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February 13, 2025

Why Darktrace / EMAIL Excels Against APTs

APTs are sophisticated threat actors with the resources to coordinate and achieve long-term objectives. Amidst the skyrocketing numbers of BEC attacks, every organization should be worried about the ability of intruders to infiltrate and exploit. This blog will look at several recent examples of complex email attacks and how Darktrace / EMAIL successfully disarmed and prevented intrusion.
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
Carlos Gray
Senior Product Marketing Manager, Email
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13
Feb 2025

What are APTs?

An Advanced Persistent Threat (APT) describes an adversary with sophisticated levels of expertise and significant resources, with the ability to carry out targeted cyber campaigns. These campaigns may penetrate an organization and remain undetected for long periods, allowing attackers to gather intelligence or cause damage over time.

Over the last few decades, the term APT has evolved from being almost exclusively associated with nation-state actors to a broader definition that includes highly skilled, well-resourced threat groups. While still distinct from mass, opportunistic cybercrime or "spray and pray" attacks, APT now refers to the elite tier of adversaries, whether state-sponsored or not, who demonstrate advanced capabilities, persistence, and a clear strategic focus. This shift reflects the growing sophistication of cyber threats, where non-state actors can now rival nation-states in executing covert, methodical intrusions to achieve long-term objectives.

These attacks are resource-intensive for threat actors to execute, but the potential rewards—ranging from financial gain to sensitive data theft—can be significant. In 2020, Business Email Compromise (BEC) attacks netted cybercriminals over $1.8 billion.1

And recently, the advent of AI has helped to automate launching these attacks, lowering the barriers to entry and making it more efficient to orchestrate the kind of attack that might previously have taken weeks to create. Research shows that AI can do 90% of a threat actor’s work2 – reducing time-to-target by automating tasks rapidly and avoiding errors in phishing communications. Email remains the most popular vector for initiating these sophisticated attacks, making it a critical battleground for cyber defense.

What makes APTs so successful?

The success of Advanced Persistent Threats (APTs) lies in their precision, persistence, and ability to exploit human and technical vulnerabilities. These attacks are carefully tailored to specific targets, using techniques like social engineering and spear phishing to gain initial access.

Once inside, attackers move laterally through networks, often remaining undetected for months or even years, silently gathering intelligence or preparing for a decisive strike. Alternatively, they might linger inside an account within the M365 environment, which could be even more valuable in terms of gathering information – in 2023 the average time to identify a breach in 2023 was 204 days.3‍

The subtle and long-term outlook nature of APTs makes them highly effective, as traditional security measures often fail to identify the subtle signs of compromise.

How Darktrace’s approach is designed to catch the most advanced threats

Luckily for our customers, Darktrace’s AI approach is uniquely equipped to detect and neutralize APTs. Unlike the majority of email security solutions that rely on static rules and signatures, or that train their AI on previous known-bad attack patterns, Darktrace leverages Self-Learning AI that baselines normal patterns of behavior within an organization, to immediately detect unusual activity that may signal an APT in progress.  

But in the modern era of email threats, no email security solution can guarantee 100% effectiveness. Because attackers operate with great sophistication, carefully adapting their tactics to evade detection – whether by altering attachments, leveraging compromised accounts, or moving laterally across an organization – a siloed security approach risks missing these subtle, multi-domain threats. That’s why a robust defense-in-depth strategy is essential to mitigate APTs.

Real-world threat finds: Darktrace / EMAIL in action

Let’s take a look at some real-world scenarios where Darktrace / EMAIL stopped tactics associated with APT campaigns in their tracks – from adversary-in-the-middle attacks to suspicious lateral movement.

1: How Darktrace disrupted an adversary-in-the-middle attack by identifying abnormal login redirects and blocking credential exfiltration

In October 2024, Darktrace detected an adversary-in-the-middle (AiTM) attack targeting a Darktrace customer. The attack began with a phishing email from a seemingly legitimate Dropbox address, which contained multiple link payloads inviting the recipient to access a file. Other solutions would have struggled to catch this attack, as the initial AitM attack was launched through delivering a malicious URL through a trusted vendor or service. Once compromised, the threat actor could have laid low on the target account, gathering reconnaissance, without detection from the email security solution.  

Darktrace / EMAIL identified the abnormal login redirects and flagged the suspicious activity. Darktrace / IDENTITY then detected unusual login patterns and blocked credential exfiltration attempts, effectively disrupting the attack and preventing the adversary from gaining unauthorized access. Read more.

Figure 1: Overview of the malicious email in the Darktrace / EMAIL console, highlighting Dropbox associated content/link payloads

2: How Darktrace stopped lateral movement to block NTLM hash theft

In early 2024, Darktrace detected an attack by the TA577 threat group, which aimed to steal NTLM hashes to gain unauthorized access to systems. The attack began with phishing emails containing ZIP files that connected to malicious infrastructure.  

A traditional email security solution would have likely missed this attack by focusing too heavily on analyzing the zip file payloads or relying on reputation analysis to understand whether the infrastructure was registered as bad before this activity was a recognized IoC.

Because it correlates activity across domains, Darktrace identified unusual lateral movement within the network and promptly blocked the attempts to steal NTLM hashes, effectively preventing the attackers from accessing sensitive credentials and securing the network. Read more.

Figure 2: A summary of anomaly indicators seen for a campaign email sent by TA577, as detected by Darktrace / EMAIL

3: How Darktrace prevented the WarmCookie backdoor deployment embedded in phishing emails

In mid-2024, Darktrace identified a phishing campaign targeting organizations with emails impersonating recruitment firms. These emails contained malicious links that, when clicked, deployed the WarmCookie backdoor.  

These emails are difficult to detect, as they use social engineering tactics to manipulate users into engaging with emails and following the embedded malicious links – but if a security solution is not analysing content and context, these could be allowed through.

In several observed cases across customer environments, Darktrace detected and blocked the suspicious behavior associated with WarmCookie that had already managed to evade customers’ native email security. By using behavioral analysis to correlate anomalous activity across the digital estate, Darktrace was able to identify the backdoor malware strain and notify customers. Read more.

Conclusion

These threat examples highlight a key principle of the Darktrace approach – that a backwards-facing approach grounded in threat intelligence will always be one step behind.

Most threat actors operate in campaigns, carefully crafting attacks and testing them across multiple targets. Once a campaign is identified, good defenders and traditional security solutions quickly update their defenses with new threat intelligence, rules, and signatures. However, APTs have the resources to rapidly adapt – spinning up new infrastructure, modifying payloads and altering their attack footprint to evade detection.

This is where Darktrace / EMAIL excels. Only by analyzing each user, message and interaction can an email security solution hope to catch the types of highly-sophisticated attacks that have the potential to cause major reputational and financial damage. Darktrace / EMAIL ensures that even the most subtle threats are detected and blocked with autonomous response, before causing impact – helping organizations remain one step ahead of increasingly adaptive threat actors.

Download the Darktrace / EMAIL Solution Brief

Discover the most advanced cloud-native AI email security solution to protect your domain and brand while preventing phishing, novel social engineering, business email compromise, account takeover, and data loss.

  • Gain up to 13 days of earlier threat detection and maximize ROI on your current email security
  • Experience 20-25% more threat blocking power with Darktrace / EMAIL
  • Stop the 58% of threats bypassing traditional email security

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References

[1] FBI Internet Crime Report 2020

[2] https://www.optiv.com/insights/discover/blog/future-security-automation-how-ai-machine-learning-and-automation-are

[3] IBM Cost of a Data Breach Report 2023

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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
Carlos Gray
Senior Product Marketing Manager, Email

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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

[related-resource]

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About the author
Elise Longhurst
Senior Product Executive

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October 15, 2026

CikaBika Bot: Inside a Discord RAT

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Key Takeaways

·      Darktrace malware researchers have found an ongoing malware campaign using a document-themed lure containing a malicious LNK file and a PDF with an embedded payload.

·      The Nim based loader deploys a Rust-based remote access trojan (RAT), using a UAC bypass and DLL search-order hijacking technique targeting Microsoft Edge.

·      The RAT uses Discord for C2, creating dedicated channels to manage infected devices and execute commands.

·      CikaBika Bot supports system reconnaissance, remote command execution, screenshot capture, and file transfers.

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Introduction

In a threat landscape where attackers continue to rely on social engineering techniques to deliver malware, legitimate services are increasingly being abused to support malicious operations. Communications platforms like Discord can provide attackers with a ready-made channel for command-and-control (C2), allowing them to manage compromised devices without establishing dedicated infrastructure.

Malware researchers at Darktrace have identified a campaign distributing CikaBika, a Rust-based RAT that uses Discord for C2. The infection chain begins with a disk image and a malicious Windows shortcut. A Nim loader extracts the final payload and attempts to plant it in Microsoft Edge’s installation directory using a PowerShell UAC bypass. Once running, the RAT collects host information, registers the device with Discord, and accepts commands through a dedicated channel.

Technical Analysis

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Figure 1: Contents of Proposition_Projet.img.

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Initial access and PDF payload

The malware is packaged within a disk image named Proposition_Projet.img. Inside are a PDF titled “Presentation_Generale.pdf.pdf” and a Windows shortcut (LNK) disguised as a Word document, “Proposition_Projet_Personnalisable.docx”. The delivery method has not been confirmed; however, the filenames and document-themed lure are consistent with a phishing campaign.

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Figure 2: PowerShell command embedded in the malicious shortcut.


Opening the shortcut launches a hidden PowerShell process while displaying the PDF as a decoy. The French-language document explains how to produce a written summary or contraction of a text. In the background, PowerShell reads the PDF as bytes and uses the value at offset 239 (0x20) as an XOR key to decode an embedded executable. The resulting payload is written to %TEMP%\IfyuUapz.txt and executed using cmd /c.

Nim loader and UAC bypass

“IfyuUapz.txt” is a Nim-based loader that contains two Base64-encoded blobs: a Rust dynamic-link library (DLL)  and a PowerShell deployment script. Both are decoded and written to temporary files, referenced to within the loader as “dllFile” and “copyPs1”. The loader runs the script through a hidden PowerShell process using an encoded command, then attempts to load the DLL using Nim’s loadLib function. If loading fails, it invokes regsvr32.exe and retries.

Figure 3: Silent DLL deployer.

The PowerShell component is responsible for moving the DLL to C:\Program Files (x86)\Microsoft\Edge\Application\dbghelp.dll. It first copies the DLL to C:\Windows\Temp\dbghelp.tmp, then modifies HKCU\Software\Classes\ms-settings\shell\open\command so that launching computerdefaults.exe can trigger a command to move the staged DLL into the protected Edge directory. This is an attempt to bypass User Account Control (UAC), which depends on the target’s Windows configuration.

The script also compiles a small C# helper that calls ShowWindow to hide windows associated with Windows Settings and ApplicationFrameHost. It launches the bypass helper three times, checks for the presence of the destination DLL, and attempts to delete the temporary scripts, intermediate DLL, and associated registry changes.

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Figure 4: Legitimate msedge.exe function to load the legitimate dbghelp.dll.

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The legitimate msedge.exe has a delay-loaded dependency on dbghelp.dll, and the malicious DLL exports a subset of the functions associated with Microsoft’s debugging library. The attacker appears to be exploiting this dependency through DLL search-order hijacking. However, it does not export every function in Edge’s delay-import table, meaning its compatibility with the host is incomplete. The observed code establishes the intended sideloading route, rather than proving that Edge successfully loaded the planted DLL.

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Rust RAT payload

Figure 5: Discord configuration within the RAT.

The malicious dbghelp.dll (IfyuUapz.txt) is a Rust-based RAT. At startup, the RAT registers the compromised device with Discord and begins collecting information about the host. Its reconnaissance routine gathers system details, runs tasklist to enumerate processes, executes ipconfig to obtain network configuration, and attempts to take a screenshot. The resulting artifacts include tasklist_startup.txt, network_info.txt, and screenshot.png.

Figure 6: Network configuration notification in Discord channel.

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Figure 7: Network configuration output from network_info.txt.

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Discord C2

The RAT uses Discord channels to send commands and report results. The operator can send commands to the device’s channel and receive the resulting output in the same conversation. This gives the attacker an interactive command interface without having to operate and maintain a dedicated C2 server.

Figure 8: Commands available in Discord.

The command set includes !shell for remote command execution; !sysinfo and !info for host details; !isOn for status checks; !tasklist and !ps for process enumeration; and !kill for process termination. File-transfer commands include !upload and !download. Additional strings and command handlers reference screenshots (!src), camera access (!camera), streaming (!stream), and broadcasting (!broadcast).

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Figure 9: Device registration in Discord.

During device registration, the malware retrieves the victim’s public IP address using ipify.com. The registration message records the device ID, hostname, registration time, private and public IP addresses, status, uptime, and the Discord channel assigned to that device.

Operator activity and exfiltration

Figure 10: Powershell commands sent through Discord to exfiltrate data.

Once a device is registered, the attacker can issue commands through its dedicated Discord channel. In the observed activity, the attacker compressed a directory into a ZIP and exfiltrated it using ngrok.

Figure 11: Download command to retrieve a document from the target.

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The operator can also use !download to retrieve files from target. The example above displays a request for a specific document on the victim’s desktop. The requested file is then returned through the device’s Discord channel, giving the attacker a direct way to store and access selected data.

Conclusion

CikaBika Bot demonstrates how attackers continue to rely on social engineering techniques to deliver malware. Although the initial distribution method has not been confirmed, the use of a disk image containing a shortcut disguised as a Word document, alongside a legitimate-looking PDF, suggests the campaign was designed for phishing.

While Nim is less commonly encountered than C in Windows malware, it has increasingly appeared in malicious tooling. In this case, Nim is used to unpack and deploy a Rust-based RAT, adding another layer to the infection chain.

Once installed, CikaBika Bot uses Discord as its C2 platform, allowing the attacker to manage compromised devices, execute commands, and retrieve files without maintaining dedicated C2 infrastructure. The observed use of Discord channels for individual victims, alongside additional data exfiltration through ngrok, shows how legitimate services can be repurposed to support malicious operations.

Credit to Tara Gould (Malware Research Lead)

Indicators of Compromise (IoCs)

Presentation_Generale.pdf.pdf - eeb52cb95113a131c2c7713c17872da6

Proposition_Projet_Personnalisable.docx.lnk - 62e3e85eca78a0cd7e6669e9f33d936d

dbghelp.dll - c66858718d883bcfb9ce2c19b59f96c9

IfyuUapz.txt - 2deb724a709b0b892248405323350e9b

MITRE ATTACK

T1204.002 – User Execution: Malicious File
T1036.007 – Masquerading: Double File Extension
T1059.001 – PowerShell
T1027 – Obfuscated Files or Information
T1140 – Deobfuscate/Decode Files or Information
T1548.002 – Bypass User Account Control
T1112 – Modify Registry
T1574.001 – DLL Search Order Hijacking
T1036.005 – Masquerading: Match Legitimate Resource Name or Location
T1082 – System Information Discovery
T1016 – System Network Configuration Discovery
T1057 – Process Discovery
T1113 – Screen Capture
T1059.003 – Command and Scripting Interpreter: Windows Command Shell
T1102.002 – Web Service: Bidirectional Communication
T1560.001 – Archive Collected Data: Archive via Utility
T1567 – Exfiltration Over Web Service
T1070.004 – Indicator Removal: File Deletion
T1218.010 – Regsvr32
T1005 – Data from Local System
T1074.001 – Data Staged: Local Data Staging
T1041 – Exfiltration Over C2 Channel
T1564.003 – Hide Artifacts: Hidden Window
T1027.013 – Encrypted/Encoded File

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About the author
Tara Gould
Malware Research Lead
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