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April 30, 2024

Detecting Attacks Across Email, SaaS, and Network Environments with Darktrace’s ActiveAI Security Platform

This blog explores how Darktrace’s combined AI approach enabled it to identify and connect an attack that took place over three critical areas of a customer’s digital environment, namely email, SaaS and network.
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
Zoe Tilsiter
Cyber Analyst
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30
Apr 2024

The State of AI in Cybersecurity

In a recent survey outlined in Darktrace’s State of AI Cyber Security whitepaper, 95% of cyber security professionals agree that AI-powered security solutions will improve their organization’s detection of cyber-threats [1]. Crucially, a combination of multiple AI methods is the most effective to improve cybersecurity; improving threat detection, accelerating threat investigation and response, and providing visibility across an organization’s digital environment.

In March 2024, Darktrace’s AI-led security platform was able to detect suspicious activity affecting a customer’s email, Software-as-a-Service (SaaS), and network environments, whilst its applied supervised learning capability, Cyber AI Analyst, autonomously correlated and connected all of these events together in one single incident, explained concisely using natural language processing.

Attack Overview

Following an initial email attack vector, an attacker logged into a compromised SaaS user account from the Netherlands, changed inbox rules, and leveraged the account to send thousands of phishing emails to internal and external users. Internal users fell victim to the emails by clicking on contained suspicious links that redirected them to newly registered suspicious domains hosted on same IP address as the hijacked SaaS account login. This activity triggered multiple alerts in Darktrace DETECT™ on both the network and SaaS side, all of which were correlated into one Cyber AI Analyst incident.

In this instance, Darktrace RESPOND™ was not active on any of the customer’s environments, meaning the compromise was able to escalate until their security team acted on the alerts raised by DETECT. Had RESPOND been enabled at the time of the attack, it would have been able to apply swift actions to contain the attack by blocking connections to suspicious endpoints on the network side and disabling users deviating from their normal behavior on the customer’s SaaS environment.

Nevertheless, thanks to DETECT and Cyber AI Analyst, Darktrace was able to provide comprehensive visibility across the customer’s three digital estate environments, decreasing both investigation and response time which enabled them to quickly enact remediation during the attack. This highlights the crucial role that Darktrace’s combined AI approach can play in anomaly detection cyber defense

Attack Details & Darktrace Coverage

Attack timeline

1. Email: the initial attack vector  

The initial attack vector was likely email, as on March 18, 2024, Darktrace observed a user device making several connections to the email provider “zixmail[.]net”, shortly before it connected to the first suspicious domain. Darktrace/Email identified multiple unusual inbound emails from an unknown sender that contained a suspicious link. Darktrace recognized these emails as potentially malicious and locked the link, ensuring that recipients could not directly click it.

Figure 1: Suspected initial compromise email from an unknown sender, containing a suspicious link, which was locked by Darktrace/Email.

2. Escalation to Network

Later that day, despite Darktrace/Email having locked the link in the suspicious email, the user proceeded to click on it and was directed to a suspicious external location, namely “rz8js7sjbef[.]latovafineart[.]life”, which triggered the Darktrace/Network DETECT model “Suspicious Domain”. Darktrace/Email was able to identify that this domain had only been registered 4 days before this activity and was hosted on an IP address based in the Netherlands, 193.222.96[.]9.

3. SaaS Account Hijack

Just one minute later, Darktrace/Apps observed the user’s Microsoft 365 account logging into the network from the same IP address. Darktrace understood that this represented unusual SaaS activity for this user, who had only previously logged into the customer’s SaaS environment from the US, triggering the “Unusual External Source for SaaS Credential Use” model.

4. SaaS Account Updates

A day later, Darktrace identified an unusual administrative change on the user’s Microsoft 365 account. After logging into the account, the threat actor was observed setting up a new multi-factor authentication (MFA) method on Microsoft Authenticator, namely requiring a 6-digit code to authenticate. Darktrace understood that this authentication method was different to the methods previously used on this account; this, coupled with the unusual login location, triggered the “Unusual Login and Account Update” DETECT model.

5. Obfuscation Email Rule

On March 20, Darktrace detected the threat actor creating a new email rule, named “…”, on the affected account. Attackers are typically known to use ambiguous or obscure names when creating new email rules in order to evade the detection of security teams and endpoints users.

The parameters for the email rule were:

“AlwaysDeleteOutlookRulesBlob: False, Force: False, MoveToFolder: RSS Feeds, Name: ..., MarkAsRead: True, StopProcessingRules: True.”

This rule was seemingly created with the intention of obfuscating the sending of malicious emails, as the rule would move sent emails to the "RSS Feeds” folder, a commonly used tactic by attackers as the folder is often left unchecked by endpoint users. Interestingly, Darktrace identified that, despite the initial unusual login coming from the Netherlands, the email rule was created from a different destination IP, indicating that the attacker was using a Virtual Private Network (VPN) after gaining a foothold in the network.

Hijacked SaaS account making an anomalous login from the unusual Netherlands-based IP, before creating a new email rule.
Figure 2: Hijacked SaaS account making an anomalous login from the unusual Netherlands-based IP, before creating a new email rule.

6. Outbound Phishing Emails Sent

Later that day, the attacker was observed using the compromised customer account to send out numerous phishing emails to both internal and external recipients. Darktrace/Email detected a significant spike in inbound emails on the compromised account, with the account receiving bounce back emails or replies in response to the phishing emails. Darktrace further identified that the phishing emails contained a malicious DocSend link hidden behind the text “Click Here”, falsely claiming to be a link to the presentation platform Prezi.

Figure 3: Darktrace/Email detected that the DocSend link displayed via text “Click Here”, was embedded in a Prezi link.
Figure 3: Darktrace/Email detected that the DocSend link displayed via text “Click Here”, was embedded in a Prezi link.

7. Suspicious Domains and Redirects

After the phishing emails were sent, multiple other internal users accessed the DocSend link, which directed them to another suspicious domain, “thecalebgroup[.]top”, which had been registered on the same day and was hosted on the aforementioned Netherlands-based IP, 193.222.96[.]91. At the time of the attack, this domain had not been reported by any open-source intelligence (OSINT), but it has since been flagged as malicious by multiple vendors [2].

External Sites Summary showing the suspicious domain that had never previously been seen on the network. A total of 11 “Suspicious Domain” models were triggered in response to this activity.
Figure 4: External Sites Summary showing the suspicious domain that had never previously been seen on the network. A total of 11 “Suspicious Domain” models were triggered in response to this activity.  

8. Cyber AI Analyst’s Investigation

As this attack was unfolding, Darktrace’s Cyber AI Analyst was able to autonomously investigate the events, correlating them into one wider incident and continually adding a total of 14 new events to the incident as more users fell victim to the phishing links.

Cyber AI Analyst successfully weaved together the initial suspicious domain accessed in the initial email attack vector (Figure 5), the hijack of the SaaS account from the Netherlands IP (Figure 6), and the connection to the suspicious redirect link (Figure 7). Cyber AI Analyst was also able to uncover other related activity that took place at the time, including a potential attempt to exfiltrate data out of the customer’s network.

By autonomously analyzing the thousands of connections taking place on a network at any given time, Darktrace’s Cyber AI Analyst is able to detect seemingly separate anomalous events and link them together in one incident. This not only provides organizations with full visibility over potential compromises on their networks, but also saves their security teams precious time ensuring they can quickly scope out the ongoing incident and begin remediation.

Figure 5: Cyber AI Analyst correlated the attack’s sequence, starting with the initial suspicious domain accessed in the initial email attack vector.
Figure 5: Cyber AI Analyst correlated the attack’s sequence, starting with the initial suspicious domain accessed in the initial email attack vector.
Figure 6: As the attack progressed, Cyber AI Analyst correlated and appended additional events to the same incident, including the SaaS account hijack from the Netherlands-based IP.
Figure 6: As the attack progressed, Cyber AI Analyst correlated and appended additional events to the same incident, including the SaaS account hijack from the Netherlands-based IP.
Cyber AI Analyst correlated and appended additional events to the same incident, including additional users connecting to the suspicious redirect link following the outbound phishing emails being sent.
Figure 7: Cyber AI Analyst correlated and appended additional events to the same incident, including additional users connecting to the suspicious redirect link following the outbound phishing emails being sent.

Conclusion

In this scenario, Darktrace demonstrated its ability to detect and correlate suspicious activities across three critical areas of a customer’s digital environment: email, SaaS, and network.

It is essential that cyber defenders not only adopt AI but use a combination of AI technology capable of learning and understanding the context of an organization’s entire digital infrastructure. Darktrace’s anomaly-based approach to threat detection allows it to identify subtle deviations from the expected behavior in network devices and SaaS users, indicating potential compromise. Meanwhile, Cyber AI Analyst dynamically correlates related events during an ongoing attack, providing organizations and their security teams with the information needed to respond and remediate effectively.

Credit to Zoe Tilsiter, Analyst Consulting Lead (EMEA), Brianna Leddy, Director of Analysis

Appendices

References

[1] https://darktrace.com/state-of-ai-cyber-security

[2] https://www.virustotal.com/gui/domain/thecalebgroup.top

Darktrace DETECT Model Coverage

SaaS Models

- SaaS / Access / Unusual External Source for SaaS Credential Use

- SaaS / Compromise / Unusual Login and Account Update

- SaaS / Compliance / Anomalous New Email Rule

- SaaS / Compromise / Unusual Login and New Email Rule

Network Models

- Device / Suspicious Domain

- Multiple Device Correlations / Multiple Devices Breaching Same Model

Cyber AI Analyst Incidents

- Possible Hijack of Office365 Account

- Possible SSL Command and Control

Indicators of Compromise (IoCs)

IoC – Type – Description

193.222.96[.]91 – IP – Unusual Login Source

thecalebgroup[.]top – Domain – Possible C2 Endpoint

rz8js7sjbef[.]latovafineart[.]life – Domain – Possible C2 Endpoint

https://docsend[.]com/view/vcdmsmjcskw69jh9 - Domain - Phishing Link

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
Zoe Tilsiter
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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