April 8, 2024

Balada Injector: Darktrace’s Investigation into the Malware Exploiting WordPress Vulnerabilities

This blog explores Darktrace’s detection of Balada Injector, a malware known to exploit vulnerabilities in WordPress to gain unauthorized access to networks. Darktrace was able to define numerous use-cases within customer environments which followed previously identified patterns of activity spikes across multiple weeks.

Written by

Justin Torres

Cyber Analyst

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

Justin Torres

Cyber Analyst

Apr 2024

Introduction

With millions of users relying on digital platforms in their day-to-day lives, and organizations across the world depending on them for their business operations, they have inevitably also become a prime target for threat actors. The widespread exploitation of popular services, websites and platforms in cyber-attacks highlights the pervasive nature of malicious actors in today’s threat landscape.

A prime illustration can be seen within the content management system WordPress. Its widespread use and extensive plug-in ecosystem make it an attractive target for attackers aiming to breach networks and access sensitive data, thus leading to routine exploitation attempts. In the End of Year Threat Report for 2023, for example, Darktrace reported that a vulnerability in one WordPress plug-in, namely an authentication bypass vulnerability in miniOrange's Social Login and Register. Darktrace observed it as one of the most exploited vulnerabilities observed across its customer base in the latter half of 2023.

Between September and October 2023, Darktrace observed a string of campaign-like activity associated with Balada Injector, a malware strain known to exploit vulnerabilities in popular plug-ins and themes on the WordPress platform in order to inject a backdoor to provide further access to affected devices and networks. Thanks to its anomaly-based detection, Darktrace DETECT™ was able to promptly identify suspicious connections associated with the Balada Injector, ensuring that security teams had full visibility over potential post-compromise activity and allowing them to act against offending devices.

What is Balada Injector?

The earliest signs of the Balada Injector campaign date back to 2017; however, it was not designated the name Balada Injector until December 2022 [1]. The malware utilizes plug-ins and themes in WordPress to inject a backdoor that redirects end users to malicious and fake sites. It then exfiltrates sensitive information, such as database credentials, archive files, access logs and other valuable information which may not be properly secured [1]. Balada Injector compromise activity is also reported to arise in spikes of activity that emerge every couple of weeks [4].

In its most recent attack activity patterns, specifically in September 2023, Balada Injector exploited a cross-site scripting (XSS) vulnerability in CVE-2023-3169 associated with the tagDiv composer plug-in. Some of the injection methods observed included HTML injections, database injections, and arbitrary file injections. In late September 2023, a similar pattern of behavior was observed, with the ability to plant a backdoor that could execute PHP code and install a malicious WordPress plug-in, namely ‘wp-zexit’.

According to external security researchers [2], the most recent infection activity spikes for Balada Injector include the following:

Pattern 1: ‘stay.decentralappps[.]com’ injections

Pattern 2: Autogenerated malicious WordPress users

Pattern 3: Backdoors in the Newspaper theme’s 404.php file

Pattern 4: Malicious ‘wp-zexit’ plug-in installation

Pattern 5: Three new Balada Injector domains (statisticscripts[.]com, dataofpages[.]com, and listwithstats[.]com)

Pattern 6: Promsmotion[.]com domain

Darktrace’s Coverage of Balada Injector

Darktrace detected devices across multiple customer environments making external connections to the malicious Balada Injector domains, including those associated with aforementioned six infection activity patterns. Across the incidents investigated by Darktrace, much of the activity appeared to be associated with TLS/SSL connectivity, related to Balada Injector endpoints, which correlated with the reported infection patterns of this malware. The observed hostnames were all recently registered and, in most cases, had IP geolocations in either the Netherlands or Ukraine.

In the observed cases of Balada Injector across the Darktrace fleet, Darktrace RESPOND™ was not active on the affected customer environments. If RESPOND had been active and enabled in autonomous response mode at the time of these attacks, it would have been able to quickly block connections to malicious Balada Injector endpoints as soon as they were identified by DETECT, thereby containing the threat.

Looking within the aforementioned activity patterns, Darktrace identified a Balada Injector activity within a customer’s environment on October 16, 2023, when a device was observed making a total of 9 connection attempts to ‘sleep[.]stratosbody[.]com’, a domain that had previously been associated with the malware [2]. Darktrace recognized that the endpoint had never been seen on the network, with no other devices having connected to it previously, thus treated it as suspicious.

Figure 1: The connection details above demonstrate 100% rare external connections were made from the internal device to the ‘sleep[.]stratosbody[.]com’ endpoint.

Similarly, on September 21, 2023, Darktrace observed a device on another customer network connecting to an external IP that had never previously been observed on the environment, 111.90.141[.]193. The associated server name was a known malicious endpoint, ‘stay.decentralappps[.]com’, known to be utilized by Balada Injector to host malicious scripts used to compromise WordPress sites. Although the ‘stay.decentralappps[.]com’ domain was only registered in September 2023, it was reportedly used in the redirect chain of the aforementioned stratosbody[.com] domain [2]. Such scripts can be used to upload backdoors, including malicious plug-ins, and create blog administrators who can perform administrative tasks without having to authenticate [2].

Figure 2: Advance Search results displaying the metadata logs surrounding the unusual connections to ‘stay.decentralappps[.]com’. A total of nine HTTP CONNECT requests were observed, with status messages “Proxy Authorization Required” and “Connection established”.

Darktrace observed additional connections within the same customer’s environment on October 10 and October 18, specifically SSL connections from two distinct source devices to the ‘stay.decentralappps[.]com’ endpoint. Within these connections, Darktrace observed the normalized JA3 fingerprints, “473f0e7c0b6a0f7b049072f4e683068b” and “aa56c057ad164ec4fdcb7a5a283be9fc”, the latter of which corresponds to GitHub results mentioning a Python client (curl_cffi) that is able to impersonate the TLS signatures of browsers or JA3 fingerprints [8].

Figure 3: Advanced Search query results showcasing Darktrace’s detection of SSL connections to ‘stay.decentralappps[.]com over port 443.

On September 29, 2023, a device on a separate customer’s network was observed connecting to the hostname ‘cdn[.]dataofpages[.]com’, one of the three new Balada Injector domains identified as part of the fifth pattern of activity outlined above, using a new SSL certificate via port 443. Multiple open-source intelligence (OSINT) vendors flagged this domain as malicious and associated with Balada Injector malware [9].

Figure 4: The Model Breach Event Log detailing the Balada Injector-related connections observed causing the ‘Anomalous External Activity from Critical Network Device’ DETECT model to breach.

On October 2, 2023, Darktrace observed the device of another customer connecting to the rare hostname, ‘js.statisticscripts[.]com’ with the IP address 185.39.206[.]161, both of which had only been registered in late September and are known to be associated with the Balada Injector.

Figure 5: Model Breach Event Log detailing connections to the hostname ‘js.statisticscripts[.]com’ over port 137.

On September 13, 2023, Darktrace identified a device on another customer’s network connecting to the Balada Injector endpoint ‘stay.decentralappps[.]com’ endpoint, with the destination IP 1.1.1[.]1, using the SSL protocol. This time, however, Darktrace also observed the device making subsequent connections to ‘get.promsmotion[.]com’ a subdomain of the ‘promsmotion[.]com’ domain. This domain is known to be used by Balada Injector actors to host malicious scripts that can be injected into the WordPress Newspaper theme as potential backdoors to be leveraged by attackers.

In a separate case observed on September 14, Darktrace identified a device on another environment connecting to the domain ‘collect[.]getmygateway[.]com’ with the IP 88.151.192[.]254. No other device on the customer’s network had visited this endpoint previously, and the device in question was observed repeatedly connecting to it via port 443 over the course of four days. While this specific hostname had not been linked with a specific activity pattern of Balada Injector, it was reported as previously associated with the malware in September 2023 [2].

Figure 6: Model Breach Event Log displaying a customer device making repeated connections to the endpoint ‘collect[.]getmygateway[.]com’, breaching the DETECT model ‘Repeating Connections Over 4 Days’.

In addition to DETECT’s identification of this suspicious activity, Darktrace’s Cyber AI Analyst™ also launched its own autonomous investigation into the connections. AI Analyst was able to recognize that these separate connections that took place over several days were, in fact, connected and likely represented command-and-control (C2) beaconing activity that had been taking place on the customer networks.

By analyzing the large number of external connections taking place on a customer’s network at any one time, AI Analyst is able to view seemingly isolated events as components of a wider incident, ensuring that customers maintain full visibility over their environments and any emerging malicious activity.

Figure 7: Cyber AI Analyst investigation detailing the SSL connectivity observed, including endpoint details and overall summary of the beaconing activity.

Conclusion

While Balada Injector’s tendency to interchange C2 infrastructure and utilize newly registered domains may have been able to bypass signature-based security measures, Darktrace’s anomaly-based approach enabled it to swiftly identify affected devices across multiple customer environments, without needing to update or retrain its models to keep pace with the evolving iterations of WordPress vulnerabilities.

Unlike traditional measures, Darktrace DETECT’s Self-Learning AI focusses on behavioral analysis, crucial for identifying emerging threats like those exploiting commonly used platforms such as WordPress. Rather than relying on historical threat intelligence or static indicators of compromise (IoC) lists, Darktrace identifies the subtle deviations in device behavior, such as unusual connections to newly registered domains, that are indicative of network compromise.

Darktrace’s suite of products, including DETECT+RESPOND, is uniquely positioned to proactively identify and contain network compromises from the onset, offering vital protection against disruptive cyber-attacks.

Credit to: Justin Torres, Cyber Analyst, Nahisha Nobregas, Senior Cyber Analyst

Appendices

Darktrace DETECT Model Coverage

Anomalous Server Activity / Anomalous External Activity from Critical Network Device
Anomalous Connection / Anomalous SSL without SNI to New External
Anomalous Connection / Rare External SSL Self-Signed
Compliance / Possible DNS Over HTTPS/TLS
Compliance / External Windows Communications
Compromise / Repeating Connections Over 4 Days
Compromise / Beaconing Activity To External Rare
Compromise / SSL Beaconing to Rare Destination
Compromise / HTTP Beaconing to Rare Destination
Compromise / Suspicious TLS Beaconing To Rare External
Compromise / Large DNS Volume for Suspicious Domain
Anomalous Server Activity / Outgoing from Server
Anomalous Server Activity / Rare External from Server
Device / Suspicious Domain

List of IoCs

IoC - Type - Description + Confidence

collect[.]getmygateway[.]com - Hostname - Balada C2 Endpoint

cdn[.]dataofpages[.]com - Hostname - Balada C2 Endpoint

stay[.]decentralappps[.]com - Hostname - Balada C2 Endpoint

get[.]promsmotion[.]com - Hostname - Balada C2 Endpoint

js[.]statisticscripts[.]com - Hostname - Balada C2 Endpoint

sleep[.]stratosbody[.]com - Hostname - Balada C2 Endpoint

trend[.]stablelightway[.]com - Hostname - Balada C2 Endpoint

cdn[.]specialtaskevents[.]com - Hostname - Balada C2 Endpoint

88.151.192[.]254 - IP Address - Balada C2 Endpoint

185.39.206[.]160 - IP Address - Balada C2 Endpoint

111.90.141[.]193 - IP Address - Balada C2 Endpoint

185.39.206[.]161 - IP Address - Balada C2 Endpoint

2.59.222[.]121 - IP Address - Balada C2 Endpoint

80.66.79[.]253 - IP Address - Balada C2 Endpoint

Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:68.0) - User Agent - Observed User Agent in Balada C2 Connections

Gecko/20100101 Firefox/68.0 - User Agent - Observed User Agent in Balada C2 Connections

Mozilla/5.0 (Windows NT 10.0; Win64; x64) - User Agent - Observed User Agent in Balada C2 Connections

AppleWebKit/537.36 (KHTML, like Gecko) - User Agent - Observed User Agent in Balada C2 Connections

Chrome/117.0.0.0 - User Agent - Observed User Agent in Balada C2 Connections

Safari/537.36 - User Agent - Observed User Agent in Balada C2 Connections

Edge/117.0.2045.36 - User Agent - Observed User Agent in Balada C2 Connections

MITRE ATT&CK Mapping

Technique - Tactic - ID - Sub Technique

Exploit Public-Facing Application

INITIAL ACCESS

T1190

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Web Protocols

COMMAND AND CONTROL

T1071.001

T1071

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Protocol Tunneling

COMMAND AND CONTROL

T1572

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Default Accounts

DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS

T1078.001

T1078

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Domain Accounts

DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS

T1078.002

T1078

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External Remote Services

PERSISTENCE, INITIAL ACCESS

T1133

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Local Accounts

DEFENSE EVASION, PERSISTENCE, PRIVILEGE ESCALATION, INITIAL ACCESS

T1078.003

T1078

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Application Layer Protocol

COMMAND AND CONTROL

T1071

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Browser Extensions

PERSISTENCE

T1176

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Encrypted Channel

COMMAND AND CONTROL

T1573

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Fallback Channels

COMMAND AND CONTROL

T1008

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Multi-Stage Channels

COMMAND AND CONTROL

T1104

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Non-Standard Port

COMMAND AND CONTROL

T1571

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Supply Chain Compromise

INITIAL ACCESS ICS

T0862

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Commonly Used Port

COMMAND AND CONTROL ICS

T0885

References

[1] https://blog.sucuri.net/2023/04/balada-injector-synopsis-of-a-massive-ongoing-wordpress-malware-campaign.html

[2] https://blog.sucuri.net/2023/10/balada-injector-targets-unpatched-tagdiv-plugin-newspaper-theme-wordpress-admins.html

[3] https://securityboulevard.com/2021/05/wordpress-websites-redirecting-to-outlook-phishing-pages-travelinskydream-ga-track-lowerskyactive/

[4] https://thehackernews.com/2023/10/over-17000-wordpress-sites-compromised.html

[5] https://www.bleepingcomputer.com/news/security/over-17-000-wordpress-sites-hacked-in-balada-injector-attacks-last-month/

[6]https://nvd.nist.gov/vuln/detail/CVE-2023-3169

[7] https://www.geoedge.com/balda-injectors-2-0-evading-detection-gaining-persistence/

[8] https[:]//github[.]com/yifeikong/curl_cffi/blob/master/README.md

[9] https://www.virustotal.com/gui/domain/cdn.dataofpages.com

Written by

Justin Torres

Cyber Analyst

Inside the SOC

Written by

Justin Torres

Cyber Analyst

Malware Research Lead

Senior Product Executive

Watch the NIS2 Webinar

Blog

Network

October 20, 2026

Why Kubernetes Security Needs A Behavioral Approach

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:

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.
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.
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}
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:

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.
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.
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

Blog

Network

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.

‍

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.

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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.

‍

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.

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

About the author

Tara Gould

Malware Research Lead

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