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June 23, 2023

How Darktrace Quickly Foiled An Information Stealer

Discover how Darktrace thwarted the CryptBot malware in just 2 seconds. Learn about this fast-moving threat and the defense strategies employed.
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
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23
Jun 2023

The recent trend of threat actors using information stealer malware, designed to gather and exfiltrate confidential data, shows no sign of slowing. With new or updated info-stealer strains appearing in the wild on a regular basis, it came as no surprise to see a surge in yet another prolific variant in late 2022, CryptBot.

What is CryptBot?

CryptBot is a Windows-based trojan malware that was first discovered in the wild in December 2019. It belongs to the prolific category of information stealers whose primary objective, as the name suggests, is to gather information from infected devices and send it to the threat actor.

ZeuS was reportedly the first info-stealer to be discovered, back in 2006. After its code was leaked, many other variants came to light and have been gaining popularity amongst cyber criminals [1] [2] [3]. Indeed, Inside the SOC has discussed multiple infections across its customer base associated with several types of stealers in the past months [4] [5] [6] [7]. 

The Darktrace Threat Research team investigated CryptBot infections on the digital environments of more than 40 different Darktrace customers between October 2022 and January 2023. Darktrace DETECT™ and its anomaly-based approach to threat detection allowed it to successfully identify the unusual activity surrounding these info-stealer infections on customer networks. Meanwhile, Darktrace RESPOND™, when enabled in autonomous response mode, was able to quickly intervene and prevent the exfiltration of sensitive company data.

Why is info-stealer malware popular?

It comes as no surprise that info-stealers have “become one of the most discussed malware types on the cybercriminal underground in 2022”, according to Accenture’s Cyber Threat Intelligence team [10]. This is likely in part due to the fact that:

More sensitive data on devices

Due to the digitization of many aspects of our lives, such as banking and social interactions, a trend accelerated by the COVID-19 pandemic.

Cost effective

Info-stealers provide a great return on investment (ROI) for threat actors looking to exfiltrate data without having to do the traditional internal reconnaissance and data transfer associated with data theft. Info-stealers are usually cheap to purchase and are available through Malware-as-a-Service (MaaS) offerings, allowing less technical and resourceful threat actors in on the stealing action. This makes them a prevalent threat in the malware landscape. 

How does CryptBot work?

The techniques employed by info-stealers to gather and exfiltrate data as well as the type of data targeted vary from malware to malware, but the data targeted typically includes login credentials for a variety of applications, financial information, cookies and global information about the infected computer [8]. Given its variety and sensitivity, threat actors can leverage the stolen data in several ways to make a profit. In the case of CryptBot, the data obtained is sold on forums or underground data marketplaces and can be later employed in higher profile attacks [9]. For example, stolen login information has previously been leveraged in credential-based attacks, which can successfully bypass authentication-based security measures, including multi-factor authentication (MFA). 

CryptBot functionalities

Like many information stealers, CryptBot is designed to steal a variety of sensitive personal and financial information such as browser credentials, cookies and history information and social media accounts login information, as well as cryptocurrency wallets and stored credit card information [11]. General information (e.g., OS, installed applications) about the infected computer is also retrieved. Browsers targeted by CryptBot include Chrome, Firefox, and Edge. In early 2022, CryptBot’s code was revamped in order to streamline its data extraction capabilities and improve its overall efficiency, an update that coincided with a rise in the number of infections [11] [12].

Some of CryptBot's functionalities were removed and its exfiltration process was streamlined, which resulted in a leaner payload, around half its original size and a quicker infection process [11]. Some of the features removed included sandbox detection and evasion functionalities, the collection of desktop text files and screen captures, which were deemed unnecessary. At the same time, the code was improved in order to include new Chrome versions released after CryptBot’s first appearance in 2019. Finally, its exfiltration process was simplified: prior to its 2022 update, the malware saved stolen data in two separate folders before sending it to two separate command and control (C2) domains. Post update, the data is only saved in one location and sent to one C2 domain, which is hardcoded in the C2 transmission function of the code. This makes the infection process much more streamlined, taking only a few minutes from start to finish. 

Aside from the update to its malware code, CryptBot regularly updates and refreshes its C2 domains and dropper websites, making it a highly fluctuating malware with constantly new indicators of compromise and distribution sites. 

Even though CryptBot is less known than other info-stealers, it was reportedly infecting thousands of devices daily in the first months of 2020 [13] and its continued prevalence resulted in Google taking legal action against its distribution infrastructure at the end of April 2023 [14].  

How is CryptBot obtained?

CryptBot is primarily distributed through malicious websites offering free and illegally modified software (i.e., cracked software) for common commercial programs (e.g., Microsoft Windows and Office, Adobe Photoshop, Google Chrome, Nitro PDF Pro) and video games. From these ‘malvertising’ pages, the user is redirected through multiple sites to the actual payload dropper page [15]. This distribution method has seen a gain in popularity amongst info-stealers in recent months and is also used by other malware families such as Raccoon Stealer and Vidar [16] [17].

A same network of cracked software websites can be used to download different malware strains, which can result in multiple simultaneous infections. Additionally, these networks often use search engine optimization (SEO) in order to make adverts for their malware distributing sites appear at the top of the Google search results page, thus increasing the chances of the malicious payloads being downloaded.

Furthermore, CryptBot leverages Pay-Per-Install (PPI) services such as 360Installer and PrivateLoader, a downloader malware family used to deliver payloads of multiple malware families operated by different threat actors [18] [19] [20]. The use of this distribution method for CryptBot payloads appears to have stemmed from its 2022 update. According to Google, 161 active domains were associated with 360Installer, of which 90 were associated with malware delivery activities and 29 with the delivery of CryptBot malware specifically. Google further identified hundreds of domains used by CryptBot as C2 sites, all of which appear to be hosted on the .top top-level domain [21].

This simple yet effective distribution tactic, combined with the MaaS model and the lucrative prospects of selling the stolen data resulted in numerous infections. Indeed, CryptBot was estimated to have infected over 670,000 computers in 2022 [14]. Even though the distribution method chosen means that most of the infected devices are likely to be personal computers, bring your own device (BYOD) policies and users’ tendency to reuse passwords means that corporate environments are also at risk. 

CryptBot Attack Overview

In some cases observed by Darktrace, after connecting to malvertising websites, devices were seen making encrypted SSL connections to file hosting services such as MediaFire or Mega, while in others devices were observed connecting to an endpoint associated with a content delivery network. This is likely the location from where the malware payload was downloaded alongside cracked software, which is executed by the unsuspecting user. As the user expects to run an executable file to install their desired software, the malware installation often happens without the user noticing.

Some of the malvertising sites observed by Darktrace on customer deployments were crackful[.]com, modcrack[.]net, windows-7-activator[.]com and office-activator[.]com. However, in many cases detected by Darktrace, CryptBot was propagated via websites offering trojanized KMSPico software (e.g., official-kmspico[.]com, kmspicoofficial[.]com). KMSPico is a popular Microsoft Windows and Office product activator that emulates a Windows Key Management Services (KMS) server to activate licenses fraudulently. 

Once it has been downloaded and executed, CryptBot will search the system for confidential information and create a folder with a seemingly randomly generated name, matching the regex [a-zA-Z]{10}, to store the gathered sensitive data, ready for exfiltration. 

Figure 1: Packet capture (PCAP) of an HTTP POST request showing the file with the stolen data being sent over the connection.
Figure 1: Packet capture (PCAP) of an HTTP POST request showing the file with the stolen data being sent over the connection.

This data is then sent to the C2 domain via HTTP POST requests on port 80 to the URI /gate.php. As previously stated, CryptBot C2 infrastructure is changed frequently and many of the domains seen by Darktrace had been registered within the previous 30 days. The domain names detected appeared to have been generated by an algorithm, following the regex patterns [a-z]{6}[0-9]{2,3}.top or [a-z]{6}[0-9]{2,3}.cfd. In several cases, the C2 domain had not been flagged as malicious by other security vendors or had just one detection. This is likely because of the frequent changes in the C2 infrastructure operated by the threat actors behind CryptBot, with new malicious domains being created periodically to avoid detection. This makes signature-based security solutions much less efficient to detect and block connections to malicious domains. Additionally, the fact that the stolen data is sent over regular HTTP POST requests, which are used daily as part of a multitude of legitimate processes such as file uploads or web form submissions, allows the exfiltration connections to blend in with normal and legitimate traffic making it difficult to isolate and detect as malicious activity. 

In this context, anomaly-based security detections such as Darktrace DETECT are the best way to pick out these anomalous connections amidst legitimate Internet traffic. In the case of CryptBot, two DETECT models were seen consistently breaching for CryptBot-related activity: ‘Device / Suspicious Domain’, breaching for connections to 100% rare C2 .top domains, and ‘Anomalous Connection / POST to PHP on New External Host’, breaching on the data exfiltration HTTP POST request. 

In deployments where Darktrace RESPOND was deployed, a RESPOND model breached within two seconds of the first HTTP POST request. If enabled in autonomous mode, RESPOND would block the data exfiltration connections, thus preventing the data safe from being sold in underground forums to other threat actors. In one of the cases investigated by Darktrace’s Threat Research team, DETECT was able to successfully identify and alert the customer about CryptBot-related malicious activity on a device that Darktrace had only begun to monitor one day before, showcasing how fast Darktrace’s Self-Learning AI learns every nuance of customer networks and the devices within it.

In most cases investigated by Darktrace, fewer than 5 minutes elapsed between the first connection to the endpoint offering free cracked software and the data being exfiltrated to the C2 domain. For example, in one of the attack chains observed in a university’s network, a device was seen connecting to the 100% rare endpoint official-kmspico[.]com at 16:53:47 (UTC).

Device Event Log showing SSL connections to the official-kmspico[.]com malvertising website.
Figure 2: Device Event Log showing SSL connections to the official-kmspico[.]com malvertising website.

One minute later, at 16:54:19 (UTC), the same device was seen connecting to two mega[.]co[.]nz subdomains and downloading around 13 MB of data from them. As mentioned previously, these connections likely represent the CryptBot payload and cracked software download.

Device Event Log showing SSL connections to mega[.]com endpoints following the connection to the malvertising site.
Figure 3: Device Event Log showing SSL connections to mega[.]com endpoints following the connection to the malvertising site.

At 16:56:01 (UTC), Darktrace detected the device making a first HTTP POST request to the 100% rare endpoint, avomyj24[.]top, which has been associated with CryptBot’s C2 infrastructure [22]. This initial HTTP POST connection likely represents the transfer of confidential data to the attacker’s infrastructure.

Device Event Log showing HTTP connections made by the infected device to the C2 domain. 
Figure 4: Device Event Log showing HTTP connections made by the infected device to the C2 domain. 

The full attack chain, from visiting the malvertising website to the malicious data egress, took less than three minutes to complete. In this circumstance, the machine-speed detection and response capabilities offered by Darktrace DETECT and RESPOND are paramount in order to stop CryptBot before it can successfully exfiltrates sensitive data. This is an incredibly quick infection timeline, with no lateral movement nor privilege escalation required to carry out the malware’s objective. 

Device Event Log showing the DETECT and RESPOND models breached during the attack. 
Figure 5: Device Event Log showing the DETECT and RESPOND models breached during the attack. 

Darktrace Cyber AI Analyst incidents were also generated as a result of this activity, displaying all relevant information in one panel for easy review by customer security teams.

Cyber AI Analyst event log showing the HTTP connections made by the breach device to the C2 endpoint.
Figure 6: Cyber AI Analyst event log showing the HTTP connections made by the breach device to the C2 endpoint.

Conclusion 

CryptBot info-stealer is fast, efficient, and apt at evading detection given its small size and swift process of data gathering and exfiltration via legitimate channels. Its constantly changing C2 infrastructure further makes it difficult for traditional security tools that really on rules and signatures or known indicators of compromise (IoCs) to detect these infections. 

In the face of such a threat, Darktrace’s anomaly-based detection allows it to recognize subtle deviations in a device’s pattern of behavior that may signal an evolving threat and instantly bring it to the attention of security teams. Darktrace DETECT is able to distinguish between benign activity and malicious behavior, even from newly monitored devices, while Darktrace RESPOND can move at machine-speed to prevent even the fastest moving threat actors from stealing confidential company data, as it demonstrated here by stopping CryptBot infections in as little as 2 seconds.

Credit to Alexandra Sentenac, Cyber Analyst, Roberto Romeu, Senior SOC Analyst

Darktrace Model Detections  

AI Analyst Coverage 

  • Possible HTTP Command and Control  

DETECT Model Breaches  

  • Device / Suspicious Domain 
  • Anomalous Connection / POST to PHP on New External Host 
  • Anomalous Connection / Multiple HTTP POSTs to Rare Hostname 
  • Compromise / Multiple SSL to Rare DGA Domains

List of IOCs

Indicator Type Description
luaigz34[.]top Hostname CryptBot C2 endpoint
watibt04[.]top Hostname CryptBot C2 endpoint
avolsq14[.]top Hostname CryptBot C2 endpoint

MITRE ATT&CK Mapping

Category Technique Tactic
INITIAL ACCESS Drive-by Compromise - T1189 N/A
COMMAND AND CONTROL Web Protocols - T1071.001 N/A
COMMAND AND CONTROL Domain Generation Algorithm - T1568.002 N/A

References

[1] https://www.malwarebytes.com/blog/threats/info-stealers

[2] https://cybelangel.com/what-are-infostealers/

[3] https://ke-la.com/information-stealers-a-new-landscape/

[4] https://darktrace.com/blog/vidar-info-stealer-malware-distributed-via-malvertising-on-google

[5] https://darktrace.com/blog/a-surge-of-vidar-network-based-details-of-a-prolific-info-stealer 

[6] https://darktrace.com/blog/laplas-clipper-defending-against-crypto-currency-thieves-with-detect-respond

[7] https://darktrace.com/blog/amadey-info-stealer-exploiting-n-day-vulnerabilities 

[8] https://cybelangel.com/what-are-infostealers/

[9] https://webz.io/dwp/the-top-10-dark-web-marketplaces-in-2022/

[10] https://www.accenture.com/us-en/blogs/security/information-stealer-malware-on-dark-web

[11] https://www.bleepingcomputer.com/news/security/revamped-cryptbot-malware-spread-by-pirated-software-sites/

[12] https://blogs.blackberry.com/en/2022/03/threat-thursday-cryptbot-infostealer

[13] https://www.deepinstinct.com/blog/cryptbot-how-free-becomes-a-high-price-to-pay

[14] https://blog.google/technology/safety-security/continuing-our-work-to-hold-cybercriminal-ecosystems-accountable/

[15] https://asec.ahnlab.com/en/31802/

[16] https://darktrace.com/blog/the-last-of-its-kind-analysis-of-a-raccoon-stealer-v1-infection-part-1

[17] https://www.trendmicro.com/pt_br/research/21/c/websites-hosting-cracks-spread-malware-adware.html

[18] https://intel471.com/blog/privateloader-malware

[19] https://cyware.com/news/watch-out-pay-per-install-privateloader-malware-distribution-service-is-flourishing-888273be 

[20] https://regmedia.co.uk/2023/04/28/handout_google_cryptbot_complaint.pdf

[21] https://www.bankinfosecurity.com/google-wins-court-order-to-block-cryptbot-infrastructure-a-21905

[22] https://github.com/stamparm/maltrail/blob/master/trails/static/malware/cryptbot.txt

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

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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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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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