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June 27, 2021

Post-Mortem Analysis of a SQL Server Exploit

Learn about the post-mortem analysis of a SQL Server exploit. Discover key insights and strategies to enhance your cybersecurity defenses.
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
Max Heinemeyer
Global Field CISO
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27
Jun 2021

While SaaS and IoT devices are increasingly popular vectors of intrusion, server-side attacks remain a serious threat to organizations worldwide. With sophisticated vulnerability scanning tools, attackers can now pinpoint security flaws in seconds, finding points of entry across the attack surface. Human security teams often struggle to keep pace with the constant wave of newly documented vulnerabilities and patches.

Darktrace recently stopped a targeted cyber-attack by an unknown attacker. After the initial entry, the attacker exploited an unpatched vulnerability (CVE-2020-0618), granting a low-privileged credential the ability to remotely execute code. This enabled the attacker to spread laterally and eventually establish a foothold in the system by creating a new user account.

The server-side attack cycle: authenticates user; scans network; infects three servers; downloads malware; c2 traffic; creates new user.

Figure 1: Overview of the server-side attack cycle.

This blog breaks down the intrusion and explores how Darktrace’s Autonomous Response technology took three surgical actions to halt the attacker’s movements.

Unknown threat actors exploit a vulnerability

Initial compromise

At a financial firm in Canada with around 3,000 devices, Cyber AI detected the use of a new credential, ‘parents’. The attacker used this credential to access the company’s internal environment through the VPN. From there, the credential authenticated to a desktop using NT LAN Manager (NTLM). No further suspicious activity was observed.

NTLM is a popular attack vector for cyber-criminals as it is vulnerable to multiple methods of compromise, including brute-force and ‘pass the hash’. The initial access to the credential could have been obtained via phishing before Darktrace had been deployed.

Figure 2: The credential was first observed on the device five days prior to reconnaissance. The attacker performed reconnaissance and lateral movement for two days, until the compromised devices were taken down.

Internal reconnaissance

Five days later, the ‘parents’ credential was seen logging onto the desktop. The desktop began scanning the network – over 80 internal IPs – on Port 443 and 445.

Shortly after the scan, the device used Nmap to attempt to establish SMBv1 sessions to 139 internal IPs, using guest / user credentials. 79 out of the 278 sessions were successful, all using the login.

Figure 3: New failed internal connections performed by an initially infected desktop, in a similar incident. The graph highlights a surge in failed internal connections and model breaches.

The network scan was the first stage after intrusion, enabling the attacker to find out which services were running, before looking for unpatched vulnerabilities.

Nmap has multiple built-in functionalities which are often exploited for reconnaissance and lateral movement. In this case, it was being used to establish the SMBv1 sessions to the domain controller, saving the attacker from having to initiate SMBv1 sessions with each destination one by one. SMBv1 has well-known vulnerabilities and best practice is to disable it where possible.

Lateral movement

The desktop began controlling services (svcctl endpoint) on a SQL server. It was observed both creating and starting services (CreateServiceW, StartServiceW).

The desktop then initiated an unencrypted HTTP connection to a SQL Reporting server. This was the first HTTP connection between the two devices and the first time the user agent had been seen on the device.

A packet capture of the connection reveals a POST that is seen in an exploit of CVE-2020-0613. This vulnerability is a deserialization issue, whereby the server mishandles carefully crafted page requests and allows low-privileged accounts to establish a reverse shell and remotely execute code on the server.

Figure 4: A partial PCAP of the HTTP connection. The traffic matches the CVE-2020-0618 exploit, which enables Remote Code Execution (RCE) in SQL Server Reporting Services (SSRS).

Most movements were seen in East-West traffic, with readily-available remote procedure call (RPC) methods. Such connections are abundant in systems. Without learning an organization’s ‘pattern of life’, it would have been near-impossible to highlight the malicious connections.

Cyber AI detected connections to the svcctl endpoint, via the DCE-RPC endpoint. This is called the 'service control' endpoint and is used to remotely control running processes on a device.

During the lateral movement from the desktop, the HTTP POST request revealed that the desktop was exploiting CVE-2020-0613. The attacker had managed to find and exploit an existing vulnerability which hadn’t been patched.

Darktrace was the only tool which alerted to the HTTP connection, revealing this underlying (and concluding) exploit. The AI determined that the user agent was unusual for the device and for the wider organization, and that the connection was highly anomalous. This connection would have gone otherwise amiss, since HTTP connections are common in most digital environments.

Because the attacker on the desktop used readily-available tools and protocols, such as Nmap, DCE-RPC, and HTTP, the device went undetected by all the other cyber defenses. However, Cyber AI noticed multiple scanning and lateral movement anomalies – triggering high-fidelity detections which would have been alerted to with Proactive Threat Notifications.

Command and control (C2) communication

The next day, the attacker connected to an SNMP server from the VPN. The connection used the ‘parents’ RDP cookie.

Immediately after the RDP connection began, the server connected to Pastebin and downloaded small amounts of encrypted data. Pastebin was likely being used as a vector to drop malicious scripts onto the device.

The SNMP server then started controlling services (svcttl) on the SQL server: again, creating and starting services.

Following this, both the SQL server and the SNMP server made a high volume of SSL connections to a rare external domain. One upload to the destination was around 21 MB, but otherwise the connections were mostly the same packet size. This, among other factors, indicated that the destination was being used as a C2 server.

Figure 5: Example Cyber AI Analyst investigation into beaconing activity by a SQL server.

With just one compromised credential, the attacker was now connecting to the VPN and infecting multiple servers on the company’s internal network.

The attacker dropped scripts onto the host using Pastebin. Darktrace alerted on this because Pastebin is highly rare for the organization. In fact, these connections were the first time it had been seen. Most security tools would miss this, as Pastebin is a legitimate site and would not be blocked by open-source intelligence (OSINT).

Even if a lesser-known Pastebin alternative had been used – say, in an environment where Pastebin was blocked on the firewall but the alternative not — Darktrace would have picked up on it in exactly the same way.

The C2 beaconing endpoint – dropbox16[.]com – has no OSINT information available online. The connections were on Port 443 and nothing about them was notable except from their rarity on the company’s system. Darktrace sent alerts because of its high rarity, rather than relying on known signatures.

Achieve persistence

After another Pastebin pull, the attacker attempted to maintain a greater foothold and escalate privileges by creating a new user using the SamrCreateUser2InDomain operation (endpoint: samr).

To establish persistence, the attacker now created a new user through a specific DCE-RPC command to the domain controller. This was highly unusual activity for the device, and was given a 100% anomaly score for ‘New or Uncommon Occurrence’.

If Darktrace had not alerted on this activity, the attacker would have continued to access files and make further inroads in the company, extracting sensitive data and potentially installing ransomware. This could have led to sensitive data loss, reputational damage, and financial losses for the company.

The value of Autonomous Response

The organization had Antigena in passive mode, so although it was not able to respond autonomously, we have visibility into the actions that it would have taken.

Antigena would have taken three actions on the initially infected desktop, as shown in the table below. The actions would have taken effect immediately in response to the first scan and the first service control requests.

During the two days of reconnaissance and lateral movement activity, these were the only steps Antigena suggested. The steps were all directly relevant to the intrusion – there was no attempt to block anything unrelated to the attack, and no other Antigena actions were triggered during this period.

By surgically blocking connections on specific ports during the scanning activity and enforcing the ‘pattern of life’ on the infected desktop, Antigena would have paralyzed the attacker’s reconnaissance efforts.

Furthermore, unusual service control attempts performed by the device would have been halted, minimizing the damage to the targeted destination.

Antigena would have delivered these blocks directly or via whatever integration was most suitable for the customer, such as firewall integrations or NAC integrations.

Lessons learned

The threat story above demonstrates the importance of controlling the access granted to low-privileged credentials, as well as remaining up-to-date with security patches. Since such attacks take advantage of existing network infrastructure, it is extremely difficult to detect these anomalous connections without the use of AI.

There was a delay of several days between the initial use of the ‘parents’ credentials and the first signs of lateral movement. This dormancy period – between compromise and the start of internal activities – is commonly seen in attacks. It likely indicates that the attacker was checking initially if their access worked, and then re-visiting the victim for further compromise once their schedule allowed for it.

Stopping a server-side attack

This compromise is reflective of many real-life intrusions: attacks cannot be easily attributed and are often conducted by sophisticated, unidentified threat actors.

Nevertheless, Darktrace managed to detect each stage of the attack cycle: initial compromise, reconnaissance, lateral movement, established foothold, and privilege escalation, and had Antigena been in active mode, it would have blocked these connections, and even prevented the initial desktop from ever exploiting the SQL vulnerability, which allowed the attacker to execute code remotely.

One day later, after seeing the power of Autonomous Response, the company decided to deploy Antigena in active mode.

Thanks to Darktrace analyst Isabel Finn for her insights on the above threat find.

Darktrace model detections:

  • Device / Anomalous Nmap SMB Activity
  • Device / Network Scan - Low Anomaly Score
  • Device / Network Scan
  • Device / ICMP Address Scan
  • Device / Suspicious Network Scan Activity
  • Anomalous Connection / New or Uncommon Service Control
  • Device / Multiple Lateral Movement Model Breaches
  • Device / New User Agent To Internal Server
  • Compliance / Pastebin
  • Device / Repeated Unknown RPC Service Bind Errors
  • Anomalous Server Activity / Rare External from Server
  • Compromise / Unusual Connections to Rare Lets Encrypt
  • User / Anomalous Domain User Creation Or Addition To Group


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
Max Heinemeyer
Global Field CISO

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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
Technical Author (AI) Developer

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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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Mikey Anderson
Product Marketing Manager, Network Detection & Response
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