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June 13, 2021

Neutralizing QakBot: Darktrace SOC's Success Story

Learn about the strategies used by Darktrace's SOC team to neutralize the QakBot banking trojan and safeguard financial data.
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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager
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13
Jun 2021

While cutting-edge technology is essential for organizations to secure their digital assets, having on-hand human support to deal with threats can be invaluable for lean security teams and organizations without Autonomous Response in their digital enterprise.

Cyber AI technology recently detected the QakBot banking trojan in a customer environment, and with the help of Darktrace’s SOC team, the customer was able to shut down the attack in under two hours.

QakBot malware

QakBot has built a name for itself over the past twelve years as one of the most deadly trojans in the game. Used in fast-paced, automated attacks against individual businesses, it has the ability to drain company resources and steal vast amounts of financial data. It is often downloaded during Emotet campaigns to infect devices and harvest bank account information.

Like other banking trojans, QakBot uses a dropper to install itself on a corporate device. It then self-propagates through a system and collects credentials at machine speed. Cyber-criminals can use this information to extract private data or distribute ransomware and further malicious payloads.

QakBot is extremely difficult for traditional security tools to detect. Due to a combination of its automatic worm-like capabilities, its use of a virus dropper with delayed execution, and several other obfuscation methods, it is able to bypass the majority of legacy tools and can lead to extreme financial repercussions if not dealt with in its initial stages.

The Darktrace SOC team

Darktrace’s Security Operations Center (SOC) team, located in Cambridge, San Francisco, and Singapore, deal with a wide range of these quick-moving and stealthy threats which are identified by Cyber AI, including ransomware deployments, SaaS account takeovers, and data exfiltration.

Such attacks often use ‘Living off the Land’ techniques which make them difficult to differentiate from legitimate network traffic. Moreover, many threat actors carry out malicious activities outside of a target organization’s normal working hours, amplifying the potential impact of a breach before it is discovered.

The Darktrace SOC team provides around-the-clock coverage of customer environments through Proactive Threat Notification (PTN) and Ask the Expert (ATE) services. Alongside autonomous AI detection, these services provide additional human monitoring and support for customers undergoing significant security events.

Uncovering the QakBot banking trojan

Figure 1: Timeline of the QakBot banking trojan attack, including the response from Darktrace’s services.

At a company in the EMEA region with around 7,000 devices, Cyber AI detected the early signs of a trojan horse. The organization did not have Antigena Email analyzing its email traffic in order to respond to attacks in the inbox, so when a phishing email slipped through the gateway and was opened by a user, their device began connecting to a high volume of suspicious endpoints.

This resembled command and control (C2) communication, and, based on the unusual nature of this activity for the device and the environment, this behavior triggered multiple high scoring model breaches. One of these was a high fidelity model breach for ‘Suspicious SSL Activity’, which prompted an investigation through the Proactive Threat Notification service.

Figure 2: An example of the Cyber AI Analyst incident timeline for an infected device, showing command and control and reconnaissance activity.

An expert Darktrace analyst was alerted to the unusual connectivity by the Enterprise Immune System and began to investigate the anomalous behavior, determining that this device was exhibiting strong signs of a banking trojan infection. The analyst needed to move quickly: the trojan had immediately begun reconnaissance and was preparing to spread across the network.

Within an hour, the analyst had produced a brief report summarizing the activity and this was sent as a PTN alert to the customer. The report contained key technical information from the model breach and Cyber AI Analyst incident – including the timeframe, device hostname and IP address, suspicious external domains, and a reference for the customer to view this alert in the Darktrace UI.

Figure 3: Visual example of the Darktrace threat tray. In the QakBot attack, four Enhanced Monitoring model breaches were triggered, and these were investigated and alerted through the PTN service. They were all high scoring detections, clearly indicating a compromise.

Upon receiving the alert, the customer initiated further investigation and quickly shut down the affected device. The attack was contained in less than two hours.

Ask the Expert

After their initial remediation, the company reached out to the Darktrace team via Ask the Expert to confirm that this was a QakBot infection and to gain additional assistance in investigating the extent of the compromise.

The analyst team provided ongoing support to the investigation over the next six hours, concluding that this likely came from a phishing email and that no other devices in the environment were compromised. The analyst provided a list of observed Indicators of Compromise (IoCs) and worked with the customer to add these to the Darktrace Watched Domains List for further monitoring. The customer was also able to use this list to block the IoCs at the firewall.

The organization contained the infection, and no further suspicious behavior was observed from network devices.

Humans and AI

This case study is a perfect example of how Darktrace’s services provide constant assistance to customers every day of every week. On top of Darktrace’s advanced machine learning technology, the Darktrace SOC team serves as an additional layer of support for security teams of all sizes. Proactive Threat Notifications offer an extra set of eyes on emerging threats, while Ask The Expert provides a mechanism for customers to gain investigative support directly from Darktrace analysts.

The early detection of this banking trojan allowed the organization to deal with the threat before it could develop into a serious infection or a ransomware attack. QakBot is just one of many strains of swift self-spreading malware in today’s threat landscape. Such automated attacks consistently outpace the fastest of human defenders, exposing the desperate need for AI and autonomous systems to augment human teams and protect digital systems in real time.

If Antigena Network had been active in this environment, the suspicious external connectivity would have been blocked upon first detection, stopping the attack within seconds. In fact, the customer decided to deploy Antigena Network following this incident, and now benefits from 24/7 Autonomous Response against all emerging cyber-threats.

IoCs:

nerotimethod[.]com193[.]29[.]58[.]17345[.]32[.]211[.]20754[.]36[.]108[.]120144[.]139[.]166[.]1875[.]67[.]192[.]125 149[.]28[.]101[.]9037[.]211[.]90[.]17568[.]131[.]107[.]37162[.]222[.]226[.]194mywebscrap[.]com

Darktrace model detections:

  • Compromise / SSL or HTTP Beacon
  • Compromise / Suspicious SSL Activity
  • Device / Multiple C2 Model Breaches
  • Device / Lateral Movement and C2 Activity
  • Device / Multiple Lateral Movement Model Breaches
  • Device / Large Number of Model Breaches
  • Compromise / Suspicious Beaconing Behaviour
  • Compromise / SSL Beaconing to Rare Destination
  • Compromise / Slow Beaconing Activity To External Rare
  • Compromise / High Volume of Connections with Beacon Score
  • Anomalous Connection / Suspicious Self-Signed SSL
  • Anomalous Connection / Rare External SSL Self-Signed
  • Device / Reverse DNS Sweep
  • Unusual Activity / Possible RPC Recon Activity
  • Device / Active Directory Reconnaissance
  • Device / Network Scan - Low Anomaly Score
  • Anomalous Connection / SMB Enumeration

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
Brianna Luong (Leddy)
Sr. Technical Alliances Manager

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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.

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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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