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June 27, 2021

Post-Mortem Analysis of a SQL Server Exploit

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

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.
Author
Max Heinemeyer
Global Field CISO

Max is a cyber security expert with over a decade of experience in the field, specializing in a wide range of areas such as Penetration Testing, Red-Teaming, SIEM and SOC consulting and hunting Advanced Persistent Threat (APT) groups. At Darktrace, Max is closely involved with Darktrace’s strategic customers & prospects. He works with the R&D team at Darktrace, shaping research into new AI innovations and their various defensive and offensive applications. Max’s insights are regularly featured in international media outlets such as the BBC, Forbes and WIRED. Max holds an MSc from the University of Duisburg-Essen and a BSc from the Cooperative State University Stuttgart in International Business Information Systems.

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April 4, 2025

Darktrace Named as Market Leader in the 2025 Omdia Market Radar for OT Cybersecurity Platforms

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We are pleased to announce that Darktrace / OT has been named a Market Leader in Omdia’s  2025 Market Radar for OT Cybersecurity Platforms. We believe this highlights our unique capabilities in the OT security market and follows similar recognition from Gartner who recently named Darktrace / OT as the sole Visionary in in the Magic Quadrant for Cyber Physical Systems (CPS) Protection Platforms market.

Historically, IT and OT systems have been managed separately, creating challenges due to the differences of priorities between the two domains. While both value availability, IT emphasizes confidentiality and integrity whereas OT focuses on safety and reliability. Organizations are increasingly converging these systems to reap the benefits of automation, efficiency, and productivity (1).

Omdia’s research highlights that decision makers are increasingly prioritizing comprehensive security coverage, centralized management, and advanced cybersecurity capabilities when selecting OT security solutions (1).

Rising productivity demands have driven the convergence of OT, IT, and cloud-connected systems, expanding attack surfaces and exposing vulnerabilities. Darktrace / OT provides a comprehensive OT security solution, purpose-built for critical infrastructure, offering visibility across OT, IoT, and IT assets, bespoke risk management, and industry-leading threat detection and response powered by Self-Learning AITM.

Figure 1: Omdia vendor overview for OT cybersecurity platforms
Figure 1: Omdia vendor overview for OT cybersecurity platforms

An AI-first approach to OT security  

Many OT security vendors have integrated AI into their offerings, often leveraging machine learning for anomaly detection and threat response. However, only a few have a deep-rooted history in AI, with longstanding expertise shaping their approach beyond surface-level adoption.

The Omdia Market Radar recognizes that Darktrace has extensive background in the AI space:

“Darktrace has invested extensively in AI research to fuel its capabilities since 2013 with 200-plus patent applications, providing anomaly detection with a significant level of customization, helping with SOC productivity and efficiency, streamlining to show what matters for OT.” (1)

Unlike other security approaches that rely on existing threat data, Darktrace / OT achieves this through Self-Learning AI that understands normal business operations, detecting and containing known and unknown threats autonomously, thereby reducing Sec Ops workload and ensuring minimal downtime

This approach extends to incident investigations where an industry-first Cyber AI AnalystTM automatically investigates all relevant threats across IT and OT, prioritizes critical incidents, and then summarizes findings in an easily understandable view—bringing production engineers and security analysts together to communicate and quickly take appropriate action.

Balancing autonomous response with human oversight

In OT environments where uptime is essential, autonomous response technology can be approached with apprehension. However, Darktrace offers customizable response actions that can be set to “human confirmation mode.”

Omdia recognizes that our approach provides customizable options for autonomous response:

“Darktrace’s autonomous response functionality enforces normal, expected behavior. This can be automated but does not need to be from the beginning, and it can be fine-tuned. Alternative step-by-step mitigations are clearly laid out step-by-step and updated based on organizational risk posture and current level of progress.” (1)

This approach allows security and production to keep humans-in-the-loop with pre-defined actions for potential attacks, enforcing normal to contain a threat, and allowing production to continue without disruption.  

Bespoke vulnerability and risk management

In the realm of OT security, asset management takes precedent as one of the key focus points for organizations. With a large quantity of assets to manage, practitioners are overwhelmed with information with no real way to prioritize or apply them to their unique environment.

Darktrace / OT is recognized by Omdia as having:

“Advanced risk management capabilities that showcase metrics on impact, exploit difficulty, and estimated cost of an attack […] Given the nascency of this capability (April 2024), it is remarkably granular in depth and insight.” (1)

Enabling this is Darktrace’s unique approach to AI extends to risk management capabilities for OT. Darktrace / OT understands customers’ unique risks by building a comprehensive and contextualized picture that goes beyond isolated CVE scoring. It combines attack path modeling with MITRE ATT&CK  techniques to provide hardening recommendations regardless of patching availability and gives you a clearer view of the potential impact of an attack from APT groups.

Modular, scalable security for industrial environments

Organizations need flexibility when it comes to OT security, some want a fully integrated IT-OT security stack, while others prefer a segregated approach due to compliance or operational concerns. The Darktrace ActiveAI Security Platform offers integrated security across multiple domains, allowing flexibility and unification across IT and OT security. The platform combines telemetry from all areas of your digital estate to detect and respond to threats, including OT, network, cloud, email, and user identities.

Omdia recognizes Darktrace’s expansive coverage across multiple domains as a key reason why organizations should consider Darktrace / OT:

“Darktrace’s modular and platform, approach offer’s integrated security across multiple domains. It offers the option of Darktrace / OT as a separate platform product for those that want to segregate IT and OT cybersecurity or are not yet in a position to secure both domains in tandem. The deployment of Darktrace’s platform is flexible—with nine different deployment options, including physical on-premises, virtual, cloud, and hybrid.” (1)

With flexible deployment options, Darktrace offers security teams the ability to choose a model that works best for their organization, ensuring that security doesn’t have to be a “one-size-fits-all” approach.

Conclusion: Why Darktrace / OT stands out in Omdia’s evaluation

Omdia’s 2025 Market Radar for OT Cybersecurity Platforms provides a technical-first, vendor-agnostic evaluation, offering critical insights for organizations looking to strengthen their OT security posture. Darktrace’s recognition as a Market Leader reinforces its unique AI-driven approach, flexible deployment options, and advanced risk management capabilities as key differentiators in an evolving threat landscape.

By leveraging Self-Learning AI, autonomous response, and real-world risk analysis, Darktrace / OT enables organizations to detect, investigate, and mitigate threats before they escalate, without compromising operational uptime.

Read the full report here!

References

  1. www.darktrace.com/resources/darktrace-named-a-market-leader-in-the-2025-omdia-market-radar-for-ot-cybersecurity-platforms
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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance

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Cloud

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April 2, 2025

Fusing Vulnerability and Threat Data: Enhancing the Depth of Attack Analysis

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Cado Security, recently acquired by Darktrace, is excited to announce a significant enhancement to its data collection capabilities, with the addition of a vulnerability discovery feature for Linux-based cloud resources. According to Darktrace’s Annual Threat Report 2024, the most significant campaigns observed in 2024 involved the ongoing exploitation of significant vulnerabilities in internet-facing systems. Cado’s new vulnerability discovery capability further deepens its ability to provide extensive context to security teams, enabling them to make informed decisions about threats, faster than ever.

Deep context to accelerate understanding and remediation

Context is critical when understanding the circumstances surrounding a threat. It can also take many forms – alert data, telemetry, file content, business context (for example asset criticality, core function of the resource), and risk context, such as open vulnerabilities.

When performing an investigation, it is common practice to understand the risk profile of the resource impacted, specifically determining open vulnerabilities and how they may relate to the threat. For example, if an analyst is triaging an alert related to an internet-facing Webserver running Apache, it would greatly benefit the analyst to understand open vulnerabilities in the Apache version that is running, if any of them are exploitable, whether a fix is available, etc. This dataset also serves as an invaluable source when developing a remediation plan, identifying specific vulnerabilities to be prioritised for patching.

Data acquisition in Cado

Cado is the only platform with the ability to perform full forensic captures as well as utilize instant triage collection methods, which is why fusing host-based artifact data with vulnerability data is such an exciting and compelling development.

The vulnerability discovery feature can be run as part of an acquisition – full or triage – as well as independently using a fast ‘Scan only’ mode.

Figure 1: A fast vulnerability scan being performed on the acquired evidence

Once the acquisition has completed, the user will have access to a ‘Vulnerabilities’ table within their investigation, where they are able to view and filter open vulnerabilities (by Severity, CVE ID, Resource, and other properties), as well as pivot to the full Event Timeline. In the Event Timeline, the user will be able to identify whether there is any malicious, suspicious or other interesting activity surrounding the vulnerable package, given the unified timeline presents a complete chronological dataset of all evidence and context collected.

Figure 2: Vulnerabilities discovered on the acquired evidence
Figure 3: Pivot from the Vulnerabilities table to the Event Timeline provides an in-depth view of file and process data associated with the vulnerable package selected. In this example, Apache2.

Future work

In the coming months, we’ll be releasing initial versions of highly anticipated integrations between Cado and Darktrace, including the ability to ingest Darktrace / CLOUD alerts which will automatically trigger a forensic capture (as well as a vulnerability discovery) of the impacted assets.

To learn more about how Cado and Darktrace will combine forces, request a demo today.

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About the author
Paul Bottomley
Director of Product Management, Cado
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