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June 25, 2024

Following up on our Conversation: Detecting & Containing a LinkedIn Phishing Attack with Darktrace

Darktrace/Email detected a phishing attack that had originated from LinkedIn, where the attacker impersonated a well known construction company to conduct a credential harvesting attack on the target. Darktrace’s ActiveAI Security Platform played a critical role in investigating the activity and initiating real-time responses that were outside the physical capability of human security teams.
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
Nicole Wong
Cyber Security Analyst
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25
Jun 2024

Note: Real organization, domain and user names have been modified and replaced with fictitious names to maintain anonymity.  

Social media cyber-attacks

Social media is a known breeding ground for cyber criminals to easily connect with a near limitless number of people and leverage the wealth of personal information shared on these platforms to defraud the general public.  Analysis suggests even the most tech savvy ‘digital natives’ are vulnerable to impersonation scams over social media, as criminals weaponize brands and trends, using the promise of greater returns to induce sensitive information sharing or fraudulent payments [1].

LinkedIn phishing

As the usage of a particular social media platform increases, cyber criminals will find ways to exploit the increasing user base, and this trend has been observed with the rise in LinkedIn scams in recent years [2].  LinkedIn is the dominant professional networking site, with a forecasted 84.1million users by 2027 [3].  This platform is data-driven, so users are encouraged to share information publicly, including personal life updates, to boost visibility and increase job prospects [4] [5].  While this helps legitimate recruiters to gain a good understanding of the user, an attacker could also leverage the same personal content to increase the sophistication and success of their social engineering attempts.  

Darktrace detection of LinkedIn phishing

Darktrace detected a Software-as-a-Service (SaaS) compromise affecting a construction company, where the attack vector originated from LinkedIn (outside the monitoring of corporate security tools), but then pivoted to corporate email where a credential harvesting payload was delivered, providing the attacker with credentials to access a corporate file storage platform.  

Because LinkedIn accounts are typically linked to an individual’s personal email and are most commonly accessed via the mobile application [6] on personal devices that are not monitored by security teams, it can represent an effective initial access point for attackers looking to establish an initial relationship with their target. Moreover, user behaviors to ignore unsolicited emails from new or unknown contacts are less frequently carried over to platforms like LinkedIn, where interactions with ‘weak ties’ as opposed to ‘strong ties’ are a better predictor of job mobility [7]. Had this attack been allowed to continue, the threat actor could have leveraged access to further information from the compromised business cloud account to compromise other high value accounts, exfiltrate sensitive data, or defraud the organization.

LinkedIn phishing attack details

Reconnaissance

The initial reconnaissance and social engineering occurred on LinkedIn and was thus outside the purview of corporate security tools, Darktrace included.

However, the email domain “hausconstruction[.]com” used by the attacker in subsequent communications appears to be a spoofed domain impersonating a legitimate construction company “haus[.]com”, suggesting the attacker may have also impersonated an employee of this construction company on LinkedIn.  In addition to spoofing the domain, the attacker seemingly went further to register “hausconstruction.com” on a commercial web hosting platform.  This is a technique used frequently not just to increase apparent legitimacy, but also to bypass traditional security tools since newly registered domains will have no prior threat intelligence, making them more likely to evade signature and rules-based detections [8].  In this instance, open-source intelligence (OSINT) sources report that the domain was created several months earlier, suggesting this may have been part of a targeted attack on construction companies.  

Initial Intrusion

It was likely that during the correspondence over LinkedIn, the target user was solicited into following up over email regarding a prospective construction project, using their corporate email account.  In a probable attempt to establish a precedent of bi-directional correspondence so that subsequent malicious emails would not be flagged by traditional security tools, the attacker did not initially include suspicious links, attachments or use solicitous or inducive language within their initial emails.

Example of bi-directional email correspondence between the target and the attacker impersonating a legitimate employee of the construction company haus.com.
Figure 1: Example of bi-directional email correspondence between the target and the attacker impersonating a legitimate employee of the construction company haus.com.
Cyber AI Analyst investigation into one of the initial emails the target received from the attacker.
Figure 2: Cyber AI Analyst investigation into one of the initial emails the target received from the attacker.  

To accomplish the next stage of their attack, the attacker shared a link, hidden behind the inducing text “VIEW ALL FILES”, to a malicious file using the Hightail cloud storage service. This is also a common method employed by attackers to evade detection, as this method of file sharing does not involve attachments that can be scanned by traditional security tools, and legitimate cloud storage services are less likely to be blocked.

OSINT analysis on the malicious link link shows the file hosted on Hightail was a HTML file with the associated message “Following up on our LinkedIn conversation”.  Further analysis suggests the file contained obfuscated Javascript that, once opened, would automatically redirect the user to a malicious domain impersonating a legitimate Microsoft login page for credential harvesting purposes.  

The malicious HTML file containing obfuscated Javascript, where the highlighted string references the malicious credential harvesting domain.
Figure 3: The malicious HTML file containing obfuscated Javascript, where the highlighted string references the malicious credential harvesting domain.
Screenshot of fraudulent Microsoft Sign In page hosted on the malicous credential harvesting domain.
Figure 4: Screenshot of fraudulent Microsoft Sign In page hosted on the malicious credential harvesting domain.

Although there was prior email correspondence with the attacker, this email was not automatically deemed safe by Darktrace and was further analyzed for unusual properties and unusual communications for the recipient and the recipient’s peer group.  

Darktrace determined that:

  • It was unusual for this file storage solution to be referenced in communications to the user and the wider network
  • Textual properties of the email body suggested a high level of inducement from the sender, with a high level of focus on the phishing link.
  • The full link contained suspicious properties suggesting it is high risk.
Darktrace’s analysis of the phishing email, presenting key information about the unusual characteristics of this email, information on highlighted content, and an overview of actions that were initially applied.
Figure 5: Darktrace’s analysis of the phishing email, presenting key information about the unusual characteristics of this email, information on highlighted content, and an overview of actions that were initially applied.  

Based on these anomalies, Darktrace initially moved the phishing email to the junk folder and locked the link, preventing the user from directly accessing the malicious file hosted on Hightail.  However, the customer’s security team released the email, likely upon end-user request, allowing the target user to access the file and ultimately enter their credentials into that credential harvesting domain.

Darktrace alerts triggered by the malicious phishing email and the corresponding Autonomous Response actions.
Figure 6: Darktrace alerts triggered by the malicious phishing email and the corresponding Autonomous Response actions.

Lateral Movement

Correspondence between the attacker and target continued for two days after the credential harvesting payload was delivered.  Five days later, Darktrace detected an unusual login using multi-factor authentication (MFA) from a rare external IP and ASN that coincided with Darktrace/Email logs showing access to the credential harvesting link.

This attempt to bypass MFA, known as an Office365 Shell WCSS attack, was likely achieved by inducing the target to enter their credentials and legitimate MFA token into the fake Microsoft login page. This was then relayed to Microsoft by the attacker and used to obtain a legitimate session. The attacker then reused the legitimate token to log into Exchange Online from a different IP and registered their own device for MFA.

Screenshot within Darktrace/Email of the phishing email that was released by the security team, showing the recipient clicked the link to file storage where the malicious payload was stored.
Figure 7: Screenshot within Darktrace/Email of the phishing email that was released by the security team, showing the recipient clicked the link to file storage where the malicious payload was stored.

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Event Log showing a malicious login and MFA bypass at 17:57:16, shortly after the link was clicked.  Highlighted in green is activity from the legitimate user prior to the malicious login, using Edge.
Figure 8: Event Log showing a malicious login and MFA bypass at 17:57:16, shortly after the link was clicked.  Highlighted in green is activity from the legitimate user prior to the malicious login, using Edge. Highlighted in orange and red is the malicious activity using Chrome.

The IP addresses used by the attacker appear to be part of anonymization infrastructure, but are not associated with any known indicators of compromise (IoCs) that signature-based detections would identify [9] [10].

In addition to  logins being observed within half an hour of each other from multiple geographically impossible locations (San Francisco and Phoenix), the unexpected usage of Chrome browser, compared to Edge browser previously used, provided Darktrace with further evidence that this activity was unlikely to originate from the legitimate user.  Although the user was a salesperson who frequently travelled for their role, Darktrace’s Self-Learning AI understood that the multiple logins from these locations was highly unusual at the user and group level, and coupled with the subsequent unexpected account modification, was a likely indicator of account compromise.  

Accomplish mission

Although the email had been manually released by the security team, allowing the attack to propagate, additional layers of defense were triggered as Darktrace's Autonomous Response initiated “Disable User” actions upon detection of the multiple unusual logins and the unauthorized registration of security information.  

However, the customer had configured Autonomous Response to require human confirmation, therefore no actions were taken until the security team manually approved them over two hours later. In that time, access to mail items and other SharePoint files from the unusual IP address was detected, suggesting a potential loss of confidentiality to business data.

Advanced Search query showing several FilePreviewed and MailItemsAccessed events from either the IPs used by the attacker, or using the software Chrome.  Note some of the activity originated from Microsoft IPs which may be whitelisted by traditional security tools.
Figure 9: Advanced Search query showing several FilePreviewed and MailItemsAccessed events from either the IPs used by the attacker, or using the software Chrome.  Note some of the activity originated from Microsoft IPs which may be whitelisted by traditional security tools.

However, it appears that the attacker was able to maintain access to the compromised account, as login and mail access events from 199.231.85[.]153 continued to be observed until the afternoon of the next day.  

Conclusion

This incident demonstrates the necessity of AI to security teams, with Darktrace’s ActiveAI Security Platform detecting a sophisticated phishing attack where human judgement fell short and initiated a real-time response when security teams could not physically respond as fast.  

Security teams are very familiar with social engineering and impersonation attempts, but these attacks remain highly prevalent due to the widespread adoption of technologies that enable these techniques to be deployed with great sophistication and ease.  In particular, the popularity of information-rich platforms like LinkedIn that are geared towards connecting with unknown people make it an attractive initial access point for malicious attackers.

In the second half of 2023 alone, over 200 thousand fake profiles were reported by members on LinkedIn [11].  Fake profiles can be highly sophisticated, use professional images, contain compelling descriptions, reference legitimate company listings and present believable credentials.  

It is unrealistic to expect end users to defend themselves against such sophisticated impersonation attempts. Moreover, it is extremely difficult for human defenders to recognize every fraudulent interaction amidst a sea of fake profiles. Instead, defenders should leverage AI, which can conduct autonomous investigations without human biases and limitations. AI-driven security can ensure successful detection of fraudulent or malicious activity by learning what real users and devices look like and identifying deviations from their learned behaviors that may indicate an emerging threat.

Appendices

Darktrace Model Detections

DETECT/ Apps

SaaS / Compromise / SaaS Anomaly Following Anomalous Login

SaaS / Compromise / Unusual Login and Account Update

SaaS / Unusual Activity / Multiple Unusual External Sources For SaaS Credential

SaaS / Access / Unusual External Source for SaaS Credential Use

SaaS / Compliance / M365 Security Information Modified

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RESPOND/ Apps

Antigena / SaaS / Antigena Suspicious SaaS Activity Block

Antigena / SaaS / Antigena Unusual Activity Block

DETECT & RESPOND/ Email

·      Link / High Risk Link + Low Sender Association

·      Link / New Correspondent Classified Link

·      Link / Watched Link Type

·      Antigena Anomaly

·      Association / Unknown Sender

·      History / New Sender

·      Link / Link to File Storage

·      Link / Link to File Storage + Unknown Sender

·      Link / Low Link Association

List of IoCs

·      142.252.106[.]251 - IP            - Possible malicious IP used by attacker during cloud account compromise

·      199.231.85[.]153 – IP - Probable malicious IP used by attacker during cloud account compromise

·      vukoqo.hebakyon[.]com – Endpoint - Credential harvesting endpoint

MITRE ATT&CK Mapping

·      Resource Development - T1586 - Compromise Accounts

·      Resource Development - T1598.003 – Spearphishing Link

·      Persistence - T1078.004 - Cloud Accounts

·      Persistence - T1556.006 - Modify Authentication Process: Multi-Factor Authentication

·      Reconnaissance - T1593.001 – Social Media

·      Reconnaissance - T1598 – Phishing for Information

·      Reconnaissance - T1589.001 – Credentials

·      Reconnaissance - T1591.002 – Business Relationships

·      Collection - T1111 – Multifactor Authentication Interception

·      Collection - T1539 – Steal Web Session Cookie

·      Lateral Movement - T1021.007 – Cloud Services

·      Lateral Movement - T1213.002 - Sharepoint

References

[1] Jessica Barker, Hacked: The secrets behind cyber attacks, (London: Kogan Page, 2024), p. 130-146.

[2] https://www.bitdefender.co.uk/blog/hotforsecurity/5-linkedin-scams-and-how-to-avoid-them/

[3] https://www.washingtonpost.com/technology/2023/08/31/linkedin-personal-posts/

[4] https://www.forbes.com/sites/joshbersin/2012/05/21/facebook-vs-linkedin-whats-the-difference/

[5] https://thelinkedblog.com/2022/3-reasons-why-you-should-make-your-profile-public-1248/

[6] https://www.linkedin.com/pulse/50-linkedin-statistics-every-professional-should-ti9ue

[7] https://www.nytimes.com/2022/09/24/business/linkedin-social-experiments.html

[8] https://darktrace.com/blog/the-domain-game-how-email-attackers-are-buying-their-way-into-inboxes

[9] https://spur.us/context/142.252.106[.]251

[10] https://spur.us/context/199.231.85[.]153

[11]https://www.statista.com/statistics/1328849/linkedin-number-of-fake-accounts-detected-and-removed

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
Nicole Wong
Cyber Security 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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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
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