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December 6, 2023

How Darktrace Triumphed Over MyKings Botnet

Darktrace has provided full visibility over the MyKings botnet kill chain from the beginning of its infections to the eventual cryptocurrency mining activity.
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
Oluwatosin Aturaka
Analyst Team Lead, Cambridge
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06
Dec 2023

Botnets: A persistent cyber threat

Since their appearance in the wild over three decades ago, botnets have consistently been the attack vector of choice for many threat actors. The most prevalent of these attack vectors are distributed denial of service (DDoS) and phishing campaigns. Their persistent nature means that even if a compromised device in identified, attackers can continue to operate by using the additional compromised devices they will likely have on the target network. Similarly, command and control (C2) infrastructure can easily be restructured between infected systems, making it increasingly difficult to remove the infection.  

MyKings Botnet

One of the most prevalent and sophisticated examples in recent years is the MyKings botnet, also known as Smominru or DarkCloud. Darktrace has observed numerous cases of MyKings botnet compromises across multiple customer environments in several different industries as far back as August 2022. The diverse tactics, techniques, and procedures (TTPs) and sophisticated kill chains employed by MyKings botnet may prove a challenge to traditional rule and signature-based detections.

However, Darktrace’s anomaly-centric approach enabled it to successfully detect a wide-range of indicators of compromise (IoCs) related to the MyKings botnet and bring immediate awareness to customer security teams, as it demonstrated on the network of multiple customers between March and August 2023.

Background on MyKings Botnet

MyKings has been active and spreading steadily since 2016 resulting in over 520,000 infections worldwide.[1] Although verified attribution of the botnet remains elusive, the variety of targets and prevalence of crypto-mining software on affected devices suggests the threat group behind the malware is financially motivated. The operators behind MyKings appear to be highly opportunistic, with attacks lacking an obvious specific target industry. Across Darktrace’s customer base, the organizations affected were representative of multiple industries such as entertainment, mining, education, information technology, health, and transportation.

Given its longevity, the MyKings botnet has unsurprisingly evolved since its first appearance years ago. Initial analyses of the botnet showed that the primary crypto-related activity on infected devices was the installation of Monero-mining software. However, in 2019 researchers discovered a new module within the MyKings malware that enabled clipboard-jacking, whereby the malware replaces a user's copied cryptowallet address with the operator's own wallet address in order to siphon funds.[2]

Similar to other botnets such as the Outlaw crypto-miner, the MyKings botnet can also kill running processes of unrelated malware on the compromised hosts that may have resulted from prior infection.[3] MyKings has also developed a comprehensive set of persistence techniques, including: the deployment of bootkits, initiating the botnet immediately after a system reboot, configuring Registry run keys, and generating multiple Scheduled Tasks and WMI listeners.[4] MyKings have also been observed rotating tools and payloads over time to propagate the botnet. For example, some operators have been observed utilizing PCShare, an open-source remote access trojan (RAT) customized to conduct C2 services, execute commands, and download mining software[5].

Darktrace Coverage

Across observed customer networks between March and August 2023, Darktrace identified the MyKings botnet primarily targeting Windows-based servers that supports services like MySQL, MS-SQL, Telnet, SSH, IPC, WMI, and Remote Desktop (RDP).  In the initial phase of the attack, the botnet would initiate a variety of attacks against a target including brute-forcing and exploitation of unpatched vulnerabilities on exposed servers. The botnet delivers a variety of payloads to the compromised systems including worm downloaders, trojans, executable files and scripts.

This pattern of activity was detected across the network of one particular Darktrace customer in the education sector in early March 2023. Unfortunately, this customer did not have Darktrace RESPOND™ deployed on their network at the time of the attack, meaning the MyKings botnet was able to move through the cyber kill chain ultimately achieving its goal, which in this case was mining cryptocurrency.

Initial Access

On March 6, Darktrace observed an internet-facing SQL server receiving an unusually large number of incoming MySQL connections from the rare external endpoint 171.91.76[.]31 via port 1433. While it is not possible to confirm whether these suspicious connections represented the exact starting point of the infection, such a sudden influx of SQL connection from a rare external endpoint could be indicative of a malicious attempt to exploit vulnerabilities in the server's SQL database or perform password brute-forcing to gain unauthorized access. Given that MyKings typically spreads primarily through such targeting of internet-exposed devices, the pattern of activity is consistent with potential initial access by MyKings.[6]

Initial Command and Control

The device then proceeded to initiate a series of repeated HTTP connections between March 6 and March 10, to the domain www[.]back0314[.]ru (107.148.239[.]111). These connections included HTTP GET requests featuring URIs such as ‘/back.txt',  suggesting potential beaconing and C2 communication. The device continued this connectivity to the external host over the course of four days, primarily utilizing destination ports 80, and 6666. While port 80 is commonly utilized for HTTP connections, port 6666 is a non-standard port for the protocol. Such connectivity over non-standard ports can indicate potential detection evasion and obfuscation tactics by the threat actors.  During this time, the device also initiated repeated connections to additional malicious external endpoints with seemingly algorithmically generated hostnames such as pc.pc0416[.]xyz.

Darktrace UI image
Figure 1: Model breach showing details of the malicious domain generation algorithm (DGA) connections.

Tool Transfer

While this beaconing activity was taking place, the affected device also began to receive potential payloads from unusual external endpoints. On April 29, the device made an HTTP GET request for “/power.txt” to the endpoint 192.236.160[.]237, which was later discovered to have multiple open-source intelligence (OSINT) links to malware. Power.txt is a shellcode written in PowerShell which is downloaded and executed with the purpose of disabling Windows Defenders related functions.[7] After the initial script was downloaded (and likely executed), Darktrace went on to detect the device making a series of additional GET requests for several varying compressed and executable files. For example, the device made HTTP requests for '/pld/cmd.txt' to the external endpoint 104.233.224[.]173. In response the external server provided numerous files, including ‘u.exe’, and ‘upsup4.exe’ for download, both of which share file names with previously identified MyKings payloads.

MyKings deploys a diverse array of payloads to expand the botnet and secure a firm position within a compromised system. This multi-faceted approach may render conventional security measures less effective due to the intricacies of and variety of payloads involved in compromises. Darktrace, however, does not rely on static or outdated lists of IoCs in order to detect malicious activity. Instead, DETECT’s Self-Learning AI allows it to identify emerging compromise activity by recognizing the subtle deviations in an affected device’s behavior that could indicate it has fallen into the hands of malicious actors.

Figure 2: External site summary of the endpoint 103.145.106[.]242 showing the rarity of connectivity to the external host.

Achieving Objectives – Crypto-Mining

Several weeks after the initial payloads were delivered and beaconing commenced, Darktrace finally detected the initiation of crypto-mining operations. On May 27, the originally compromised server connected to the rare domain other.xmrpool[.]ru over port 1081. As seen in the domain name, this endpoint appears to be affiliated with pool mining activity and the domain has various OSINT affiliations with the cryptocurrency Monero coin. During this connection, the host was observed passing Monero credentials, activity which parallels similar mining operations observed on other customer networks that had been compromised by the MyKings botnet.

Although mining activity may not pose an immediate or urgent concern for security unauthorized cryptomining on devices can result in detrimental consequences, such as compromised hardware integrity, elevated energy costs, and reduced productivity, and even potential involvement in money laundering.

Figure 3: Event breach log showing details of the connection to the other.xmrpool[.]ru endpoint associated with cryptocurrency mining activity.

Conclusion

Detecting future iterations of the MyKings botnet will likely demand a shift away from an overreliance on traditional rules and signatures and lists of “known bads”, instead requiring organizations to employ AI-driven technology that can identify suspicious activity that represents a deviation from previously established patterns of life.

Despite the diverse range of payloads, malicious endpoints, and intricate activities that constitute a typical MyKing botnet compromise, Darktrace was able successfully detect multiple critical phases within the MyKings kill chain. Given the evolving nature of the MyKings botnet, it is highly probable the botnet will continue to expand and adapt, leveraging new tactics and technologies. By adopting Darktrace’s product of suites, including Darktrace DETECT, organizations are well-positioned to identify these evolving threats as soon as they emerge and, when coupled with the autonomous response technology of Darktrace RESPOND, threats like the MyKings botnet can be stopped in their tracks before they can achieve their ultimate goals.

Credit to: Oluwatosin Aturaka, Analyst Team Lead, Cambridge, Adam Potter, Cyber Analyst

Appendix

IoC Table

IoC - Type - Description + Confidence

162.216.150[.]108- IP - C2 Infrastructure

103.145.106[.]242 - IP - C2 Infrastructure

137.175.56[.]104 - IP - C2 Infrastructure

138.197.152[.]201 - IP - C2 Infrastructure

139.59.74[.]135 - IP - C2 Infrastructure

pc.pc0416[.]xyz - Domain - C2 Infrastructure (DGA)

other.xmrpool[.]ru - Domain - Cryptomining Endpoint

xmrpool[.]ru - Domain - Cryptomining Endpoint

103.145.106[.]55 - IP - Cryptomining Endpoint

ntuser[.]rar - Zipped File - Payload

/xmr1025[.]rar - Zipped File - Payload

/20201117[.]rar - Zipped File - Payload

wmi[.]txt - File - Payload

u[.]exe - Executable File - Payload

back[.]txt - File - Payload

upsupx2[.]exe - Executable File - Payload

cmd[.]txt - File - Payload

power[.]txt - File - Payload

ups[.]html - File - Payload

xmr1025.rar - Zipped File - Payload

171.91.76[.]31- IP - Possible Initial Compromise Endpoint

www[.]back0314[.]ru - Domain - Probable C2 Infrastructure

107.148.239[.]111 - IP - Probable C2 Infrastructure

194.67.71[.]99 - IP- Probable C2 Infrastructure

Darktrace DETECT Model Breaches

  • Device / Initial Breach Chain Compromise
  • Anomalous File / Masqueraded File Transfer (x37)
  • Compromise / Large DNS Volume for Suspicious Domain
  • Compromise / Fast Beaconing to DGA
  • Device / Large Number of Model Breaches
  • Anomalous File / Multiple EXE from Rare External Locations (x30)
  • Compromise / Beacon for 4 Days (x2)
  • Anomalous Server Activity / New User Agent from Internet Facing System
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous Server Activity / New Internet Facing System
  • Anomalous File / EXE from Rare External Location (x37)
  • Device / Large Number of Connections to New Endpoints
  • Anomalous Server Activity / Server Activity on New Non-Standard Port (x3)
  • Device / Threat Indicator (x3)
  • Unusual Activity / Unusual External Activity
  • Compromise / Crypto Currency Mining Activity (x37)
  • Compliance / Internet Facing SQL Server
  • Device / Anomalous Scripts Download Followed By Additional Packages
  • Device / New User Agent

MITRE ATT&CK Mapping

ATT&CK Technique - Technique ID

Reconnaissance – T1595.002 Vulnerability Scanning

Resource Development – T1608 Stage Capabilities

Resource Development – T1588.001 Malware

Initial Access – T1190 Exploit Public-Facing Application

Command and Control – T15568.002 Domain Generated Algorithms

Command and Control – T1571 Non-Standard Port

Execution – T1047 Windows Management Instrumentation

Execution – T1059.001 Command and Scripting Interpreter

Persistence – T1542.003 Pre-OS Boot

Impact – T1496 Resource Hijacking

References

[1] https://www.binarydefense.com/resources/threat-watch/mykings-botnet-is-growing-and-remains-under-the-radar/

[2] https://therecord.media/a-malware-botnet-has-made-more-than-24-7-million-since-2019

[3] https://www.darktrace.com/blog/outlaw-returns-uncovering-returning-features-and-new-tactics

[4] https://www.sophos.com/en-us/medialibrary/pdfs/technical-papers/sophoslabs-uncut-mykings-report.pdf

[5] https://www.antiy.com/response/20190822.html

[6] https://ethicaldebuggers.com/mykings-botnet/

[7] https://ethicaldebuggers.com/mykings-botnet/

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
Oluwatosin Aturaka
Analyst Team Lead, Cambridge

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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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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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