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May 28, 2024

Stemming the Citrix Bleed Vulnerability with Darktrace’s ActiveAI Security Platform

This blog delves into Darktrace’s investigation into the exploitation of the Citrix Bleed vulnerability on the network of a customer in late 2023. Darktrace’s Self-Learning AI ensured the customer was well equipped to track the post-compromise activity and identify affected devices.
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
Vivek Rajan
Cyber Analyst
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28
May 2024

What is Citrix Bleed?

Since August 2023, cyber threat actors have been actively exploiting one of the most significant critical vulnerabilities disclosed in recent years: Citrix Bleed. Citrix Bleed, also known as CVE-2023-4966, remained undiscovered and even unpatched for several months, resulting in a wide range of security incidents across business and government sectors [1].

How does Citrix Bleed vulnerability work?

The vulnerability, which impacts the Citrix Netscaler Gateway and Netscaler ADC products, allows for outside parties to hijack legitimate user sessions, thereby bypassing password and multifactor authentication (MFA) requirements.

When used as a means of initial network access, the vulnerability has resulted in the exfiltration of sensitive data, as in the case of Xfinity, and even the deployment of ransomware variants including Lockbit [2]. Although Citrix has released a patch to address the vulnerability, slow patching procedures and the widespread use of these products has resulted in the continuing exploitation of Citrix Bleed into 2024 [3].

How Does Darktrace Handle Citrix Bleed?

Darktrace has demonstrated its proficiency in handling the exploitation of Citrix Bleed since it was disclosed back in 2023; its anomaly-based approach allows it to efficiently identify and inhibit post-exploitation activity as soon as it surfaces.  Rather than relying upon traditional rules and signatures, Darktrace’s Self-Learning AI enables it to understand the subtle deviations in a device’s behavior that would indicate an emerging compromise, thus allowing it to detect anomalous activity related to the exploitation of Citrix Bleed.

In late 2023, Darktrace identified an instance of Citrix Bleed exploitation on a customer network. As this customer had subscribed to the Proactive Threat Notification (PTN) service, the suspicious network activity surrounding the compromise was escalated to Darktrace’s Security Operation Center (SOC) for triage and investigation by Darktrace Analysts, who then alerted the customer’s security team to the incident.

Darktrace’s Coverage

Initial Access and Beaconing of Citrix Bleed

Darktrace’s initial detection of indicators of compromise (IoCs) associated with the exploitation of Citrix Bleed actually came a few days prior to the SOC alert, with unusual external connectivity observed from a critical server. The suspicious connection in question, a SSH connection to the rare external IP 168.100.9[.]137, lasted several hours and utilized the Windows PuTTY client. Darktrace also identified an additional suspicious IP, namely 45.134.26[.]2, attempting to contact the server. Both rare endpoints had been linked with the exploitation of the Citrix Bleed vulnerability by multiple open-source intelligence (OSINT) vendors [4] [5].

Darktrace model alert highlighting an affected device making an unusual SSH connection to 168.100.9[.]137 via port 22.
Figure 1: Darktrace model alert highlighting an affected device making an unusual SSH connection to 168.100.9[.]137 via port 22.

As Darktrace is designed to identify network-level anomalies, rather than monitor edge infrastructure, the initial exploitation via the typical HTTP buffer overflow associated with this vulnerability fell outside the scope of Darktrace’s visibility. However, the aforementioned suspicious connectivity likely constituted initial access and beaconing activity following the successful exploitation of Citrix Bleed.

Command and Control (C2) and Payload Download

Around the same time, Darktrace also detected other devices on the customer’s network conducting external connectivity to various endpoints associated with remote management and IT services, including Action1, ScreenConnect and Fixme IT. Additionally, Darktrace observed devices downloading suspicious executable files, including “tniwinagent.exe”, which is associated with the tool Total Network Inventory. While this tool is typically used for auditing and inventory management purposes, it could also be leveraged by attackers for the purpose of lateral movement.

Defense Evasion

In the days surrounding this compromise, Darktrace observed multiple devices engaging in potential defense evasion tactics using the ScreenConnect and Fixme IT services. Although ScreenConnect is a legitimate remote management tool, it has also been used by threat actors to carry out C2 communication [6]. ScreenConnect itself was the subject of a separate critical vulnerability which Darktrace investigated in early 2024. Meanwhile, CISA observed that domains associated with Fixme It (“fixme[.]it”) have been used by threat actors attempting to exploit the Citrix Bleed vulnerability [7].

Reconnaissance and Lateral Movement

A few days after the detection of the initial beaconing communication, Darktrace identified several devices on the customer’s network carrying out reconnaissance and lateral movement activity. This included SMB writes of “PSEXESVC.exe”, network scanning, DCE-RPC binds of numerous internal devices to IPC$ shares and the transfer of compromise-related tools. It was at this point that Darktrace’s Self-Learning AI deemed the activity to be likely indicative of an ongoing compromise and several Enhanced Monitoring models alerted, triggering the aforementioned PTNs and investigation by Darktrace’s SOC.

Darktrace observed a server on the network initiating a wide range of connections to more than 600 internal IPs across several critical ports, suggesting port scanning, as well as conducting unexpected DCE-RPC service control (svcctl) activity on multiple internal devices, amongst them domain controllers. Additionally, several binds to server service (srvsvc) and security account manager (samr) endpoints via IPC$ shares on destination devices were detected, indicating further reconnaissance activity. The querying of these endpoints was also observed through RPC commands to enumerate services running on the device, as well as Security Account Manager (SAM) accounts.  

Darktrace also identified devices performing SMB writes of the WinRAR data compression tool, in what likely represented preparation for the compression of data prior to data exfiltration. Further SMB file writes were observed around this time including PSEXESVC.exe, which was ultimately used by attackers to conduct remote code execution, and one device was observed making widespread failed NTLM authentication attempts on the network, indicating NTLM brute-forcing. Darktrace observed several devices using administrative credentials to carry out the above activity.

In addition to the transfer of tools and executables via SMB, Darktrace also identified numerous devices deleting files through SMB around this time. In one example, an MSI file associated with the patch management and remediation service, Action1, was deleted by an attacker. This legitimate security tool, if leveraged by attackers, could be used to uncover additional vulnerabilities on target networks.

A server on the customer’s network was also observed writing the file “m.exe” to multiple internal devices. OSINT investigation into the executable indicated that it could be a malicious tool used to prevent antivirus programs from launching or running on a network [8].

Impact and Data Exfiltration

Following the initial steps of the breach chain, Darktrace observed numerous devices on the customer’s network engaging in data exfiltration and impact events, resulting in additional PTN alerts and a SOC investigation into data egress. Specifically, two servers on the network proceeded to read and download large volumes of data via SMB from multiple internal devices over the course of a few hours. These hosts sent large outbound volumes of data to MEGA file storage sites using TLS/SSL over port 443. Darktrace also identified the use of additional file storage services during this exfiltration event, including 4sync, file[.]io, and easyupload[.]io. In total the threat actor exfiltrated over 8.5 GB of data from the customer’s network.

Darktrace Cyber AI Analyst investigation highlighting the details of a data exfiltration attempt.
Figure 2: Darktrace Cyber AI Analyst investigation highlighting the details of a data exfiltration attempt.

Finally, Darktrace detected a user account within the customer’s Software-as-a-Service (SaaS) environment conducting several suspicious Office365 and AzureAD actions from a rare IP for the network, including uncommon file reads, creations and the deletion of a large number of files.

Unfortunately for the customer in this case, Darktrace RESPOND™ was not enabled on the network and the post-exploitation activity was able to progress until the customer was made aware of the attack by Darktrace’s SOC team. Had RESPOND been active and configured in autonomous response mode at the time of the attack, it would have been able to promptly contain the post-exploitation activity by blocking external connections, shutting down any C2 activity and preventing the download of suspicious files, blocking incoming traffic, and enforcing a learned ‘pattern of life’ on offending devices.

Conclusion

Given the widespread use of Netscaler Gateway and Netscaler ADC, Citrix Bleed remains an impactful and potentially disruptive vulnerability that will likely continue to affect organizations who fail to address affected assets. In this instance, Darktrace demonstrated its ability to track and inhibit malicious activity stemming from Citrix Bleed exploitation, enabling the customer to identify affected devices and enact their own remediation.

Darktrace’s anomaly-based approach to threat detection allows it to identify such post-exploitation activity resulting from the exploitation of a vulnerability, regardless of whether it is a known CVE or a zero-day threat. Unlike traditional security tools that rely on existing threat intelligence and rules and signatures, Darktrace’s ability to identify the subtle deviations in a compromised device’s behavior gives it a unique advantage when it comes to identifying emerging threats.

Credit to Vivek Rajan, Cyber Analyst, Adam Potter, Cyber Analyst

Appendices

Darktrace Model Coverage

Device / Suspicious SMB Scanning Activity

Device / ICMP Address Scan

Device / Possible SMB/NTLM Reconnaissance

Device / Network Scan

Device / SMB Lateral Movement

Device / Possible SMB/NTLM Brute Force

Device / Suspicious Network Scan Activity

User / New Admin Credentials on Server

Anomalous File / Internal::Unusual Internal EXE File Transfer

Compliance / SMB Drive Write

Device / New or Unusual Remote Command Execution

Anomalous Connection / New or Uncommon Service Control

Anomalous Connection / Rare WinRM Incoming

Anomalous Connection / Unusual Admin SMB Session

Device / Unauthorised Device

User / New Admin Credentials on Server

Anomalous Server Activity / Outgoing from Server

Device / Long Agent Connection to New Endpoint

Anomalous Connection / Multiple Connections to New External TCP Port

Device / New or Uncommon SMB Named Pipe

Device / Multiple Lateral Movement Model Breaches

Device / Large Number of Model Breaches

Compliance / Remote Management Tool On Server

Device / Anomalous RDP Followed By Multiple Model Breaches

Device / SMB Session Brute Force (Admin)

Device / New User Agent

Compromise / Large Number of Suspicious Failed Connections

Unusual Activity / Unusual External Data Transfer

Unusual Activity / Enhanced Unusual External Data Transfer

Device / Increased External Connectivity

Unusual Activity / Unusual External Data to New Endpoints

Anomalous Connection / Data Sent to Rare Domain

Anomalous Connection / Uncommon 1 GiB Outbound

Anomalous Connection / Active Remote Desktop Tunnel

Anomalous Server Activity / Anomalous External Activity from Critical Network Device

Compliance / Possible Unencrypted Password File On Server

Anomalous Connection / Suspicious Read Write Ratio and Rare External

Device / Reverse DNS Sweep]

Unusual Activity / Possible RPC Recon Activity

Anomalous File / Internal::Executable Uploaded to DC

Compliance / SMB Version 1 Usage

Darktrace AI Analyst Incidents

Scanning of Multiple Devices

Suspicious Remote Service Control Activity

SMB Writes of Suspicious Files to Multiple Devices

Possible SSL Command and Control to Multiple Devices

Extensive Suspicious DCE-RPC Activity

Suspicious DCE-RPC Activity

Internal Downloads and External Uploads

Unusual External Data Transfer

Unusual External Data Transfer to Multiple Related Endpoints

MITRE ATT&CK Mapping

Technique – Tactic – ID – Sub technique of

Network Scanning – Reconnaissance - T1595 - T1595.002

Valid Accounts – Defense Evasion, Persistence, Privilege Escalation, Initial Access – T1078 – N/A

Remote Access Software – Command and Control – T1219 – N/A

Lateral Tool Transfer – Lateral Movement – T1570 – N/A

Data Transfers – Exfiltration – T1567 – T1567.002

Compressed Data – Exfiltration – T1030 – N/A

NTLM Brute Force – Brute Force – T1110 - T1110.001

AntiVirus Deflection – T1553 - NA

Ingress Tool Transfer   - COMMAND AND CONTROL - T1105 - NA

Indicators of Compromise (IoCs)

204.155.149[.]37 – IP – Possible Malicious Endpoint

199.80.53[.]177 – IP – Possible Malicious Endpoint

168.100.9[.]137 – IP – Malicious Endpoint

45.134.26[.]2 – IP – Malicious Endpoint

13.35.147[.]18 – IP – Likely Malicious Endpoint

13.248.193[.]251 – IP – Possible Malicious Endpoint

76.223.1[.]166 – IP – Possible Malicious Endpoint

179.60.147[.]10 – IP – Likely Malicious Endpoint

185.220.101[.]25 – IP – Likely Malicious Endpoint

141.255.167[.]250 – IP – Malicious Endpoint

106.71.177[.]68 – IP – Possible Malicious Endpoint

cat2.hbwrapper[.]com – Hostname – Likely Malicious Endpoint

aj1090[.]online – Hostname – Likely Malicious Endpoint

dc535[.]4sync[.]com – Hostname – Likely Malicious Endpoint

204.155.149[.]140 – IP - Likely Malicious Endpoint

204.155.149[.]132 – IP - Likely Malicious Endpoint

204.155.145[.]52 – IP - Likely Malicious Endpoint

204.155.145[.]49 – IP - Likely Malicious Endpoint

References

  1. https://www.axios.com/2024/01/02/citrix-bleed-security-hacks-impact
  2. https://www.csoonline.com/article/1267774/hackers-steal-data-from-millions-of-xfinity-customers-via-citrix-bleed-vulnerability.html
  3. https://www.cybersecuritydive.com/news/citrixbleed-security-critical-vulnerability/702505/
  4. https://www.virustotal.com/gui/ip-address/168.100.9.137
  5. https://www.virustotal.com/gui/ip-address/45.134.26.2
  6. https://www.trendmicro.com/en_us/research/24/b/threat-actor-groups-including-black-basta-are-exploiting-recent-.html
  7. https://www.cisa.gov/news-events/cybersecurity-advisories/aa23-325a
  8. https://www.file.net/process/m.exe.html
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
Vivek Rajan
Cyber Analyst

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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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