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August 5, 2020

Guarding Against Threats Beyond IT

We explore insights from a vast customer database, exposing the widespread adoption of ICS protocols within IT settings.
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
David Masson
VP, Field CISO
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05
Aug 2020

Key takeaways

  • Multiple well-known ICS attacks have been successful by gaining an initial foothold into the IT network, such as EKANS, Black Energy, and Havex
  • Stage One of the ICS Cyber Kill Chain is network reconnaissance, and so IT/OT network segregation is critical
  • Darktrace finds that many organizations’ networks have at least some level of IT/OT convergence
  • Visibility across ICS infrastructure, actions, and commands provides a better picture into potentially malicious internal activity

IT & OT Convergence Threats

Shipping, manufacturing, and other forms of heavy industry are seeing an ever-increasing convergence of IT and OT systems with the growth in Industrial Internet of Things (IIoT). At the same time, it remains critical to segment IT from OT networks, as the lack of segmentation could provide a malicious actor – either a hacker or rogue insider – easy access to pivot into the OT network.

High-profile attack campaigns such as Havex or Black Energy show traditional network security monitoring tools can be insufficient in preventing these intrusions. After the initial compromise, these ICS attacks progressed from IT to OT systems, showing that the convergence of IT and OT in cyber-physical ecosystems calls for technology that can understand how these two systems interact.

More recently, analysis of the EKANS ransomware revealed that attackers are attempting to use malware to actively disrupt OT as well as IT networks. The attack contained ICS processes on its ‘kill list,’ which allowed it to halt global manufacturing for large organizations like Honda.

More often than not, a lack of visibility is a major challenge in protecting critical ICS assets. Security specialists benefit when they have visibility over unusual or unexpected connections, or more crucially, when ICS commands are being sent by malicious actors attempting to perform industrial sabotage.

Investigation details

Darktrace analysts investigated the use of industrial protocols in the enterprise environments of various customers. The industries ranged from banking to government, retail to food manufacturing and beyond, and included companies with Industrial Control Systems that leverage Darktrace to defend their corporate networks.

In some cases, the security teams may not have been aware of IT/OT convergence within their enterprise environments. In other cases, the IT team may be aware of the ICS segments, but do not see them as a security priority because it does not fall directly within their remit.

The results revealed that hundreds of companies are using OT protocols in their enterprise environments, which suggests that IT/OT systems are not properly segmented. Specifically, Darktrace detected over 6,500 suspected instances of ICS protocol use across 1,000 environments. Note that this data was collected anonymously, only keeping track of the industry for analysis purposes.

Figure 1: A chart showing the percentage of ICS protocol use in enterprise environments

The ICS protocol which was detected the most was BacNet, seen in approximately 75% of instances. BacNet is used in Building Management Systems, so it is not surprising that it is widely used across multiple industries and within corporate networks. It is likely the security teams are aware that their BMS is part of the enterprise network, but may not appreciate how its use of the BacNet OT protocol increases the attack surface for the business and can be a blind spot for security teams.

Core ICS protocols

Darktrace also detected ‘core’ ICS protocols, Modbus and CIP (Common Industrial Protocol). These are normally associated with traditional ICS industries such as manufacturing, oil and gas, robotics, and utilities, and provides further evidence of IT/OT convergence.

This increased IT/OT convergence creates new blind spots on the network and sets up new pathways to disruption. This offers opportunities for attackers, and the public are now increasingly aware of attacks that have pivoted from IT into OT.

Improper segmentation between IT and OT systems can lead to highly unusual connections to ICS protocols. This can be seen in our recent analysis of industrial sabotage, with the timeline of the attack’s main events presented below.

Figure 2: A timeline showing the events of an incident of industrial sabotage

This is just one example of an attack that began in IT systems before affecting OT. More high-profile attacks that follow this pattern are presented below:

EKANS ransomware

The recent EKANS attack involved a strain of ransomware with close links to the MEGACORTEX variant, which gained infamy following an attack on Honda’s global operations in June 2020. Like many ransomware variants, EKANS encrypts files in IT systems and demands ransom in order to unlock the infected machines. However, the malware also has the ability to kill ICS processes on infected hosts. Notably, it is the first public example of ransomware that can target ICS operations.

Havex

Havex utilized multiple attack vectors, including spear phishing, trojans, and infected vendor websites, often known as a ‘watering hole attack’. It targeted IT systems, Internet-connected workstations, or a combination of the two. With Havex, attackers leveraged lateral movement techniques to pivot into Level 3 of ICS networks. The attack’s motive was data exfiltration to a C2 server, likely as part of a government-backed espionage campaign.

Black Energy 3

Black Energy 3 favored macro-embedded MS Office documents delivered via spear phishing emails as attack vectors. Older variants of Black Energy targeted vulnerabilities in ICS HMIs (Human Machine Interfaces) which were connected to the Internet. The attack’s motive was industrial sabotage and is what was used against the Ukrainian electric grid in 2015, leading to power outages for over 225,000 civilians and requiring a switch to manual operations as substations were taken offline.

Lessons learned

Each of the attack campaigns detailed above was in some way enabled by IT/OT convergence. Attackers still favor targeting IT networks with their initial attack vectors, as IT networks have significantly more interaction with the Internet through emails, and various other interconnected technologies. Poor network segmentation allows attackers easy access to OT systems once an IT network has been compromised.

In all of these ICS cyber-attacks, devices deviated from their normal patterns of life at one or more points in the cyber kill chain. Indicators of compromise can include anything from new external connections, to network reconnaissance using active scanning, to lateral movement using privileged credentials, ICS reprogram commands, or ICS discovery requests. With proper enterprise-wide visibility, across both IT and OT systems, and security tools that are able to detect these deviations, a security team would be alerted to these compromises before an attacker could carry out their objectives.

Ultimately, visibility is crucial for cyber defenders to protect industrial property and processes. Darktrace/OT enables many Industrial Model Detections, a selection of which are listed below:

  • Anomalous IT to ICS Connection
  • Multiple Failed Connections to OT Device
  • Multiple New Action Commands
  • Uncommon ICS Reprogram
  • Suspicious Network Scanning Activity
  • Unusual Broadcast from ICS PLC
  • Unusual Admin RDP Session

It is clear that attackers continue to exploit increasing IT/OT convergence to carry out industrial sabotage. Still, as revealed by our analysis of our customer base, many organizations continue to unknowingly use ICS protocols in their corporate environments, both increasing their attack surface and creating dangerous blind spots. A new, holistic approach to cyber defense is needed – one that can reveal this convergence of IT and OT, provide visibility, and detect deviations indicative of emerging cyber-attacks against critical systems.

Thanks to Darktrace analyst Oakley Cox for his insights on the above investigation.

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
David Masson
VP, Field CISO

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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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