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October 18, 2022

Kill Chain Insights: Detecting AutoIT Malware Compromise

Discover how AutoIt malware operates and learn strategies to combat this emerging threat in our latest blog post.
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
Joel Davidson
Cyber Analyst
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18
Oct 2022

Introduction 

Good defence is like an onion, it has layers. Each part of a security implementation should have checks built in so that if one wall is breached, there are further contingencies. Security aficionados call this ‘defence in depth’, a military concept introduced to the cyber-sphere in 2009 [1]. Since then, it has remained a central tenet when designing secure systems, digital or otherwise [2]. Despite this, the attacker’s advantage is ever-present with continued development of malware and zero-day exploits. No matter how many layers a security platform has, how can organisations be expected to protect against a threat they do not know or even understand? 

Take the case of one Darktrace customer, a government-contracted manufacturing company located in the Americas. This company possesses a modern OT and IT network comprised of several thousand devices. They have dozens of servers, a few of which host Microsoft Exchange. Every week, these few mail servers receive hundreds of malicious payloads which will ultimately attempt to make their way into over a thousand different inboxes while dodging different security gateways. Had the RESPOND portion of Darktrace for Email been properly enabled, this is where the story would have ended. However, in June 2022 an employee made an instinctual decision that could have potentially cost the company its time, money, and reputation as a government contractor. Their crime: opening an unknown html file attached to a compelling phishing email. 

Following this misstep, a download was initiated which resulted in compromise of the system via vulnerable Microsoft admin tools from endpoints largely unknown to conventional OSINT sources. Using these tools, further malicious connectivity was accomplished before finally petering out. Fortunately, their existing Microsoft security gateway was up to date on the command and control (C2) domains observed in this breach and refused the connections.

Darktrace detected this activity at every turn, from the initial email to the download and subsequent attempted C2. Cyber AI Analyst stitched the events together for easy understanding and detected Indicators of Compromise (IOCs) that were not yet flagged in the greater intelligence community and, critically, did this all at machine speed. 

So how did the attacker evade action for so long? The answer is product misconfiguration - they did not refine their ‘layers’.  

Attack Details

On the night of June 8th an employee received a malicious email. Darktrace detected that this email contained a html attachment which itself contained links to endpoints 100% rare to the network. This email also originated from a never-before-seen sender. Although it would usually have been withheld based on these factors, the customer’s Darktrace/Email deployment was set to Advisory Mode meaning it continued through to the inbox. Late the next day, this user opened the attachment which then routed them to the 100% rare endpoint ‘xberxkiw[.]club’, a probable landing page for malware that did not register on OSINT available at the time.

Figure 1- Popular OSINT VirusTotal showing zero hits against the rare endpoint 

Only seconds after reaching the endpoint, Darktrace detected the Microsoft BITS user agent reaching out to another 100% rare endpoint ‘yrioer[.]mikigertxyss[.]com’, which generated a DETECT/Network model breach, ‘Unusual BITS Activity’. This was immediately suspicious since BITS is a deprecated and insecure windows admin tool which has been known to facilitate the movement of malicious payloads into and around a network. Upon successfully establishing a connection, the affected device began downloading a self-professed .zip file. However, Darktrace detected this file to be an extension-swapped .exe file. A PCAP of this activity can be seen below in Figure 2.

Figure 2- PCAP highlighting BITs service connections and false .zip (.exe) download

This activity also triggered a correlating breach of the ‘Masqueraded File Transfer’ model and pushed a high-fidelity alert to the Darktrace Proactive Threat Notification (PTN) service. This ensured both Darktrace and the customer’s SOC team were alerted to the anomalous activity.

At this stage the local SOC were likely beginning their triage. However further connections were being made to extend the compromise on the employee’s device and the network. The file they downloaded was later revealed to be ‘AutoIT3.exe’, a default filename given to any AutoIt script. AutoIt scripts do have legitimate use cases but are often associated with malicious activity for their ability to interact with the Windows GUI and bypass client protections. After opening, these scripts would launch on the host device and probe for other weaknesses. In this case, the script may have attempted to hunt passwords/default credentials, scan the local directory for common sensitive files, or scout local antivirus software on the device. It would then share any information gathered via established C2 channels.  

After the successful download of this mismatched MIME type, the device began attempting to further establish C2 to the endpoint ‘dirirxhitoq[.]kialsoyert[.]tk’. Even though OSINT still did not flag this endpoint, Darktrace detected this outreach as suspicious and initiated its first Cyber AI Analyst investigation into the beaconing activity. Following the sixth connection made to this endpoint on the 10th of June, the infected device breached C2 models, such as ‘Agent Beacon (Long Period)’ and ‘HTTP Beaconing to Rare Destination’. 

As the beaconing continued, it was clear that internal reconnaissance from AutoIt was not widely achieved, although similar IOCs could be detected on at least two other internal devices. This may represent other users opening the same malicious email, or successful lateral movement and infection propagation from the initial user/device. However comparatively, these devices did not experience the same level of infection as the first employee’s machine and never downloaded any malicious executables. AutoIt has a history of being used to deliver information stealers, which suggests a possible motivation had wider network compromise been successful [3].

Thankfully, after the 10th of June no further exploitation was observed. This was likely due to the combined awareness and action brought by the PTN alerting, static security gateways and action from the local security team. The company were protected thanks to defence in depth.  

Darktrace Coverage

Despite this, the role of Darktrace itself cannot be understated. Darktrace/Email was integral to the early detection process and provided insight into the vector and delivery methods used by this attacker. Post-compromise, Darktrace/Network also observed the full range of suspicious activity brought about by this incursion. In particular, the AI analyst feature played a major role in reducing the time for the SOC team to triage by detecting and flagging key information regarding some of the earliest IOCs.

Figure 3- Sample information pulled by AI analyst about one of the involved endpoints

Alongside the early detection, there were several instances where RESPOND/Network would have intervened however autonomous actions were limited to a small test group and not enabled widely throughout the customer’s deployment. As such, this activity continued unimpeded- a weak layer. Figure 4 highlights the first Darktrace RESPOND action which would have been taken.

Figure 4- Upon detecting the download of a mismatched mime from a rare endpoint, Darktrace RESPOND would have blocked all connections to the rare endpoint on the relevant port in a targeted manner

This Darktrace RESPOND action provides a precise and limited response by blocking the anomalous file download. However, after continued anomalous activity, RESPOND would have strengthened its posture and enforced stronger curbs across the wider anomalous activity. This stronger enforcement is a measure designed to relegate a device to its established norm. The breach which would generate this response can be seen below:

Figure 5- After a prolonged period of anomalous activity, Darktrace RESPOND would have stepped in to enforce the typical pattern of life observed on this device

Although Darktrace RESPOND was not fully enabled, this company had an extra layer of security in the PTN service, which alerted them just minutes after the initial file download was detected, alongside details relevant to the investigation. This ensured both Darktrace analysts and their own could review the activity and begin to isolate and remediate the threat. 

Concluding Insights

Thankfully, with multiple layers in their security, the customer managed to escape this incident largely unscathed. Quick and comprehensive email and network detection, customer alerting and local gateway blocking C2 connections ensured that the infection did not have leeway to propagate laterally throughout the network. However, even though this infection did not lead to catastrophe, the fact that it happened in the first place should be a learning point. 

Had RESPOND/Email been properly configured, this threat would have been stopped before reaching its intended recipients, removing the need to rely on end-users as a security measure. Furthermore, had RESPOND/Network been utilized beyond a limited test group, this activity would have been blocked at every other step of the network-level kill chain. From the anomalous MIME download to the establishment of C2, Darktrace RESPOND would have been able to effectively isolate and quarantine this activity to the host device, without any reliance on slow-to-update OSINT sources. RESPOND allows for the automation of time-sensitive security decisions and adds a powerful layer of defence that conventional security solutions cannot provide. Although it can be difficult to relinquish human ownership of these decisions, doing so is necessary to prevent unknown attackers from infiltrating using unknown vectors to achieve unknown ends.  

In conclusion, this incident demonstrates an effective case study around detecting a threat with novel IOCs. However, it is also a reminder that a company’s security makeup can always be improved. Overall, when building security layers in a company’s ‘onion’, it is great to have the best tools, but it is even greater to use them in the best way. Only with continued refining can organisations guarantee defence in depth. 

Thanks to Connor Mooney and Stefan Rowe for their contributions.

Appendices

Darktrace Model Detections

·      Anomalous File / EXE from Rare External Location 

·      Compromise / Agent Beacon (Long Period) 

·      Compromise / HTTP Beaconing to Rare Destination 

·      Device / Large Number of Model Breaches 

·      Device / Suspicious Domain 

·      Device / Unusual BITS Activity 

·      Enhanced Monitoring: Anomalous File / Masqueraded File Transfer 

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
Joel Davidson
Cyber Analyst

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May 26, 2026

Journey of a Threat: How Multi-Layered AI Works in Darktrace / EMAIL

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Darktrace / EMAIL is an implementation of the Darktrace methodology – a multi-layered AI system built into a single product. As with other Darktrace products, Darktrace / EMAIL learns the expected behaviours of an organization and its employees to identify novel threats and anomalous activity.

The diagram below represents the architecture of Darktrace / EMAIL’s multi-layered AI: a structured visualization of how intelligence is built, step by step, from raw data to actionable insight. Each layer plays a distinct role, feeding into the next: collecting data, understanding behaviour, analysing intent, making decisions, and presenting clear outcomes.

It all starts with an email

In this blog, we’ll follow a malicious email as it passes through the Darktrace / EMAIL system, showing exactly what happens as it travels through each layer of the pyramid, from basic data extraction to AI-powered metric creation, and finally deciding on any autonomous actions.

Let’s take this example email. As an end-user, you can see that this is an obvious extortion attempt where an adversary is threatening legal action if money isn’t paid within 24 hours, but how does Darktrace figure that out?

Part 1: Data Gathering

Processing of an email begins on point-of-transit for all inbound, outbound, or lateral emails. The first step is to extract information directly. This includes taking information from the headers (such as sending and receiving addresses, sender IP address, routing, and authentication protocols), as well as extraction of raw HTML and CSS data from the email itself.

This directly extracted information only allows for immediate surface level analysis, such as identifying signature-based attacks (known malicious addresses / domains), but is insufficient for identifying novel threats, complex attacks, or potential email or vendor compromise. This is where Darktrace’s AI analysis shines.

In this example, the SPF, DKIM, and DMARC authentication all passed successfully, showing that even malicious emails can still bypass these signature-based checks. Even with this success, Darktrace will continue to analyse the email.

Diving deeper into the technical information, we can see further information extracted from the headers, including aggregations from the header information, historical calculations such as the frequency and volume of emails to and from a particular domain, and much more.

Part 2: Social Graphing

Social Graphing involves the analysis of sending and receiving behaviours of different mailboxes to create peer-groups. Mailboxes who often send and receive to and from the same mailboxes, or exhibit other correlated behaviours, will be clustered together using a collection of unsupervised AI clustering systems. These groups may represent uses in the same teams who perform similar activity, groups of external facing mailboxes which often receive unsolicited emails, or groups of VIP users (such as C-suite or executives).

Social graphing is an essential component of Darktrace’s pattern of life analysis. This clustering allows Darktrace to understand the responsibilities of individuals – for example, behaviours which are anomalous for one group of users may be completely expected of another group.

In our example, the email was sent to 3 different users within the organization. As part of the social graphing, an “Association Anomaly” is calculated which indicates the likelihood that these users would receive emails from this user or domain, based on historical patterns.

Part 3: Metric Calculation

Metrics are calculated for every email, representing more complex characteristics of an email which can’t be directly extracted. Darktrace / EMAIL features over 1000 unique metrics, calculated both algorithmically and using an ensemble of AI systems.

Algorithmically calculated (non-AI) metrics include further historical calculations, and counts of features such as code blocks, and hidden text, to name a few.

AI-driven metrics include Inducement Classification which uses Natural Language Processing to identify potential phishing, solicitation, or extortion attempts; Named Entity Recognition to identify PII and other sensitive data within an email to support Data Loss Prevention; and many more.

We can follow our example email through this process and view the outcome of these metric calculations. Looking at the language metrics for this email, we can see that our email has reported a high extortion inducement, along with identification of banking information and language indicating urgency.

Part 4: Evaluation and Combination Engine (models)

Once all metrics have been calculated for an email, it gets sent to an evaluation and combination engine where the metrics are compared against blocks of logic to determine if an email contains a threat. One key model which alerted for this example message was a model to tag and block extortion attempts.

Since our example email has a high inducement score for extortion, along the presence of a bitcoin wallet address in the message, this model alerts. When a model in the engine is activated, actions are taken – in this case adding a tag to the email to flag it as extortion in the console and hold the email to prevent it from reaching the end-user mailbox.

Part 5: Meta-Modelling and Actions

Once the models have been run, the actions are taken against the email. If the email hasn’t been blocked or held, this is the point where it will reach the end-user's mailbox.

In the Darktrace / EMAIL UI, all actions models which alerted for an email and actions taken as a result can be seen. At the top of this page, you can see the alert indicating an extortion attempt along with the action to hold the message.

Alongside this, a meta-classifier is used to calculate an overall anomaly score for each email, based on how much the email differs from the pattern of life for the user. The score of the email is boosted by any actions that have taken place.

Part 6: Campaign Clustering

All emails are passed through the Darktrace / EMAIL campaign clustering system. This system creates clusters based on related features within the emails to identify groups of emails with the same sender or intent.

In our case, the email was identified as part of a campaign, alongside other emails which were also identified as extortion attempts against a small group of recipients.

Email campaigns may have additional actions applied to them if the campaign is deemed malicious, and in this case, you can see that the autonomous response was to hold all emails in the campaign. This means that if an email manages to avoid being blocked in the evaluation and combination engine but gets identified as part of the campaign, the hold action will be applied to it retroactively.

Part 7: Cyber AI Analyst

Darktrace’s Cyber AI Analyst presents key information and anomaly indicators for each email, such as further information about authentication, specific metrics, or other identified anomalies and mismatches.

Cyber AI Analyst can also utilize data from Darktrace / EMAIL to enhance its investigation of incidents from other Darktrace products, correlating relevant information to build a fuller picture. More information about the Cyber AI Analyst is available in the Darktrace AI Arsenal.

Part 8: Data Presentation (UI)

Once all processing has taken place against the email, it is presented in the Darktrace / EMAIL UI. Here, members of the SOC team can investigate incidents and anomalies, interact with malicious emails to see why they were blocked, and much more.

Our email stands out here with its 100 anomaly score. Every email which passes through a Darktrace / EMAIL will undergo the same thorough and rigorous analysis to identify potential risks, apply autonomous actions where required, and will ultimately be assigned a score to be displayed here. By providing a single overall score in the UI, rather than presenting emails in full, Darktrace / EMAIL allows SOC teams to more easily identify which emails are most important to investigate, increasing efficiency and reducing alert fatigue.

Take the next step

Many email security tools on the market that claim to be AI-driven are in fact bolting AI onto attack-centric approaches, which rely on automating the identification of known threats. These approaches struggle, and will continue to struggle, with adapting to novel, AI-generated threats.

By analyzing every email within its deeply integrated, multi-layered AI system, Darktrace / EMAIL is able to identify the subtle threats that others miss. This depth not only improves detection accuracy, but enables confident, autonomous action, giving security teams clearer insight into AI outcomes and greater control while supporting users.

For a full deep dive into each stage of the AI system, check out the white paper: A Guide to the Multi-Layered AI in Darktrace / EMAIL

Learn more about securing AI in your enterprise.

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Jamie Bali
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May 26, 2026

Darktrace named a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) For the Second Consecutive Year

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Continued recognition in NDR  

Darktrace has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), marking the second consecutive year in the Leaders quadrant.

We believe this consistency reflects sustained ability to execute, adapt, and deliver outcomes as the market evolves.

While we are immensely proud to be recognized by industry analysts as a Leader in NDR, that's just part of the story. Darktrace was also Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response based on direct customer feedback and real-world experience.

We believe the combination of these two signals is important. One reflects how the market is evaluated. The other reflects how technology performs in practice.

Why Darktrace continues to be recognized as a leader

We believe our position as a Leader for the second consecutive year reflects a combination of our sustained ability to execute in NDR, continued AI innovation, and proven delivery of security outcomes for customers and partners worldwide.

We also feel that our leadership in the NDR market is a testament to our unique and multi-layered AI approach, for which we were recognized as No.7 on Fast Company’s Most Innovative AI Companies of 2026 list, plus one of the hottest AI cybersecurity companies in CRN's AI 100.

Adapting to complex, real-world environments

Organizations are no longer protecting a single network perimeter. They are securing a mix of users, devices, applications, and data that move across hybrid environments.

Darktrace has focused on maintaining visibility and detection across these conditions, allowing security teams to understand activity as it scales.

Supporting organizations globally, not just technically

Security outcomes are shaped as much by deployment and support as they are by detection capability.

Darktrace continues to invest in regional presence across 29 countries around the world, helping organizations operationalize NDR in ways that align with local requirements, internal processes, and team structures.

Continuing to push AI beyond detection

AI in cybersecurity is often positioned as a way to improve detection accuracy. But the more important shift is how AI can influence decision-making and response.

Darktrace continues to develop models that learn from both live environments and historical incident data, combining real-time behavioral analysis with insights derived from prior attack patterns.

Using technologies such as the Incident Graph and DIGEST (Darktrace Incident Graph Evaluation for Security Threats), activity is not analyzed in isolation. Instead, relationships between users, devices, connections, and events are mapped over time, allowing the system to reconstruct how an incident is unfolding and how similar incidents have progressed in the past.

By evaluating these patterns, Darktrace can assess the likelihood that an incident will escalate, prioritizing the activity that poses the greatest risk and surfacing the most relevant context for investigation.

This shifts security operations from simply identifying anomalies to understanding their trajectory, helping teams anticipate potential impact and respond earlier with greater precision.

Why NDR is shifting from reactive detection to proactive, AI-driven security

Traditional approaches to NDR have been built around reactively identifying threats once they become clearly visible. That model is increasingly difficult to rely on.

Attackers are no longer operating in ways that stand out. They use valid credentials, trusted tools, and low-and-slow techniques that blend into everyday activity. By the time something looks obviously malicious, the impact is often already underway.

This is the core limitation of reactive detection. It depends on recognizing something that already looks like a threat.

As a result, many of the most consequential incidents today fall into a gap.

Insider activity, compromised credentials, and novel attacks rarely trigger traditional alerts because they do not follow known patterns. On the surface, they often appear legitimate, making them difficult to distinguish from normal behavior without deeper context.

This is why we believe this Gartner recognition reflects a broader shift in NDR toward autonomous, proactive and pre‑emptive security operations.

By understanding normal behavior within an environment, it is possible to identify subtle deviations rather than waiting for confirmation of threats as they are taking place.

Darktrace’s Self-Learning AI is designed for behavioral understanding. By continuously learning each organization’s normal patterns, it can detect deviations in real time, enabling a proactive and pre-emptive model of NDR where security teams can respond to early signs of risk as they emerge, reducing the window in which attacks can develop.

In multiple cases, this behavioral approach has led to early threat detection where Darktrace identified completely unknown threats, including pre-CVE zero-day activity. By detecting subtle behavioral changes before vulnerabilities were publicly disclosed or widely understood, organizations can mitigate threats before they do damage.

This shift is subtle but important. Modern NDR solutions must shift from a system that explains what happened to one that helps prevent threats from developing in the first place, and Darktrace is proud to be at the forefront of this shift - helping organizations build and maintain a state of proactive network resilience.

Continuing to innovate at the forefront of NDR

In our view, recognition as a Leader reflects where the market is today. Continuing to innovate defines what comes next.

As businesses evolve, new technologies like AI tools and agents introduce new security risks and challenges; security teams need more than simple detection. They need a complete understanding of risk as it develops, the ability to investigate it in context, and to contain threats at machine speed.  

Darktrace / NETWORK is built to deliver across that full spectrum. Its Self-Learning AI continuously adapts to each organization’s environment, identifying subtle behavioral changes that signal emerging threats. Integrated investigation and autonomous response reduce the time between detection and action, allowing teams to move with greater speed and confidence.

This combination enables organizations to detect and contain known, unknown, and insider threats as they develop, while also strengthening resilience over time.

As a two-time Leader in the Gartner® Magic Quadrant™ for NDR and the only 2025 Gartner® Peer Insights™ Customers’ Choice, we feel Darktrace continues to evolve its platform to meet the demands of modern environments, delivering a more complete and adaptive approach to network security.

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Disclaimer: The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) ,The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), Thomas Lintemuth, Charanpal Bhogal, Nahim Fazal, 18 May 2026.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

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