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June 5, 2025

Unpacking ClickFix: Darktrace’s detection of a prolific social engineering tactic

ClickFix is a social engineering technique that exploits human error through fake prompts, leading users to unknowingly run malicious commands. Learn how Darktrace detects and responds to such threats!
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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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05
Jun 2025

What is ClickFix and how does it work?

Amid heightened security awareness, threat actors continue to seek stealthy methods to infiltrate target networks, often finding the human end user to be the most vulnerable and easily exploited entry point.

ClickFix baiting is an exploitation of the end user, making use of social engineering techniques masquerading as error messages or routine verification processes, that can result in malicious code execution.

Since March 2024, the simplicity of this technique has drawn attention from a range of threat actors, from individual cybercriminals to Advanced Persistent Threat (APT) groups such as APT28 and MuddyWater, linked to Russia and Iran respectively, introducing security threats on a broader scale [1]. ClickFix campaigns have been observed affecting organizations in across multiple industries, including healthcare, hospitality, automotive and government [2][3].

Actors carrying out these targeted attacks typically utilize similar techniques, tools and procedures (TTPs) to gain initial access. These include spear phishing attacks, drive-by compromises, or exploiting trust in familiar online platforms, such as GitHub, to deliver malicious payloads [2][3]. Often, a hidden link within an email or malvertisements on compromised legitimate websites redirect the end user to a malicious URL [4]. These take the form of ‘Fix It’ or fake CAPTCHA prompts [4].

From there, users are misled into believing they are completing a human verification step, registering a device, or fixing a non-existent issue such as a webpage display error. As a result, they are guided through a three-step process that ultimately enables the execution of malicious PowerShell commands:

  1. Open a Windows Run dialog box [press Windows Key + R]
  2. Automatically or manually copy and paste a malicious PowerShell command into the terminal [press CTRL+V]
  3. And run the prompt [press ‘Enter’] [2]

Once the malicious PowerShell command is executed, threat actors then establish command and control (C2) communication within the targeted environment before moving laterally through the network with the intent of obtaining and stealing sensitive data [4]. Malicious payloads associated with various malware families, such as XWorm, Lumma, and AsyncRAT, are often deployed [2][3].

Attack timeline of ClickFix cyber attack

Based on investigations conducted by Darktrace’s Threat Research team in early 2025, this blog highlights Darktrace’s capability to detect ClickFix baiting activity following initial access.

Darktrace’s coverage of a ClickFix attack chain

Darktrace identified multiple ClickFix attacks across customer environments in both Europe, the Middle East, and Africa (EMEA) and the United States. The following incident details a specific attack on a customer network that occurred on April 9, 2025.

Although the initial access phase of this specific attack occurred outside Darktrace’s visibility, other affected networks showed compromise beginning with phishing emails or fake CAPTCHA prompts that led users to execute malicious PowerShell commands.

Darktrace’s visibility into the compromise began when the threat actor initiated external communication with their C2 infrastructure, with Darktrace / NETWORK detecting the use of a new PowerShell user agent, indicating an attempt at remote code execution.

Darktrace / NETWORK's detection of a device making an HTTP connection with new PowerShell user agent, indicating PowerShell abuse for C2 communications.
Figure 1: Darktrace / NETWORK's detection of a device making an HTTP connection with new PowerShell user agent, indicating PowerShell abuse for C2 communications.

Download of Malicious Files for Lateral Movement

A few minutes later, the compromised device was observed downloading a numerically named file. Numeric files like this are often intentionally nondescript and associated with malware. In this case, the file name adhered to a specific pattern, matching the regular expression: /174(\d){7}/. Further investigation into the file revealed that it contained additional malicious code designed to further exploit remote services and gather device information.

Figure 2: Darktrace / NETWORK's detection of a numeric file, one minute after the new PowerShell User Agent alert.

The file contained a script that sent system information to a specified IP address using an HTTP POST request, which also processed the response. This process was verified through packet capture (PCAP) analysis conducted by the Darktrace Threat Research team.

By analyzing the body content of the HTTP GET request, it was observed that the command converts the current time to Unix epoch time format (i.e., 9 April 2025 13:26:40 GMT), resulting in an additional numeric file observed in the URI: /1744205200.

PCAP highlighting the HTTP GET request that sends information to the specific IP, 193.36.38[.]237, which then generates another numeric file titled per the current time.
Figure 3: PCAP highlighting the HTTP GET request that sends information to the specific IP, 193.36.38[.]237, which then generates another numeric file titled per the current time.

Across Darktrace’s investigations into other customers' affected by ClickFix campaigns, both internal information discovery events and further execution of malicious code were observed.

Data Exfiltration

By following the HTTP stream in the same PCAP, the Darktrace Threat Research Team assessed the activity as indicative of data exfiltration involving system and device information to the same command-and-control (C2) endpoint, , 193.36.38[.]237. This endpoint was flagged as malicious by multiple open-source intelligence (OSINT) vendors [5].

PCAP highlighting HTTP POST connection with the numeric file per the URI /1744205200 that indicates data exfiltration to 193.36.38[.]237.
Figure 4: PCAP highlighting HTTP POST connection with the numeric file per the URI /1744205200 that indicates data exfiltration to 193.36.38[.]237.

Further analysis of Darktrace’s Advanced Search logs showed that the attacker’s malicious code scanned for internal system information, which was then sent to a C2 server via an HTTP POST request, indicating data exfiltration

Advanced Search further highlights Darktrace's observation of the HTTP POST request, with the second numeric file representing data exfiltration.
Figure 5: Advanced Search further highlights Darktrace's observation of the HTTP POST request, with the second numeric file representing data exfiltration.

Actions on objectives

Around ten minutes after the initial C2 communications, the compromised device was observed connecting to an additional rare endpoint, 188.34.195[.]44. Further analysis of this endpoint confirmed its association with ClickFix campaigns, with several OSINT vendors linking it to previously reported attacks [6].

In the final HTTP POST request made by the device, Darktrace detected a file at the URI /init1234 in the connection logs to the malicious endpoint 188.34.195[.]44, likely depicting the successful completion of the attack’s objective, automated data egress to a ClickFix C2 server.

Darktrace / NETWORK grouped together the observed indicators of compromise (IoCs) on the compromised device and triggered an Enhanced Monitoring model alert, a high-priority detection model designed to identify activity indicative of the early stages of an attack. These models are monitored and triaged 24/7 by Darktrace’s Security Operations Center (SOC) as part of the Managed Threat Detection service, ensuring customers are promptly notified of malicious activity as soon as it emerges.

Darktrace correlated the separate malicious connections that pertained to a single campaign.
Figure 6: Darktrace correlated the separate malicious connections that pertained to a single campaign.

Darktrace Autonomous Response

In the incident outlined above, Darktrace was not configured in Autonomous Response mode. As a result, while actions to block specific connections were suggested, they had to be manually implemented by the customer’s security team. Due to the speed of the attack, this need for manual intervention allowed the threat to escalate without interruption.

However, in a different example, Autonomous Response was fully enabled, allowing Darktrace to immediately block connections to the malicious endpoint (138.199.156[.]22) just one second after the initial connection in which a numerically named file was downloaded [7].

Darktrace Autonomous Response blocked connections to a suspicious endpoint following the observation of the numeric file download.
Figure 7: Darktrace Autonomous Response blocked connections to a suspicious endpoint following the observation of the numeric file download.

This customer was also subscribed to our Managed Detection and Response service, Darktrace’s SOC extended a ‘Quarantine Device’ action that had already been autonomously applied in order to buy their security team additional time for remediation.

Autonomous Response blocked connections to malicious endpoints, including 138.199.156[.]22, 185.250.151[.]155, and rkuagqnmnypetvf[.]top, and also quarantined the affected device. These actions were later manually reinforced by the Darktrace SOC.
Figure 8: Autonomous Response blocked connections to malicious endpoints, including 138.199.156[.]22, 185.250.151[.]155, and rkuagqnmnypetvf[.]top, and also quarantined the affected device. These actions were later manually reinforced by the Darktrace SOC.

Conclusion

ClickFix baiting is a widely used tactic in which threat actors exploit human error to bypass security defenses. By tricking end point users into performing seemingly harmless, everyday actions, attackers gain initial access to systems where they can access and exfiltrate sensitive data.

Darktrace’s anomaly-based approach to threat detection identifies early indicators of targeted attacks without relying on prior knowledge or IoCs. By continuously learning each device’s unique pattern of life, Darktrace detects subtle deviations that may signal a compromise. In this case, Darktrace's Autonomous Response, when operating in a fully autonomous mode, was able to swiftly contain the threat before it could progress further along the attack lifecycle.

Credit to Keanna Grelicha (Cyber Analyst) and Jennifer Beckett (Cyber Analyst)

Appendices

NETWORK Models

  • Device / New PowerShell User Agent
  • Anomalous Connection / New User Agent to IP Without Hostname
  • Anomalous Connection / Posting HTTP to IP Without Hostname
  • Anomalous Connection / Powershell to Rare External
  • Device / Suspicious Domain
  • Device / New User Agent and New IP
  • Anomalous File / New User Agent Followed By Numeric File Download (Enhanced Monitoring Model)
  • Device / Initial Attack Chain Activity (Enhanced Monitoring Model)

Autonomous Response Models

  • Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block
  • Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block
  • Antigena / Network::External Threat::Antigena File then New Outbound Block
  • Antigena / Network::External Threat::Antigena Suspicious File Block
  • Antigena / Network::Significant Anomaly::Antigena Alerts Over Time Block
  • Antigena / Network::External Threat::Antigena Suspicious File Block

IoC - Type - Description + Confidence

·       141.193.213[.]11 – IP address – Possible C2 Infrastructure

·       141.193.213[.]10 – IP address – Possible C2 Infrastructure

·       64.94.84[.]217 – IP address – Possible C2 Infrastructure

·       138.199.156[.]22 – IP address – C2 server

·       94.181.229[.]250 – IP address – Possible C2 Infrastructure

·       216.245.184[.]181 – IP address – Possible C2 Infrastructure

·       212.237.217[.]182 – IP address – Possible C2 Infrastructure

·       168.119.96[.]41 – IP address – Possible C2 Infrastructure

·       193.36.38[.]237 – IP address – C2 server

·       188.34.195[.]44 – IP address – C2 server

·       205.196.186[.]70 – IP address – Possible C2 Infrastructure

·       rkuagqnmnypetvf[.]top – Hostname – C2 server

·       shorturl[.]at/UB6E6 – Hostname – Possible C2 Infrastructure

·       tlgrm-redirect[.]icu – Hostname – Possible C2 Infrastructure

·       diagnostics.medgenome[.]com – Hostname – Compromised Website

·       /1741714208 – URI – Possible malicious file

·       /1741718928 – URI – Possible malicious file

·       /1743871488 – URI – Possible malicious file

·       /1741200416 – URI – Possible malicious file

·       /1741356624 – URI – Possible malicious file

·       /ttt – URI – Possible malicious file

·       /1741965536 – URI – Possible malicious file

·       /1.txt – URI – Possible malicious file

·       /1744205184 – URI – Possible malicious file

·       /1744139920 – URI – Possible malicious file

·       /1744134352 – URI – Possible malicious file

·       /1744125600 – URI – Possible malicious file

·       /1[.]php?s=527 – URI – Possible malicious file

·       34ff2f72c191434ce5f20ebc1a7e823794ac69bba9df70721829d66e7196b044 – SHA-256 Hash – Possible malicious file

·       10a5eab3eef36e75bd3139fe3a3c760f54be33e3 – SHA-1 Hash – Possible malicious file

MITRE ATT&CK Mapping

Tactic – Technique – Sub-Technique  

Spearphishing Link - INITIAL ACCESS - T1566.002 - T1566

Drive-by Compromise - INITIAL ACCESS - T1189

PowerShell - EXECUTION - T1059.001 - T1059

Exploitation of Remote Services - LATERAL MOVEMENT - T1210

Web Protocols - COMMAND AND CONTROL - T1071.001 - T1071

Automated Exfiltration - EXFILTRATION - T1020 - T1020.001

References

[1] https://www.logpoint.com/en/blog/emerging-threats/clickfix-another-deceptive-social-engineering-technique/

[2] https://www.proofpoint.com/us/blog/threat-insight/security-brief-clickfix-social-engineering-technique-floods-threat-landscape

[3] https://cyberresilience.com/threatonomics/understanding-the-clickfix-attack/

[4] https://www.group-ib.com/blog/clickfix-the-social-engineering-technique-hackers-use-to-manipulate-victims/

[5] https://www.virustotal.com/gui/ip-address/193.36.38.237/detection

[6] https://www.virustotal.com/gui/ip-address/188.34.195.44/community

[7] https://www.virustotal.com/gui/ip-address/138.199.156.22/detection

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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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August 21, 2026

AI Agents: Securing the Path from Intent to Action

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The UK’s National Cyber Security Centre (NCSC) recently published guidance on managing the cyber risk of agentic AI. While the document is framed as interim advice as more formal guidance is developed, the framing reflects the current state of the industry: organizations are already deploying agents into production environments while standards, controls, and operating models for autonomous systems remain unsettled. Governance is evolving alongside adoption rather than preceding it, a reality which underscores the importance of robust controls.  

The NCSC’s guidance recommends aligning controls to an agent's level of autonomy, assigning distinct identities, limiting permissions, constraining access to systems and data, monitoring activity, maintaining human oversight, and preserving the ability to intervene when necessary. Most of these recommendations will sound familiar to security teams. The challenge is not the novelty of the controls. It is the type of system those controls now need to govern.

The shift from model security to agent security

For several years, AI security discussions have focused heavily on models. Can a model be manipulated? Jailbroken? Trusted? Can it expose information it should not? Those questions remain important, but they capture only part of the problem. A model generating text is one thing. A system connected to identities, applications, tools, workflows, and business data is another.

The difference becomes clearer when comparing a chatbot that answers questions with an agent that can retrieve customer records, update tickets, invoke tools, trigger workflows, and interact with external systems. The underlying model may be identical. Its access is not. The security question begins to shift from what the model knows to what the system can do.

The same theme appears in the Five Eyes statement released earlier this year, describing AI as a force multiplier that is accelerating both offensive and defensive cyber operations. The NCSC guidance explores what that reality looks like when autonomous systems begin operating inside enterprise environments.

Securing AI agents in operation

The NCSC spends relatively little time debating model behavior and considerably more time discussing identity, permissions, monitoring, oversight, containment, and response. Agents are treated as participants within an environment rather than isolated pieces of technology.  

That's broadly consistent with how we think about the problem at Darktrace.

An agent should not be treated as an extension of a user account. It develops its own behavioral patterns. It accesses systems, interacts with data, invokes tools, and moves across workflows in ways that can be observed independently. Understanding what an agent is permitted to do matters. Understanding how it actually behaves once deployed, and whether that behavior aligns with business intent, matters just as much.

Identity provides an obvious example. The NCSC recommends assigning distinct identities to agents rather than allowing them to disappear into surrounding human or service accounts. Most importantly, assigning agents distinct identities enables independent behavioral monitoring.

Development assumptions vs. real-world behavior

The same principle extends to monitoring. NCSC guidance places agent activity within normal security operations rather than treating it as a separate AI governance function. Many of the controls described are put in place before an agent begins operating. Sandboxing, credential design, approval workflows and human oversight all reflect judgments about how the system is expected to behave and what risks it is likely to create.

Actual use may challenge those assumptions. Access patterns change. Workflows expand. Systems begin interacting with resources they have never touched before. Processes that appeared reasonable during design behave differently in production. Human oversight requirements may turn out to be either excessive or inadequate once the system is operating at scale and operating within the context of unique business processes.

The Five Eyes statement points to a similar issue: organizations need confidence that controls continue to work as intended once systems are exposed to real users, data, tools and operational pressures. Often, the question is not whether an agent is technically allowed to perform an action, but whether its behavior remains consistent with the role it was intended to play.

Monitoring and governance of AI agents go hand-in-hand

This problem is exactly why monitoring and governance should be treated as part of the same process. Governance sets the initial parameters for deployment, while monitoring provides evidence about whether those parameters remain appropriate. That evidence should, in turn, inform changes to permissions, controls and oversight.

This matters increasingly as autonomous systems are integrated into business processes. The relevant risk is shaped not only by the model or agent itself, but by what it can access, what actions it can take, and how its behavior changes in practice.

Developing continuous oversight of AI agent behavior

The implication is clear: governance cannot end at deployment. Organizations need a way to understand how agents behave after deployment, test whether controls remain appropriate, and adjust them as conditions change. That requires visibility not just into technical activity, but into whether that activity makes sense in the context of the business process the agent is intended to support.

This is where business-centric behavioral security can become critical. Risk does not emerge from the model itself: it emerges from the actions an autonomous system takes within the enterprise and the downstream consequences of those actions.  

An agent can operate exactly as intended and still create risk if it accesses sensitive information in an unexpected context, exercises permissions in ways that create unintended exposure, or influences business processes in ways that were not anticipated during design and review.

Traditional governance vs. behavioral analytics

Traditional governance frameworks provide assurance at a point in time. Behavioral security can provide ongoing visibility into how autonomous systems interact with the organization they are meant to serve. Rather than focusing exclusively on model performance or policy compliance, organizations need to understand whether an agent's behavior aligns with business intent, operational expectations, and acceptable risk tolerances as conditions change.

As enterprises move from isolated AI deployments to interconnected ecosystems of agents, visibility into behavior becomes as important as visibility into code. Governance determines what an autonomous system is permitted to do. Behavioral analytics helps determine what it is doing, what business outcomes it is producing, and whether those outcomes remain aligned with the organization's objectives.

[related-resource]

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About the author
Margaret Cunningham, PhD
VP, Security & AI Strategy, Field CISO

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August 19, 2026

When AI Becomes the Lure: A Fake Gemini Installer Delivers Vidar

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Key takeaways

  • Darktrace observed a customer download a fake Google Gemini installer hosted on Google Colab, resulting in the execution of the Vidar information stealer.
  • Darktrace identified the compromise through behavioral indicators, including suspicious process activity, anomalous network communications, and indicators of credential theft, before autonomously containing the threat.
  • The incident highlights how threat actors are increasingly exploiting trusted platforms and a growing interest in AI tools to distribute malware through seemingly legitimate software acquisition workflows.

The Growing Abuse of Generative AI

As organizations are increasingly adopting generative AI tools into their daily workflows, attackers are adapting their distribution methods accordingly too. As part of their day-to-day work, users are now searching for AI assistants, programming tools, browser extensions, desktop applications, and productivity integrations.

Recent reports have highlighted campaigns that use fake AI software and AI-related installers to distribute malware and steal credentials [1]. Researchers have documented campaigns that exploit fake AI-themed websites and services to distribute information stealers and backdoors [2]. Security researchers have also observed attackers disguising malware as legitimate installers for AI software to increase the likelihood of victim interaction and execution [3].

In July 2026, Darktrace observed one such case within a customer environment in the Europe, Middle East and Africa (EMEA) region, where attackers used a fake generative AI installer to deliver the prolific information stealer Vidar. This incident highlights how threat actors are exploiting interest in AI services to distribute established malware using increasingly convincing social engineering techniques.

How a Fake Gemini Installer Delivered Vidar

Initial Access: From Search Result to Malware Download

Unlike many malware campaigns that begin with a phishing email, this activity appears to have originated from a user searching for and downloading software.

Darktrace first observed unusual activity on the customer network after a suspicious executable file was launched from a user’s Download folder. Further investigation revealed that the file purported to be a Google Gemini installer and was named “Download_Google_Gemini_For_Windows.exe”.

During the initial analysis, it was noted that the top search result for the suspicious filename associated pointed to a file hosted on Google Colab, a cloud-based Jupyter notebook platform, commonly used by developers, researchers, and data scientists to run code and machine learning workloads through a web browser. By leveraging another trusted Google platform, the attacker increased the likelihood that users would perceive the download as legitimate, making the lure more convincing to those searching for Gemini-related software.

Figure 1: The Google Colab page containing a download prompt for the fake Google Gemini installer.

Further investigation of the Google Colab page revealed that the download prompt redirected users to a secondary site, hxxps://micronsoftwares[.]com, which posed as a "Windows Software Hub" download page and offered the fake Gemini installer for download.

Figure 2: The secondary website posing as a "Windows Software Hub" download page, which likely hosted the fake Gemini installer.

While the investigation did not uncover any HTTP or file-download telemetry data that conclusively identified the download source, SSL communication sessions with Google Colab were detected immediately before the suspicious file was executed. The timing of these connections suggests that the user interacted with the Colab resource before being redirected to the secondary site from which the executable was downloaded.

The user was not simply tricked into opening an email attachment; instead, the attacker embedded malicious content into a process many users would consider entirely legitimate: searching for and downloading software associated with a trusted platform.

Weaponizing Trusted Platforms

At the time of review (July 15, 2026), Darktrace's Threat Research team confirmed that the Google Colab page was still active and prompting users to download a ZIP archive containing the binary file.

The archive also appeared to contain a README file instructing users to run the binary file with administrator privileges and add it to their antivirus software’s exception lists. These instructions suggest that the campaign relied heavily on social engineering, convincing users to take actions that would facilitate malware execution and potentially bypass security checks.

The use of a legitimate platform also complicates the user’s decision-making. Downloads associated with a trusted service are often perceived as less suspicious than those hosted on unfamiliar domains. When combined with the branding of a widely used AI tool, the lure becomes even more convincing.

Malware Analysis

Darktrace’s Threat Research team identified the executable file as the information-stealing malware Vidar. Analysis revealed that the binary file was a newer Go-compiled variant that communicated with Telegram-based infrastructure. Darktrace’s researchers also identified dtm[.]kijangturbo88[.]top as a command-and-control (C2) endpoint associated with the activity. While the malware itself was not novel, the lure and delivery mechanism was.

For a deeper look at the information stealer, see Darktrace’s 2023 analysis of Vidar.

Figure 3: Darktrace’s detection of the unusual outbound connection associated with the fake Gemini installer.

Shortly after execution, the process established communications with the external IP address 91.98.98[.]86 via port 443, directly linking the executable to suspicious network activity observed on the device. Subsequent open-source intelligence (OSINT) analysis of the revealed multiple malicious associations [5].

Additional Darktrace detections included unusual SSL activity from the affected device. Analysis of related SSL telemetry identified 91.98.111[.]49 as additional infrastructure associated  with the activity [6].

Subsequent alerts from the customer's Microsoft Defender for Endpoint integration later confirmed activity consistent with the theft of browser credentials and other sensitive data from the affected endpoint.

Taken together, these detections provided a clear picture of the attack, from the execution of a suspicious file and unusual network connections to indicators of C2 activity and credential theft.

Figure 4: Darktrace’s detection of anomalous activity following the execution of the fake Gemini installer, seen in the Model Alert Event Log.

Darktrace's Autonomous Response

Following the detection, Darktrace’s Autonomous Response took immediate containment action, including blocking communication with suspicious external infrastructure, including 91.98.98[.]86, and quarantining the compromised device.

Despite the apparent legitimacy of the activity, with the installer hosted on a trusted platform and resembling a routine software download, Darktrace was able to detect and contain the attack because the device's behavior deviated from its normal pattern.

Figure 5: Automated containment actions implemented by Darktrace's Autonomous Response following the detection of activity associated with the fake Gemini installer.

Conclusion

This investigation highlights how threat actors continue to adapt established malware delivery techniques to emerging technology trends. While the malware itself was not new, the distribution method was. By disguising Vidar as a Google Gemini installer and hosting the malicious content on a trusted platform, the attack aligned its lure with a growing behavioral trend: users actively searching for AI tools and services as part of their day-to-day work.

Although fake installers are not a new phenomenon, the rapid rise of generative AI has created new opportunities for threat actors. Rather than relying solely on traditional delivery methods, attackers can now target users who are actively searching for AI applications. As AI adoption continues to accelerate across enterprise environments, organizations should remain alert to campaigns that exploit this interest through fake applications, malicious websites, manipulated search results, the misuse of trusted platforms, and AI-themed social engineering.

Credit to Rushanth Ramanathan (Cyber Analyst) Joanna Ng (Detection Engineer)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

  • Security Integration / C2 Activity and Integration Detection
  • Endpoint / New Suspicious Executable Launched
  • Endpoint / Process Connection / Unusual Connection from New Process
  • Anomalous Connection / Rare External SSL Self-Signed
  • Security Integration / High Severity Integration Detection
  • Antigena / Network / Significant Anomaly /  Antigena Significant Security Integration and Network Activity Block

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

List of Indicators of Compromise (IoCs)

IoC Type Description
Download_Google_Gemini_For_Windows.exe File Fake Gemini-themed installer observed during the investigation.
GoogleAppInstaller.exe File Related executable identified through endpoint telemetry.
91.98.98[.]86 IP Address External destination contacted by the malicious executable.
91.98.111[.]49 IP Address Related infrastructure identified through SSL certificate pivoting.
dtm[.]kijangturbo88[.]top Domain Command-and-control endpoint identified during malware analysis.
1e13c2c9eac72daf63fd00a9946878949e159ae6ec51b54ec64f942d79d61913 SHA256 Malware sample associated with the fake Gemini installer.

MITRE ATT@CK Mapping


MITRE ATT&CK Mapping Tactic Technique
Initial Access T1204 User Execution
Execution T1204.002 User Execution: Malicious File
Defence Evasion T1036 Masquerading
Credential Access T1555 Credentials from Password Stores
Credential Access T1555.003 Credentials from Web Browsers
Command and Control T1071 Application Layer Protocol
Exfiltration T1041 Exfiltration Over C2 Channel
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About the author
Rushanth Ramanathan
Cyber Analyst
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