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March 19, 2024

Pikabot Malware: Insights, Impact, & Attack Analysis

Learn about Pikabot malware and its rapid evolution in the wild, impacting organizations and how to defend against this growing threat.
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Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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19
Mar 2024

How does Loader Malware work?

Throughout 2023, the Darktrace Threat Research team identified and investigated multiple strains of loader malware affecting customers across its fleet. These malicious programs typically serve as a gateway for threat actors to gain initial access to an organization’s network, paving the way for subsequent attacks, including additional malware infections or disruptive ransomware attacks.

How to defend against loader malware

The prevalence of such initial access threats highlights the need for organizations to defend against multi-phase compromises, where modular malware swiftly progresses from one stage of an attack to the next. One notable example observed in 2023 was Pikabot, a versatile loader malware used for initial access and often accompanied by secondary compromises like Cobalt Strike and Black Basta ransomware.

While Darktrace initially investigated multiple instances of campaign-like activity associated with Pikabot during the summer of 2023, a new campaign emerged in October which was observed targeting a Darktrace customer in Europe. Thanks to the timely detection by Darktrace DETECT™ and the support of Darktrace’s Security Operations Center (SOC), the Pikabot compromise was quickly shut down before it could escalate into a more disruptive attack.

What is Pikabot?

Pikabot is one of the latest modular loader malware strains that has been active since the first half of 2023, with several evolutions in its methodology observed in the months since. Initial researchers noted similarities to the Qakbot aka Qbot or Pinkslipbot and Mantanbuchus malware families, and while Pikabot appears to be a new malware in early development, it shares multiple commonalities with Qakbot [1].

First, both Pikabot and Qakbot have similar distribution methods, can be used for multi-stage attacks, and are often accompanied by downloads of Cobalt Strike and other malware strains. The threat actor known as TA577, which has also been referred to as Water Curupira, has been seen to use both types of malware in spam campaigns which can lead to Black Basta ransomware attacks [2] [3].Notably, a rise in Pikabot campaigns were observed in September and October 2023, shortly after the takedown of Qakbot in Operation Duck Hunt, suggesting that Pikabot may be serving as a replacement for initial access to target network [4].

How does Pikabot malware work?

Many Pikabot infections start with a malicious email, particularly using email thread hijacking; however, other cases have been distributed via malspam and malvertising [5]. Once downloaded, Pikabot runs anti-analysis techniques and checks the system’s language, self-terminating if the language matches that of a Commonwealth of Independent States (CIS) country, such as Russian or Ukrainian. It will then gather key information to send to a command-and-control (C2) server, at which point additional payload downloads may be observed [2]. Early response to a Pikabot infection is important for organizations to prevent escalation to a significant compromise such as ransomware.

Darktrace’s Coverage of Pikabot malware

Between April and July 2023, the Darktrace Threat Research team investigated Pikabot infections affected more than 15 customer environments; these attacks primarily targeted US and European organizations spanning multiple industries, and most followed the below lifecycle:

  1. Initial access via malspam or email, often outside of Darktrace’s scope
  2. Suspicious executable download from a URI in the format /\/[a-z0-9A-Z]{3,}\/[a-z0-9A-Z]{5,}/ and using a Windows PowerShell user agent
  3. C2 connections to IP addresses on uncommon ports including 1194 and 2078
  4. Some cases involved further C2 activity to Cobalt Strike endpoints

In October 2023, a second campaign emerged that largely followed the same attack pattern, with a notable difference that cURL was used for the initial payload download as opposed to PowerShell. All the Pikabot cases that Darktrace has observed since October 2023 have used cURL, which could indicate a shift in approach from targeting Windows devices to multi-operating system environments.

Figure 1: Timeline of the Pikabot infection over a 2-hour period.

On October 17, 2023, Darktrace observed a Pikabot infection on the network of a European customer after an internal user seemingly clicked a malicious link in a phishing email, thereby compromising their device. As the customer did not have Darktrace/Email™ deployed on their network, Darktrace did not have visibility over the email. Despite this, DETECT was still able to provide full visibility over the network-based activity that ensued.

Darktrace observed the device using a cURL user agent when initiating the download of an unusual executable (.exe) file from an IP address that had never previously been observed on the network. Darktrace further recognized that the executable file was attempting to masquerade as a different file type, likely to evade the detection of security teams and their security tools. Within one minute, the device began to communicate with additional unusual IP addresses on uncommon ports (185.106.94[.]174:5000 and 80.85.140[.]152:5938), both of which have been noted by open-source intelligence (OSINT) vendors as Pikabot C2 servers [6] [7].

Figure 2: Darktrace model breach Event Log showing the initial file download, immediately followed by a connection attempt to a Pikabot C2 server.

Around 40 minutes after the initial download, Darktrace detected the device performing suspicious DNS tunneling using a pattern that resembled the Cobalt Strike Beacon. This was accompanied by beaconing activity to a rare domain, ‘wordstt182[.]com’, which was registered only 4 days prior to this activity [8]. Darktrace observed additional DNS connections to the endpoint, ‘building4business[.]net’, which had been linked to Black Basta ransomware [2].

Figure 3: The affected device making successful TXT DNS requests to known Black Basta endpoints.

As this customer had integrated Darktrace with the Microsoft Defender, Defender was able to contextualize the DETECT model breaches with endpoint insights, such as known threats and malware, providing customers with unparalleled visibility of the host-level detections surrounding network-level anomalies.

In this case, the behavior of the affected device triggered multiple Microsoft Defender alerts, including one alert which linked the activity to the threat actor Storm-0464, another name for TA577 and Water Curupira. These insights were presented to the customer in the form of a Security Integration alert, allowing them to build a full picture of the ongoing incident.

Figure 4: Security Integration alert from Microsoft Defender in Darktrace, linking the observed activity to the threat group Storm-0464.

As the customer had subscribed to Darktrace’s Proactive Threat Notification (PTN) service, the customer received timely alerts from Darktrace’s SOC notifying them of the suspicious activity associated with Pikabot. This allowed the customer’s security team to quickly identify the affected device and remove it from their environment for remediation.

Although the customer did have Darktrace RESPOND™ enabled on their network, it was configured in human confirmation mode, requiring manual application for any RESPOND actions. RESPOND had suggested numerous actions to interrupt and contain the attack, including blocking connections to the observed Pikabot C2 addresses, which were manually actioned by the customer’s security team after the fact. Had RESPOND been enabled in autonomous response mode during the attack, it would have autonomously blocked these C2 connections and prevented the download of any suspicious files, effectively halting the escalation of the attack.

Nonetheless, Darktrace DETECT’s prompt identification and alerting of this incident played a crucial role in enabling the customer to mitigate the threat of Pikabot, preventing it from progressing into a disruptive ransomware attack.

Figure 5: Darktrace RESPOND actions recommended from the initial file download and throughout the C2 traffic, ranging from blocking specific connections to IP addresses and ports to enforcing a normal pattern of life for the source device.

Conclusion

Pikabot is just one recent example of a modular strain of loader known for its adaptability and speed, seamlessly changing tactics from one campaign to the next and utilizing new infrastructure to initiate multi-stage attacks. Leveraging commonly used tools and services like Windows PowerShell and cURL, alongside anti-analysis techniques, this malware can evade the detection and often bypass traditional security tools.

In this incident, Darktrace detected a Pikabot infection in its early stages, identifying an anomalous file download using a cURL user agent, a new tactic for this particular strain of malware. This timely detection, coupled with the support of Darktrace’s SOC, empowered the customer to quickly identify the compromised device and act against it, thwarting threat actors attempting to connect to malicious Cobalt Strike and Black Basta servers. By preventing the escalation of the attack, including potential ransomware deployment, the customer’s environment remained safeguarded.

Had Darktrace RESPOND been enabled in autonomous response mode at the time of this attack, it would have been able to further support the customer by applying targeted mitigative actions to contain the threat of Pikabot at its onset, bolstering their defenses even more effectively.

Credit to Brianna Leddy, Director of Analysis, Signe Zaharka, Senior Cyber Security Analyst

Appendix

Darktrace DETECT Models

Anomalous Connection / Anomalous SSL without SNI to New External

Anomalous Connection / Application Protocol on Uncommon Port

Anomalous Connection / Multiple Connections to New External TCP Port

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous Connection / Powershell to Rare External

Anomalous Connection / Rare External SSL Self-Signed

Anomalous Connection / Repeated Rare External SSL Self-Signed

Anomalous File / EXE from Rare External Location

Anomalous File / Masqueraded File Transfer

Anomalous File / Multiple EXE from Rare External Locations

Compromise / Agent Beacon to New Endpoint

Compromise / Beacon to Young Endpoint

Compromise / Beaconing Activity To External Rare

Compromise / DNS / DNS Tunnel with TXT Records

Compromise / New or Repeated to Unusual SSL Port

Compromise / SSL Beaconing to Rare Destination

Compromise / Suspicious Beaconing Behaviour

Compromise / Suspicious File and C2

Device / Initial Breach Chain Compromise

Device / Large Number of Model Breaches

Device / New PowerShell User Agent

Device / New User Agent

Device / New User Agent and New IP

Device / Suspicious Domain

Security Integration / C2 Activity and Integration Detection

Security Integration / Egress and Integration Detection

Security Integration / High Severity Integration Detection

Security Integration / High Severity Integration Incident

Security Integration / Low Severity Integration Detection

Security Integration / Low Severity Integration Incident

Antigena / Network / External Threat / Antigena File then New Outbound Block

Antigena / Network / External Threat / Antigena Suspicious Activity Block

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / Significant Anomaly / Antigena Breaches Over Time Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Breach

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

Antigena / Network / Significant Anomaly / Antigena Significant Security Integration and Network Activity Block

List of Indicators of Compromise (IoC)

IOC - TYPE - DESCRIPTION + CONFIDENCE

128.140.102[.]132 - IP Address - Pikabot Download

185.106.94[.]174:5000 - IP Address: Port - Pikabot C2 Endpoint

80.85.140[.]152:5938 - IP Address: Port - Pikabot C2 Endpoint

building4business[.]net - Hostname - Cobalt Strike DNS Beacon

wordstt182[.]com - Hostname - Cobalt Strike Server

167.88.166[.]109 - IP Address - Cobalt Strike Server

192.9.135[.]73 - IP - Pikabot C2 Endpoint

192.121.17[.]68 - IP - Pikabot C2 Endpoint

185.87.148[.]132 - IP - Pikabot C2 Endpoint

129.153.22[.]231 - IP - Pikabot C2 Endpoint

129.153.135[.]83 - IP - Pikabot C2 Endpoint

154.80.229[.]76 - IP - Pikabot C2 Endpoint

192.121.17[.]14 - IP - Pikabot C2 Endpoint

162.252.172[.]253 - IP - Pikabot C2 Endpoint

103.124.105[.]147 - IP - Likely Pikabot Download

178.18.246[.]136 - IP - Pikabot C2 Endpoint

86.38.225[.]106 - IP - Pikabot C2 Endpoint

198.44.187[.]12 - IP - Pikabot C2 Endpoint

154.12.233[.]66 - IP - Pikabot C2 Endpoint

MITRE ATT&CK Mapping

TACTIC - TECHNIQUE

Defense Evasion - Masquerading: Masquerade File Type (T1036.008)

Command and Control - Application Layer Protocol: Web Protocols (T1071.001)

Command and Control - Non-Standard Port (T1571)

Command and Control - Application Layer Protocol: DNS (T1071.004)

Command and Control - Protocol Tunneling (T1572)

References

[1] https://news.sophos.com/en-us/2023/06/12/deep-dive-into-the-pikabot-cyber-threat/?&web_view=true  

[2] https://www.trendmicro.com/en_be/research/24/a/a-look-into-pikabot-spam-wave-campaign.html

[3] https://thehackernews.com/2024/01/alert-water-curupira-hackers-actively.html

[4] https://www.darkreading.com/cyberattacks-data-breaches/pikabot-malware-qakbot-replacement-black-basta-attacks

[5] https://www.redpacketsecurity.com/pikabot-distributed-via-malicious-ads-6/

[6] https://www.virustotal.com/gui/ip-address/185.106.94.174/detection

[7] https://www.virustotal.com/gui/ip-address/80.85.140.152/detection

[8] https://www.domainiq.com/domain?wordstt182.com

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Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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February 3, 2026

Introducing Darktrace / SECURE AI: Complete AI Security Across Your Enterprise

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Why securing AI can’t wait

AI is entering the enterprise faster than IT and security teams can keep up, appearing in SaaS tools, embedded in core platforms, and spun up by teams eager to move faster.  

As this adoption accelerates, it introduces unpredictable behaviors and expands the attack surface in ways existing security tools can’t see or control, startup or platform, they all lack one trait. These new types of risks command the attention of security teams and boardrooms, touching everything from business integrity to regulatory exposure.

Securing AI demands a fundamentally different approach, one that understands how AI behaves, how it interacts with data and users, and how risk emerges in real time. That shift is at the core of how organizations should be thinking about securing AI across the enterprise.

What is the current state of securing AI?

In Darktrace’s latest State of AI in Cybersecurity Report research across 1,500 cybersecurity professionals shows that the percentage of organizations without an AI adoption policy grew from 55% last year to 63% this year.

More troubling, the percentage of organizations without any plan to create an AI policy nearly tripled from 3% to 8%. Without clear policies, businesses are effectively accelerating blindfolded.

When we analyzed activity across our own customer base, we saw the same patterns playing out in their environments. Last October alone, we saw a 39% month-over-month increase in anomalous data uploads to generative AI services, with the average upload being 75MB. Given the size and frequency of these uploads, it's almost certain that much of this data should never be leaving the enterprise.

Many security teams still lack visibility into how AI is being used across their business; how it’s behaving, what it’s accessing, and most importantly, whether it’s operating safely. This unsanctioned usage quietly expands, creating pockets of AI activity that fall completely outside established security controls. The result is real organizational exposure with almost no visibility, underscoring just how widespread AI use has already become given the absence of formal policies.

This challenge doesn’t stop internally. Shadow AI extends into third-party tools, vendor platforms, and partner systems, where AI features are embedded without clear oversight.

Meanwhile, attackers are now learning to exploit AI’s unique characteristics, compounding the risks organizations are already struggling to manage.

The leader in AI cybersecurity now secures AI

Darktrace brings more than a decade of behavioral AI expertise built on an enterprise‑wide platform designed to operate in the complex, ambiguous environments where today’s AI now lives.  

Other cybersecurity technologies try to predict each new attack based on historical attacks. The problem is AI operates like humans do. Every action introduces new information that changes how AI behaves, its unpredictable, and historical attack tactics are now only a small part of the equation, forcing vendors to retrofit unproven acquisitions to secure AI.  

Darktrace is fundamentally different. Our Self‑Learning AI learns what “normal” looks like for your unique business: how your users, systems, applications, and now AI agents behave, how they communicate, and how data flows. This allows us to spot even the smallest shifts when something changes in meaningful ways. Long before AI agents were introduced, our technology was already interpreting nuance, detecting drift, uncovering hidden relationships, and making sense of ambiguous activity across networks, cloud, SaaS, email, OT, identities, and endpoints.

As AI introduces new behaviors, unstructured interactions, invisible pathways, and the rise of Shadow AI, these challenges have only intensified. But this is exactly the environment our platform was built for. Securing AI isn’t a new direction for Darktrace — it’s the natural evolution of the behavioral intelligence we’ve delivered to thousands of organizations worldwide.

Introducing Darktrace / SECURE AI – Complete AI security across your enterprise

We are proud to introduce Darktrace / SECURE AI, the newest product in the Darktrace ActiveAI Security Platform designed to secure AI across the whole enterprise.

This marks the next chapter in our mission to secure organizations from cyber threats and emerging risks. By combining full visibility, intelligent behavioral oversight, and real-time control, Darktrace is enabling enterprises to safely adopt, manage, and build AI within their business. This ensures that AI usage, data access, and behavior remain aligned to security baselines, compliance, and business goals.

Darktrace / SECURE AI can bring every AI interaction into a single view, helping teams understand intent, assess risk, protect sensitive data, and enforce policy across both human and AI Agent activity. Now organizations can embrace AI with confidence, with visibility to ensure it is operating safely, responsibly, and in alignment with their security and compliance needs.  

Because securing AI spans multiple areas and layers of complexity, Darktrace / SECURE AI is built around four foundational use cases that ensure your whole enterprise and every AI use affecting your business, whether owned or through third parties, is protected, they are:

  • Monitoring the prompts driving GenAI agents and assistants
  • Securing business AI agent identities in real time
  • Evaluating AI risks in development and deployment
  • Discovering and controlling Shadow AI

Monitoring the prompts driving GenAI agents and assistants

For AI systems, prompts are one of the most active and sensitive points of interaction—spanning human‑AI exchanges where users express intent and AI‑AI interactions where agents generate internal prompts to reason and coordinate. Because prompt language effectively is behavior, and because it relies on natural language rather than a fixed, finite syntax, the attack surface is open‑ended. This makes prompt‑driven risks far more complex than traditional API‑based vulnerabilities tied to CVEs.

Whether an attacker is probing for weaknesses, an employee inadvertently exposes sensitive data, or agents generate their own sub‑tasks to drive complex workflows, security teams must understand how prompt behavior shapes model behavior—and where that behavior can go wrong. Without that behavioral understanding, organizations face heightened risks of exploitation, drift, and cascading failures within their AI systems.

Darktrace / SECURE AI brings together all prompt activity across enterprise AI systems, including Microsoft Copilot and ChatGPT Enterprise, low‑code environments like Microsoft Copilot Studio, SaaS providers like Salesforce and Microsoft 365, and high‑code platforms such as AWS Bedrock and SageMaker, into a single, unified layer of visibility.  

Beyond visibility, Darktrace applies behavioral analytics to understand whether a prompt is unusual or risky in the context of the user, their peers, and the broader organization. Because AI attacks are far more complex and conversational than traditional exploits against fixed APIs – sharing more in common with email and Teams/Slack interactions, —this behavioral understanding is essential. By treating prompts as behavioral signals, Darktrace can detect conversational attacks, malicious chaining, and subtle prompt‑injection attempts, and where integrations allow, intervene in real time to block unsafe prompts or prevent harmful model actions as they occur.

Securing business AI agent identities in real time

As organizations adopt more AI‑driven workflows, we’re seeing a rapid rise in autonomous and semi‑autonomous agents operating across the business. These agents operate within existing identities, with the capability to access systems, read and write data, and trigger actions across cloud platforms, internal infrastructure, applications, APIs, and third‑party services. Some identities are controlled, like users, others like the ones mentioned, can appear anywhere, with organizations having limited visibility into how they’re configured or how their permissions evolve over time.  

Darktrace / SECURE AI gives organizations a real‑time, identity‑centric understanding of what their AI agents are doing, not just what they were designed to do. It automatically discovers live agent identities operating across SaaS, cloud, network, endpoints, OT, and email, including those running inside third‑party environments.  

The platform maps how each agent is configured, what systems it accesses, and how it communicates, including activity such as MCP usage or interactions with storage services where sensitive data may reside.  

By continuously observing agent behavior across all domains, Darktrace / SECURE AI highlights when unnecessary or risky permissions are granted, when activity patterns deviate, or when agents begin chaining together actions in unintended ways. This real‑time audit trail allows organizations to evaluate whether agent actions align with intended operational parameters and catch anomalous or risky behavior early.    

Evaluating AI risks in development and deployment

In the build phase, new identities are created, entitlements accumulate, components are stitched together across SaaS, cloud, and internal environments, and logic starts taking shape through prompts and configurations.  

It’s a highly dynamic and often fragmented process, and even small missteps here, such as a misconfiguration in a created agent identity, can become major security issues once the system is deployed. This is why evaluating AI risk during development and deployment is critical.

Darktrace / SECURE AI brings clarity and control across this entire lifecycle — from the moment an AI system starts taking shape to the moment it goes live. It allows you to gain visibility into created identities and their access across hyperscalers, low‑code SaaS, and internal labs, supported by AI security posture management that surfaces misconfigurations, over‑entitlement, and anomalous building events. Darktrace/ SECURE AI then connects these development insights directly to prompt oversight, connecting how AI is being built to how it will behave once deployed.  The result is a safer, more predictable AI lifecycle where risks are discovered early, guardrails are applied consistently, and innovations move forward with confidence rather than guesswork.

Discovering and controlling Shadow AI

Shadow AI has now appeared across every corner of the enterprise. It’s not just an employee pasting internal data into an external chatbot; it includes unsanctioned agent builders, hidden MCP servers, rogue model deployments, and AI‑driven workflows running on devices or services no one expected to be using AI.  

Darktrace / SECURE AI brings this frontier into view by continuously analyzing interactions across cloud, networks, endpoints, OT, and SASE environments. It surfaces unapproved AI usage wherever it appears and distinguishes legitimate activity in sanctioned tools from misuse or high‑risk behavior. The system identifies hidden AI components and rogue agents, reveals unauthorized deployments and unexpected connections to external AI systems, and highlights risky data flows that deviate from business norms.

When the behavior warrants a response, Darktrace / SECURE AI enables policy enforcement that guides users back toward sanctioned options while containing unsafe or ungoverned adoption. This closes one of the fastest‑expanding security gaps in modern enterprises and significantly reduces the attack surface created by shadow AI.

Conclusion

What’s needed now along with policies and frameworks for AI adoption is the right tooling to detect threats based on AI behavior across shadow use, prompt risks, identity misuse, and AI development.  

Darktrace is uniquely positioned to secure AI, we’ve spent over a decade building AI that learns your business – understanding subtle behavior across the entire enterprise long before AI agents arrived. With over 10,000 customers relying on Darktrace as the last line of defense to capture threats others cannot, Securing AI isn’t a pivot for us, it's not an acquisition; it’s the natural extension of the behavioral expertise and enterprise‑wide intelligence our platform was built on from the start.  

To learn more about how to secure AI at your organization we curated a readiness program that brings together IT and security leaders navigating this responsibility, providing a forum to prepare for high-impact decisions, explore guardrails, and guide the business amid growing uncertainty and pressure.

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

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About the author
Brittany Woodsmall
Product Marketing Manager, AI

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February 1, 2026

ClearFake: From Fake CAPTCHAs to Blockchain-Driven Payload Retrieval

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What is ClearFake?

As threat actors evolve their techniques to exploit victims and breach target networks, the ClearFake campaign has emerged as a significant illustration of this continued adaptation. ClearFake is a campaign observed using a malicious JavaScript framework deployed on compromised websites, impacting sectors such as e‑commerce, travel, and automotive. First identified in mid‑2023, ClearFake is frequently leveraged to socially engineer victims into installing fake web browser updates.

In ClearFake compromises, victims are steered toward compromised WordPress sites, often positioned by attackers through search engine optimization (SEO) poisoning. Once on the site, users are presented with a fake CAPTCHA. This counterfeit challenge is designed to appear legitimate while enabling the execution of malicious code. When a victim interacts with the CAPTCHA, a PowerShell command containing a download string is retrieved and executed.

Attackers commonly abuse the legitimate Microsoft HTML Application Host (MSHTA) in these operations. Recent campaigns have also incorporated Smart Chain endpoints, such as “bsc-dataseed.binance[.]org,” to obtain configuration code. The primary payload delivered through ClearFake is typically an information stealer, such as Lumma Stealer, enabling credential theft, data exfiltration, and persistent access [1].

Darktrace’s Coverage of ClearFake

Darktrace / ENDPOINT first detected activity likely associated with ClearFake on a single device on over the course of one day on November 18, 2025. The system observed the execution of “mshta.exe,” the legitimate Microsoft HTML Application Host utility. It also noted a repeated process command referencing “weiss.neighb0rrol1[.]ru”, indicating suspicious external activity. Subsequent analysis of this endpoint using open‑source intelligence (OSINT) indicated that it was a malicious, domain generation algorithm (DGA) endpoint [2].

The process line referencing weiss.neighb0rrol1[.]ru, as observed by Darktrace / ENDPOINT.
Figure 1: The process line referencing weiss.neighb0rrol1[.]ru, as observed by Darktrace / ENDPOINT.

This activity indicates that mshta.exe was used to contact a remote server, “weiss.neighb0rrol1[.]ru/rpxacc64mshta,” and execute the associated HTA file to initiate the next stage of the attack. OSINT sources have since heavily flagged this server as potentially malicious [3].

The first argument in this process uses the MSHTA utility to execute the HTA file hosted on the remote server. If successful, MSHTA would then run JavaScript or VBScript to launch PowerShell commands used to retrieve malicious payloads, a technique observed in previous ClearFake campaigns. Darktrace also detected unusual activity involving additional Microsoft executables, including “winlogon.exe,” “userinit.exe,” and “explorer.exe.” Although these binaries are legitimate components of the Windows operating system, threat actors can abuse their normal behavior within the Windows login sequence to gain control over user sessions, similar to the misuse of mshta.exe.

EtherHiding cover

Darktrace also identified additional ClearFake‑related activity, specifically a connection to bsc-testnet.drpc[.]org, a legitimate BNB Smart Chain endpoint. This activity was triggered by injected JavaScript on the compromised site www.allstarsuae[.]com, where the script initiated an eth_call POST request to the Smart Chain endpoint.

Example of a fake CAPTCHA on the compromised site www.allstarsuae[.]com.
Figure 2: Example of a fake CAPTCHA on the compromised site www.allstarsuae[.]com.

EtherHiding is a technique in which threat actors leverage blockchain technology, specifically smart contracts, as part of their malicious infrastructure. Because blockchain is anonymous, decentralized, and highly persistent, it provides threat actors with advantages in evading defensive measures and traditional tracking [4].

In this case, when a user visits a compromised WordPress site, injected base64‑encoded JavaScript retrieved an ABI string, which was then used to load and execute a contract hosted on the BNB Smart Chain.

JavaScript hosted on the compromised site www.allstaruae[.]com.
Figure 3: JavaScript hosted on the compromised site www.allstaruae[.]com.

Conducting malware analysis on this instance, the Base64 decoded into a JavaScript loader. A POST request to bsc-testnet.drpc[.]org was then used to retrieve a hex‑encoded ABI string that loads and executes the contract. The JavaScript also contained hex and Base64‑encoded functions that decoded into additional JavaScript, which attempted to retrieve a payload hosted on GitHub at “github[.]com/PrivateC0de/obf/main/payload.txt.” However, this payload was unavailable at the time of analysis.

Darktrace’s detection of the POST request to bsc-testnet.drpc[.]org.
Figure 4: Darktrace’s detection of the POST request to bsc-testnet.drpc[.]org.
Figure 5: Darktrace’s detection of the executable file and the malicious hostname.

Autonomous Response

As Darktrace’s Autonomous Response capability was enabled on this customer’s network, Darktrace was able to take swift mitigative action to contain the ClearFake‑related activity early, before it could lead to potential payload delivery. The affected device was blocked from making external connections to a number of suspicious endpoints, including 188.114.96[.]6, *.neighb0rrol1[.]ru, and neighb0rrol1[.]ru, ensuring that no further malicious connections could be made and no payloads could be retrieved.

Autonomous Response also acted to prevent the executable mshta.exe from initiating HTA file execution over HTTPS from this endpoint by blocking the attempted connections. Had these files executed successfully, the attack would likely have resulted in the retrieval of an information stealer, such as Lumma Stealer.

Autonomous Response’s intervention against the suspicious connectivity observed.
Figure 6: Autonomous Response’s intervention against the suspicious connectivity observed.

Conclusion

ClearFake continues to be observed across multiple sectors, but Darktrace remains well‑positioned to counter such threats. Because ClearFake’s end goal is often to deliver malware such as information stealers and malware loaders, early disruption is critical to preventing compromise. Users should remain aware of this activity and vigilant regarding fake CAPTCHA pop‑ups. They should also monitor unusual usage of MSHTA and outbound connections to domains that mimic formats such as “bsc-dataseed.binance[.]org” [1].

In this case, Darktrace was able to contain the attack before it could successfully escalate and execute. The attempted execution of HTA files was detected early, allowing Autonomous Response to intervene, stopping the activity from progressing. As soon as the device began communicating with weiss.neighb0rrol1[.]ru, an Autonomous Response inhibitor triggered and interrupted the connections.

As ClearFake continues to rise, users should stay alert to social engineering techniques, including ClickFix, that rely on deceptive security prompts.

Credit to Vivek Rajan (Senior Cyber Analyst) and Tara Gould (Malware Research Lead)

Edited by Ryan Traill (Analyst Content Lead)

Appendices

Darktrace Model Detections

Process / New Executable Launched

Endpoint / Anomalous Use of Scripting Process

Endpoint / New Suspicious Executable Launched

Endpoint / Process Connection::Unusual Connection from New Process

Autonomous Response Models

Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block

List of Indicators of Compromise (IoCs)

  • weiss.neighb0rrol1[.]ru – URL - Malicious Domain
  • 188.114.96[.]6 – IP – Suspicious Domain
  • *.neighb0rrol1[.]ru – URL – Malicious Domain

MITRE Tactics

Initial Access, Drive-by Compromise, T1189

User Execution, Execution, T1204

Software Deployment Tools, Execution and Lateral Movement, T1072

Command and Scripting Interpreter, T1059

System Binary Proxy Execution: MSHTA, T1218.005

References

1.        https://www.kroll.com/en/publications/cyber/rapid-evolution-of-clearfake-delivery

2.        https://www.virustotal.com/gui/domain/weiss.neighb0rrol1.ru

3.        https://www.virustotal.com/gui/file/1f1aabe87e5e93a8fff769bf3614dd559c51c80fc045e11868f3843d9a004d1e/community

4.        https://www.packetlabs.net/posts/etherhiding-a-new-tactic-for-hiding-malware-on-the-blockchain/

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About the author
Vivek Rajan
Cyber Analyst
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