connection / Download and Upload
  • Anomalous Connection / Suspicious Activity On High Risk Device
  • Anomalous File / Internal::Additional Extension Appended to SMB File
  • Compromise / Ransomware::Suspicious SMB Activity
  • Anomalous Connection / Sustained MIME Type Conversion
  • Device / Anomalous RDP Followed By Multiple Model Breaches
  • Anomalous Connection / Suspicious Read Write Ratio
  • Device / Large Number of Model Breaches
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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager

More in this series

No items found.

Blog

/

OT

/

October 21, 2026

Beyond the Fridge: Exposed Industrial Control Systems

Default blog imageDefault blog image

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.

‍

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.

‍

Figure 2: WAGO Management interface.

‍

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.

‍

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

‍

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

‍

Continue reading
About the author
Tara Gould
Malware Research Lead

Blog

/

Network

/

October 20, 2026

Why Kubernetes Security Needs A Behavioral Approach

Man at computerDefault blog imageDefault blog image

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

‍

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

‍

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.

‍

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

[related-resource]

‍

Continue reading
About the author
Elise Longhurst
Senior Product Executive
Your data. Our AI.
Elevate your network security with Darktrace AI