ブログ
/
Network
/
April 2, 2024

Darktrace's Investigation of Raspberry Robin Worm

Discover how Darktrace is leading the hunt for Raspberry Robin. Explore early insights and strategies in the battle against cyber threats.
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
Alexandra Sentenac
Cyber Analyst
Default blog image
02
Apr 2024

Introduction

In the face of increasingly hardened digital infrastructures and skilled security teams, malicious actors are forced to constantly adapt their attack methods, resulting in sophisticated attacks that are designed to evade human detection and bypass traditional network security measures.  

One such example that was recently investigated by Darktrace is Raspberry Robin, a highly evasive worm malware renowned for merging existing and novel techniques, as well as leveraging both physical hardware and software, to establish a foothold within organization’s networks and propagate additional malicious payloads.

What is Raspberry Robin?

Raspberry Robin, also known as ‘QNAP worm’, is a worm malware that was initially discovered at the end of 2023 [1], however, its debut in the threat landscape may have predated this, with Microsoft uncovering malicious artifacts linked to this threat (which it tracks under the name Storm-0856) dating back to 2019 [4]. At the time, little was known regarding Raspberry Robin’s objectives or operators, despite the large number of successful infections worldwide. While the identity of the actors behind Raspberry Robin still remains a mystery, more intelligence has been gathered about the malware and its end goals as it was observed delivering payloads from different malware families.

Who does Raspberry Robin target?

While it was initially reported that Raspberry Robin primarily targeted the technology and manufacturing industries, researchers discovered that the malware had actually targeted multiple sectors [3] [4]. Darktrace’s own investigations echoed this, with Raspberry Robin infections observed across various industries, including public administration, finance, manufacturing, retail education and transportation.

How does Raspberry Robin work?

Initially, it appeared that Raspberry Robin's access to compromised networks had not been utilized to deliver final-stage malware payloads, nor to steal corporate data. This uncertainty led researchers to question whether the actors involved were merely “cybercriminals playing around” or more serious threats [3]. This lack of additional exploitation was indeed peculiar, considering that attackers could easily escalate their attacks, given Raspberry Robin’s ability to bypass User Account Control using legitimate Windows tools [4].

However, at the end of July 2022, some clarity emerged regarding the operators' end goals. Microsoft researchers revealed that the access provided by Raspberry Robin was being utilized by an access broker tracked as DEV-0206 to distribute the FakeUpdates malware downloader [2]. Researchers further discovered malicious activity associated with Evil Corp TTPs (i.e., DEV-0243) [5] and payloads from the Fauppod malware family leveraging Raspberry Robin’s access [8]. This indicates that Raspberry Robin may, in fact, be an initial access broker, utilizing its presence on hundreds of infected networks to distribute additional payloads for paying malware operators. Thus far, Raspberry Robin has been observed distributing payloads linked to FIN11, Clop Gang, BumbleBee, IcedID, and TrueBot on compromised networks [12].

Raspberry Robin’s Continued Evolution

Since it first appeared in the wild, Raspberry Robin has evolved from "being a widely distributed worm with no observed post-infection actions [...] to one of the largest malware distribution platforms currently active" [8]. The fact that Raspberry Robin has become such a prevalent threat is likely due to the continual addition of new features and evasion capabilities to their malware [6] [7].  

Since its emergence, the malware has “changed its communication method and lateral movement” [6] in order to evade signature detections based on threat intelligence and previous versions. Endpoint security vendors commonly describe it as heavily obfuscated malware, employing multiple layers of evasion techniques to hinder detection and analysis. These include for example dropping a fake payload when analyzed in a sandboxed environment and using mixed-case executing commands, likely to avoid case-sensitive string-based detections.  

In more recent campaigns, Raspberry Robin further appears to have added a new distribution method as it was observed being downloaded from archive files sent as attachments using the messaging service Discord [11]. These attachments contained a legitimate and signed Windows executable, often abused by attackers for side-loading, alongside a malicious dynamic-link library (DLL) containing a Raspberry Robin sample.

Another reason for the recent success of the malware may be found in its use of one-day exploits. According to researchers, Raspberry Robin now utilizes several local privilege escalation exploits that had been recently disclosed, even before a proof of concept had been made available [9] [10]. This led cyber security professionals to believe that operators of the malware may have access to an exploit seller [6]. The use of these exploits enhances Raspberry Robin's detection evasion and persistence capabilities, enabling it to propagate on networks undetected.

Darktrace’s Coverage of Raspberry Robin

Through two separate investigations carried out by Darktrace’s Threat Research team, first in late 2022 and then in November 2023, it became evident that Raspberry Robin was capable of integrating new functionalities and tactics, techniques and procedures (TTPs) into its attacks. Darktrace DETECT™ provided full visibility over the evolving campaign activity, allowing for a comparison of the threat across both investigations. Additionally, if Darktrace RESPOND™ was enabled on affected networks, it was able to quickly mitigate and contain emerging activity during the initial stages, thwarting the further escalation of attacks.

Raspberry Robin Initial Infection

The most prevalent initial infection vector appears to be the introduction of an infected external drive, such as a USB stick, containing a malicious .LNK file (i.e., a Windows shortcut file) disguised as a thumb drive or network share. When clicked, the LNK file automatically launches cmd.exe to execute the malicious file stored on the external drive, and msiexec.exe to connect to a Raspberry Robin command-and-control (C2) endpoint and download the main malware component. The whole process leverages legitimate Windows processes and is therefore less likely to raise any alarms from more traditional security solutions. However, Darktrace DETECT was able to identify the use of Msiexec to connect to a rare endpoint as anomalous in every case investigated.

Little is currently known regarding how the external drives are infected and distributed, but it has been reported that affected USB drives had previously been used for printing at printing and copying shops, suggesting that the infection may have originated from such stores [13].

A method as simple as leaving an infected USB on a desk in a public location can be a highly effective social engineering tactic for attackers. Exploiting both curiosity and goodwill, unsuspecting individuals may innocently plug in a found USB, hoping to identify its owner, unaware that they have unwittingly compromised their device.

As Darktrace primarily operates on the network layer, the insertion of a USB endpoint device would not be within its visibility. Nevertheless, Darktrace did observe several instances wherein multiple Microsoft endpoints were contacted by compromised devices prior to the first connection to a Raspberry Robin domain. For example, connections to the URI '/fwlink/?LinkID=252669&clcid=0x409' were observed in multiple customer environments prior to the first Raspberry Robin external connection. This connectivity seems to be related to Windows attempting to retrieve information about installed hardware, such as a printer, and could also be related to the inserting of an external USB drive.

Figure 1: Device Event Log showing an affected device making connections to Microsoft endpoints, prior to contacting the Raspberry Robin C2 endpoint ‘vqdn[.]net’.
Figure 1: Device Event Log showing an affected device making connections to Microsoft endpoints, prior to contacting the Raspberry Robin C2 endpoint ‘vqdn[.]net’.

Raspberry Robin Command-and-Control Activity

In all cases investigated by Darktrace, compromised devices were detected making HTTP GET connections via the unusual port 8080 to Raspberry Robin C2 endpoints using the new user agent 'Windows Installer'.

The C2 hostnames observed were typically short and matched the regex /[a-zA-Z0-9]{2,4}.[a-zA-Z0-9]{2,6}/, and were hosted on various top-level domains (TLD) such as ‘.rocks’, ‘.pm’, and ‘.wf’. On one customer network, Darktrace observed the download of an MSI file from the Raspberry Robin domain ‘wak[.]rocks’. This package contained a heavily protected malicious DLL file whose purpose was unknown at the time.  

However, in September 2022, external researchers revealed that the main purpose of this DLL was to download further payloads and enable lateral movement, persistence and privilege escalation on compromised devices, as well as exfiltrating sensitive information about the device. As worm infections spread through networks automatically, exfiltrating device data is an essential process for threat actor to keep track of which systems have been infected.

On affected networks investigated by Darktrace, compromised devices were observed making C2 connections that contained sensitive device information, including hostnames and credentials, with additional host information likely found within the data packets [12].

Figure 2: Model Breach Event Log displaying the events that triggered the the ‘New User Agent and Suspicious Request Data’ DETECT model breach.
Figure 2: Model Breach Event Log displaying the events that triggered the the ‘New User Agent and Suspicious Request Data’ DETECT model breach.

As for C2 infrastructure, Raspberry Robin leverages compromised Internet of Things (IoT) devices such as QNAP network attached storage (NAS) systems with hijacked DNS settings [13]. NAS devices are data storage servers that provide access to the files they store from anywhere in the world. These features have been abused by Raspberry Robin operators to distribute their malicious payloads, as any uploaded file could be stored and shared easily using NAS features.

However, Darktrace found that QNAP servers are not the only devices being exploited by Raspberry Robin, with DETECT identifying other IoT devices being used as C2 infrastructure, including a Cerio wireless access point in one example. Darktrace recognized that this connection was new to the environment and deemed it as suspicious, especially as it also used new software and an unusual port for the HTTP protocol (i.e., 8080 rather than 80).

In several instances, Darktrace observed Raspberry Robin utilizing TOR exit notes as backup C2 infrastructure, with compromised devices detected connecting to TOR endpoints.

Figure 3: Raspberry Robin C2 endpoint when viewed in a sandbox environment.
Figure 3: Raspberry Robin C2 endpoint when viewed in a sandbox environment.
Figure 4: Raspberry Robin C2 endpoint when viewed in a sandbox environment.
Figure 4: Raspberry Robin C2 endpoint when viewed in a sandbox environment.

Raspberry Robin in 2022 vs 2023

Despite the numerous updates and advancements made to Raspberry Robin between the investigations carried out in 2022 and 2023, Darktrace’s detection of the malware was largely the same.

DETECT models breached during first investigation at the end of 2022:

  • Device / New User Agent
  • Anomalous Server Activity / New User Agent from Internet Facing System
  • Device / New User Agent and New IP
  • Compromise / Suspicious Request Data
  • Compromise / Uncommon Tor Usage
  • Possible Tor Usage

DETECT models breached during second investigation in late 2023:

  • Device / New User Agent and New IP
  • Device / New User Agent and Suspicious Request Data
  • Device / New User Agent
  • Device / Suspicious Domain
  • Possible Tor Usage

Darktrace’s anomaly-based approach to threat detection enabled it to consistently detect the TTPs and IoCs associated with Raspberry Robin across the two investigations, despite the operator’s efforts to make it stealthier and more difficult to analyze.

In the first investigation in late 2022, Darktrace detected affected devices downloading addition executable (.exe) files following connections to the Raspberry Robin C2 endpoint, including a numeric executable file that appeared to be associated with the Vidar information stealer. Considering the advanced evasion techniques and privilege escalation capabilities of Raspberry Robin, early detection is key to prevent the malware from downloading additional malicious payloads.

In one affected customer environment investigated in late 2023, a total of 12 devices were compromised between mid-September and the end of October. As this particular customer did not have Darktrace RESPOND, the Raspberry Robin infection was able to spread through the network unabated until the customer acted upon Darktrace DETECT’s alerts.

Had Darktrace RESPOND been enabled in autonomous response mode, it would have been able to take immediate action following the first observed connection to a Raspberry Robin C2 endpoint, by blocking connections to the suspicious endpoint and enforcing a device’s normal ‘pattern of life’.

By enforcing a pattern of life on an affected device, RESPOND would prevent it from carrying out any activity that deviates from this learned pattern, including connections to new endpoints using new software as was the case in Figure 5, effectively shutting down the attack in the first instance.

Model Breach Event Log showing RESPOND’s actions against connections to Raspberry Robin C2 endpoints.
Figure 5: Model Breach Event Log showing RESPOND’s actions against connections to Raspberry Robin C2 endpoints.

Conclusion

Raspberry Robin is a highly evasive and adaptable worm known to evolve and change its TTPs on a regular basis in order to remain undetected on target networks for as long as possible. Due to its ability to drop additional malware variants onto compromised devices, it is crucial for organizations and their security teams to detect Raspberry Robin infections at the earliest possible stage to prevent the deployment of potentially disruptive secondary attacks.

Despite its continued evolution, Darktrace's detection of Raspberry Robin remained largely unchanged across the two investigations. Rather than relying on previous IoCs or leveraging existing threat intelligence, Darktrace DETECT’s anomaly-based approach allows it to identify emerging compromises by detecting the subtle deviations in a device’s learned behavior that would typically come with a malware compromise.

By detecting the attacks at an early stage, Darktrace gave its customers full visibility over malicious activity occurring on their networks, empowering them to identify affected devices and remove them from their environments. In cases where Darktrace RESPOND was active, it would have been able to take autonomous follow-up action to halt any C2 communication and prevent the download of any additional malicious payloads.  

Credit to Alexandra Sentenac, Cyber Analyst, Trent Kessler, Senior Cyber Analyst, Victoria Baldie, Director of Incident Management

Appendices

Darktrace DETECT Model Coverage

Device / New User Agent and New IP

Device / New User Agent and Suspicious Request Data

Device / New User Agent

Compromise / Possible Tor Usage

Compromise / Uncommon Tor Usage

MITRE ATT&CK Mapping

Tactic - Technique

Command & Control - T1090.003 Multi-hop Proxy

Lateral Movement - T1210 Exploitation of remote services

Exfiltration over C2 Data - T1041 Exfiltration over C2 Channel

Data Obfuscation - T1001 Data Obfuscation

Vulnerability Scanning - T1595.002 Vulnerability Scanning

Non-Standard Port - T1571 Non-Standard Port

Persistence - T1176 Browser Extensions

Initial Access - T1189 Drive By Compromise / T1566.002  Spearphishing Link

Collection - T1185 Man in the browser

List of IoCs

IoC - Type - Description + Confidence

vqdn[.]net - Hostname - C2 Server

mwgq[.]net - Hostname - C2 Server

wak[.]rocks - Hostname - C2 Server

o7car[.]com - Hostname - C2 Server

6t[.]nz - Hostname - C2 Server

fcgz[.]net - Hostname - Possible C2 Server

d0[.]wf - Hostname - C2 Server

e0[.]wf - Hostname - C2 Server

c4z[.]pl - Hostname - C2 Server

5g7[.]at - Hostname - C2 Server

5ap[.]nl - Hostname - C2 Server

4aw[.]ro - Hostname - C2 Server

0j[.]wf - Hostname - C2 Server

f0[.]tel - Hostname - C2 Server

h0[.]pm - Hostname - C2 Server

y0[.]pm - Hostname - C2 Server

5qy[.]ro - Hostname - C2 Server

g3[.]rs - Hostname - C2 Server

5qe8[.]com - Hostname - C2 Server

4j[.]pm - Hostname - C2 Server

m0[.]yt - Hostname - C2 Server

zk4[.]me - Hostname - C2 Server

59.15.11[.]49 - IP address - Likely C2 Server

82.124.243[.]57 - IP address - C2 Server

114.32.120[.]11 - IP address - Likely C2 Server

203.186.28[.]189 - IP address - Likely C2 Server

70.124.238[.]72 - IP address - C2 Server

73.6.9[.]83 - IP address - Likely C2 Server

References

[1] https://redcanary.com/blog/raspberry-robin/  

[2] https://www.bleepingcomputer.com/news/security/microsoft-links-raspberry-robin-malware-to-evil-corp-attacks/

[3] https://7095517.fs1.hubspotusercontent-na1.net/hubfs/7095517/FLINT%202022-016%20-%20QNAP%20worm_%20who%20benefits%20from%20crime%20(1).pdf

[4] https://www.bleepingcomputer.com/news/security/microsoft-finds-raspberry-robin-worm-in-hundreds-of-windows-networks/

[5] https://therecord.media/microsoft-ties-novel-raspberry-robin-malware-to-evil-corp-cybercrime-syndicate

[6] https://securityaffairs.com/158969/malware/raspberry-robin-1-day-exploits.html

[7] https://research.checkpoint.com/2024/raspberry-robin-keeps-riding-the-wave-of-endless-1-days/

[8] https://redmondmag.com/articles/2022/10/28/microsoft-details-threat-actors-leveraging-raspberry-robin-worm.aspx

[9] https://www.bleepingcomputer.com/news/security/raspberry-robin-malware-evolves-with-early-access-to-windows-exploits/

[10] https://www.bleepingcomputer.com/news/security/raspberry-robin-worm-drops-fake-malware-to-confuse-researchers/

[11] https://thehackernews.com/2024/02/raspberry-robin-malware-upgrades-with.html

[12] https://decoded.avast.io/janvojtesek/raspberry-robins-roshtyak-a-little-lesson-in-trickery/

[13] https://blog.bushidotoken.net/2023/05/raspberry-robin-global-usb-malware.html

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
Alexandra Sentenac
Cyber Analyst

More in this series

No items found.

Blog

/

Network

/

October 20, 2026

Why Kubernetes Security Needs A Behavioral Approach

Default 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

Blog

/

Network

/

October 15, 2026

CikaBika Bot: Inside a Discord RAT

Default blog imageDefault blog image

‍

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.

‍

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

‍

Figure 1: Contents of Proposition_Projet.img.

‍

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.

‍

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.

‍

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

‍

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.

‍

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.

‍

Figure 7: Network configuration output from network_info.txt.

‍

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).

‍

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.

‍

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

Continue reading
About the author
Tara Gould
Malware Research Lead
あなたのデータ × DarktraceのAI
唯一無二のDarktrace AIで、ネットワークセキュリティを次の次元へ