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February 24, 2021

LockBit Ransomware Analysis: Compromised Credentials

Darktrace examines how a LockBit ransomware attack that took place over just four hours was caused by one compromised credential. Read more here.
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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24
Feb 2021

Lockbit ransomware found

LockBit ransomware was recently identified by Darktrace's Cyber AI during a trial with a retail company in the US. After an initial foothold was established via a compromised administrative credential, internal reconnaissance, lateral movement, and encryption of files occurred simultaneously, allowing the ransomware to steamroll through the digital system in just a few hours.

This incident serves as the latest reminder that ransomware campaigns now move through organizations at a speed that far outpaces human responders, demonstrating the need for machine-speed Autonomous Response to contain the threat before damage is done.

Lockbit ransomware defined

First discovered in 2019, LockBit is a relatively new family of ransomware that quickly exploits commonly available protocols and tools like SMB and PowerShell. It was originally known as ‘ABCD’ due the filename extension of the encrypted files, before it started using the current .lockbit extension. Since those early beginnings, it has evolved into one of the most calamitous strains of malware to date, asking for an average ransom of around $40,000 per organization.

As cyber-criminals level up the speed and scale of their attacks, ransomware remains a critical concern for organizations across every industry. In the past 12 months, Darktrace has observed an increase of over 20% in ransomware incidents across its customer base. Attackers are constantly developing new threat variants targeting exploits, utilizing off-the-shelf tools, and profiting from the burgeoning Ransomware-as-a-Service (RaaS) business model.

How does LockBit work?

In a typical attack, a threat actor will spend days or weeks inside a system, manually screening for the best way to grind the victim’s business to a halt. This phase tends to expose multiple indicators of compromise such as command and control (C2) beaconing, which Darktrace AI identifies in real time.

LockBit, however, only requires the presence of a human for a number of hours, after which it propagates through a system and infects other hosts on its own, without the need for human oversight. Crucially, the malware performs reconnaissance and continues to spread during the encryption phase. This allows it to cause maximal damage faster than other manual approaches.

AI-powered defense is essential in fighting back against these machine-driven attacks, which have the capacity to spread at speed and scale, and often go undetected by signature-based security tools. Cyber AI augments human teams by not only detecting the subtle signs of a threat, but autonomously responding in seconds, quicker than any human can be expected to react.

Ransomware analysis: Breaking down a LockBit attack with AI

Figure 1: Timeline of attack on the infected host and the encryption host. The infected host was the device initially infected with LockBit, which then spread to the encryption host, the device which performed the encryption.

Initial compromise

The attack commenced when a cyber-criminal gained access to a single privileged credential – either through a brute-force attack on an externally facing device, as seen in previous LockBit ransomware attacks, or simply with a phishing email. With the use of this credential, the device was able to spread and encrypt files within hours of the initial infection.

Had the method of infiltration been via phishing attack, a route that has become increasingly popular in recent months, Darktrace/ EMAIL would have withheld the email and stripped the malicious payloads, and so prevented the attack from the outset.

Limiting permissions, the use of strong passwords, and multi-factor authentication (MFA), are critical in preventing the exploitation of standard network protocols in such attacks.

Internal reconnaissance

At 14:19 local time, the first of many WMI commands (ExecMethod) to multiple internal destinations was performed by an internal IP address over DCE-RPC. This series of commands occurred throughout the encryption process. Given these commands were unusual in the context of the normal ‘pattern of life’ for the organization, Darktrace DETECT alerted the security team to each of these connections.

Within three minutes, the device had started to write executable files over SMB to hidden shares on multiple destinations – many of which were the same. File writes to hidden shares are ordinarily restricted. However, the unauthorized use of an administrative credential granted these privileges. The executable files were written to the Windows / Temp directory. Filenames had a similar formatting: .*eck[0-9]?.exe

Darktrace identified each of these SMB writes as a potential threat, since such administrative activity was unexpected from the compromised device.

The WMI commands and executable file writes continued to be made to multiple destinations. In less than two hours, the ExecMethod command was delivered to a critical device – the ‘encryption host’ – shortly followed by an executable file write (eck3.exe) to its hidden c$ share.

LockBit’s script has the capability to check its current privileges and, if non-administrative, it attempts to bypass using Windows User Account Control (UAC). This particular host did provide the required privileges to the process. Once this device was infected, encryption began.

File encryption

Only one second after encryption had started, Darktrace alerted on the unusual file extension appendage in addition to the previous, high-fidelity alerts for earlier stages of the attack lifecycle.

A recovery file – ‘Restore-My-Files.txt’ – was identified by Darktrace one second after the first encryption event. 8,998 recovery files were written, one to each encrypted folder.

An example of Darktrace’s Threat Visualizer showcasing anomalous SMB connections, with model breaches represented by dots.
Figure 2: An example of Darktrace’s Threat Visualizer showcasing anomalous SMB connections, with model breaches represented by dots.

The encryption host was a critical device that regularly utilized SMB. Exploiting SMB is a popular tactic for cyber-criminals. Such tools are so frequently used that it is difficult for signature-based detection methods to identify quickly whether their activity is malicious or not. In this case, Darktrace’s ‘Unusual Activity’ score for the device was elevated within two seconds of the first encryption, indicating that the device was deviating from its usual pattern of behavior.

Throughout the encryption process, Darktrace also detected the device performing network reconnaissance, enumerating shares on 55 devices (via srvsvc) and scanning over 1,000 internal IP addresses on nine critical TCP ports.

During this time, ‘Patient Zero’ – the initially infected device – continued to write executable files to hidden file shares. LockBit was using the initial device to spread the malware across the digital estate, while the ‘encryption host’ performed reconnaissance and encrypted the files simultaneously.

Despite Cyber AI detecting the threat even before the encryption had begun, the security team did not have eyes on Darktrace at the time of the attack. The intrusion was thus allowed to continue and over 300,000 files were encrypted and appended with the .lockbit extension. Four servers and 15 desktop devices were affected, before the attack was stopped by the administrators.

The rise of ‘hit and run’ ransomware

While most ransomware resides inside an organization for days or weeks, LockBit’s self-governing nature allows the attacker to ‘hit and run’, deploying the ransomware with minimal interaction required after the initial intrusion. The ability to detect anomalous activity across the entire digital infrastructure in real time is therefore crucial in LockBit’s prevention.

WMI and SMB are relied upon by the vast majority of companies around the world, and yet they were utilized in this attack to propagate through the system and encrypt hundreds of thousands of files. The prevalence and volume of these connections make them near-impossible to monitor with humans or signature-based detection techniques alone.

Moreover, the uniqueness of every enterprise’s digital estate impedes signature-based detection from effectively alerting on internal connections and the volume of such connections. Darktrace, however, uses machine learning to understand the individual pattern of behavior for each device, in this case allowing it to highlight the unusual internal activity as it occurred.

The organization involved did not have Darktrace’s Autonomous Response technology configured in active mode. If enabled, i would have surgically blocked the initial WMI operations and SMB drive writes that triggered the attack whilst allowing the critical network devices to continue standard operations. Even if the foothold had been established, D would have enforced the ‘pattern of life’ of the encryption host, preventing the cascade of encryption over SMB. This demonstrates the importance of meeting machine-speed attacks with autonomous cyber security, which reacts in real time to sophisticated threats when human security teams cannot.

LockBit has the ability to encrypt thousands of files in just seconds, even when targeting well-prepared organizations. This type of ransomware, with built-in worm-like functionality, is expected to become increasingly common over 2021. Such attacks can move at a speed which no human security team alone can match. Darktrace’s approach, which uses unsupervised machine learning, can respond in seconds to these rapid attacks and shut them down in their earliest stages.

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

Darktrace model detections:

  • Device / New or Uncommon WMI Activity
  • Compliance / SMB Drive Write
  • Compromise / Ransomware / Suspicious SMB Activity
  • Compromise / Ransomware / Ransom or Offensive Words Written to SMB
  • Anomalous File / Internal / Additional Extension Appended to SMB File
  • Anomalous Connection / SMB Enumeration
  • Device / Network Scan – Low Anomaly Score
  • Anomalous Connection / Sustained MIME Type Conversion
  • Anomalous Connection / Suspicious Read Write Ratio
  • Unusual Activity / Sustained Anomalous SMB Activity
  • Device / Large Number of Model Breaches

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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November 12, 2025

Unmasking Vo1d: Inside Darktrace’s Botnet Detection

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What is vo1d APK malware?

Vo1d malware first appeared in the wild in September 2024 and has since evolved into one of the most widespread Android botnets ever observed. This large-scale Android malware primarily targets smart TVs and low-cost Android TV boxes. Initially, Vo1d was identified as a malicious backdoor capable of installing additional third-party software [1]. Its functionality soon expanded beyond the initial infection to include deploying further malicious payloads, running proxy services, and conducting ad fraud operations. By early 2025, it was estimated that Vo1d had infected 1.3 to 1.6 million devices worldwide [2].

From a technical perspective, Vo1d embeds components into system storage to enable itself to download and execute new modules at any time. External researchers further discovered that Vo1d uses Domain Generation Algorithms (DGAs) to create new command-and-control (C2) domains, ensuring that regardless of existing servers being taken down, the malware can quickly reconnect to new ones. Previous published analysis identified dozens of C2 domains and hundreds of DGA seeds, along with new downloader families. Over time, Vo1d has grown increasingly sophisticated with clear signs of stronger obfuscation and encryption methods designed to evade detection [2].

Darktrace’s coverage

Earlier this year, Darktrace observed a surge in Vo1d-related activity across customer environments, with the majority of affected customers based in South Africa. Devices that had been quietly operating as expected began exhibiting unusual network behavior, including excessive DNS lookups. Open-source intelligence (OSINT) has long highlighted South Africa as one of the countries most impacted by Vo1d infections [2].

What makes the recent activity particularly interesting is that the surge observed by Darktrace appears to be concentrated specifically in South African environments. This localized spike suggests that a significant number of devices may have been compromised, potentially due to vulnerable software, outdated firmware, or even preloaded malware. Regions with high prevalence of low-cost, often unpatched devices are especially susceptible, as these everyday consumer electronics can be quietly recruited into the botnet’s network. This specifically appears to be the case with South Africa, where public reporting has documented widespread use of low-cost boxes, such as non-Google-certified Android TV sticks, that frequently ship with outdated firmware [3].

The initial triage highlighted the core mechanism Vo1d uses to remain resilient: its use of DGA. A DGA deterministically creates a large list of pseudo-random domain names on a predictable schedule. This enables the malware to compute hundreds of candidate domains using the same algorithm, instead of using a hard-coded single C2 hostname that defenders could easily block or take down. To ensure reproducible from the infected device’s perspective, Vo1d utilizes DGA seeds. These seeds might be a static string, a numeric value, or a combination of underlying techniques that enable infected devices to generate the same list of candidate domains for a time window, provided the same DGA code, seed, and date are used.

Interestingly, Vo1d’s DGA seeds do not appear to be entirely unpredictable, and the generated domains lack fully random-looking endings. As observed in Figure 1, there is a clear pattern in the names generated. In this case, researchers identified that while the first five characters would change to create the desired list of domain names, the trailing portion remained consistent as part of the seed: 60b33d7929a, which OSINT sources have linked to the Vo1d botnet. [2]. Darktrace’s Threat Research team also identified a potential second DGA seed, with devices in some cases also engaging in activity involving hostnames matching the regular expression /[a-z]{5}fc975904fc9\.(com|top|net). This second seed has not been reported by any OSINT vendors at the time of writing.

Another recurring characteristic observed across multiple cases was the choice of top-level domains (TLDs), which included .com, .net, and .top.

Figure 1: Advanced Search results showing DNS lookups, providing a glimpse on the DGA seed utilized.

The activity was detected by multiple models in Darktrace / NETWORK, which triggered on devices making an unusually large volume of DNS requests for domains uncommon across the network.

During the network investigation, Darktrace analysts traced Vo1d’s infrastructure and uncovered an interesting pattern related to responder ASNs. A significant number of connections pointed to AS16509 (AMAZON-02). By hosting redirectors or C2 nodes inside major cloud environments, Vo1d is able to gain access to highly available and geographically diverse infrastructure. When one node is taken down or reported, operators can quickly enable a new node under a different IP within the same ASN. Another feature of cloud infrastructure that hardens Vo1d’s resilience is the fact that many organizations allow outbound connections to cloud IP ranges by default, assuming they are legitimate. Despite this, Darktrace was able to identify the rarity of these endpoints, identifying the unusualness of the activity.

Analysts further observed that once a generated domain successfully resolved, infected devices consistently began establishing outbound connections to ephemeral port ranges like TCP ports 55520 and 55521. These destination ports are atypical for standard web or DNS traffic. Even though the choice of high-numbered ports appears random, it is likely far from not accidental. Commonly used ports such as port 80 (HTTP) or 443 (HTTPS) are often subject to more scrutiny and deeper inspection or content filtering, making them riskier for attackers. On the other hand, unregistered ports like 55520 and 55521 are less likely to be blocked, providing a more covert channel that blends with outbound TCP traffic. This tactic helps evade firewall rules that focus on common service ports. Regardless, Darktrace was able to identify external connections on uncommon ports to locations that the network does not normally visit.

The continuation of the described activity was identified by Darktrace’s Cyber AI Analyst, which correlated individual events into a broader interconnected incident. It began with the multiple DNS requests for the algorithmically generated domains, followed by repeated connections to rare endpoints later confirmed as attacker-controlled infrastructure. Cyber AI Analyst’s investigation further enabled it to categorize the events as part of the “established foothold” phase of the attack.

Figure 2: Cyber AI Analyst incident illustrating the transition from DNS requests for DGA domains to connections with resolved attacker-controlled infrastructure.

Conclusion

The observations highlighted in this blog highlight the precision and scale of Vo1d’s operations, ranging from its DGA-generated domains to its covert use of high-numbered ports. The surge in affected South African environments illustrate how regions with many low-cost, often unpatched devices can become major hubs for botnet activity. This serves as a reminder that even everyday consumer electronics can play a role in cybercrime, emphasizing the need for vigilance and proactive security measures.

Credit to Christina Kreza (Cyber Analyst & Team Lead) and Eugene Chua (Principal Cyber Analyst & Team Lead)

Edited by Ryan Traill (Analyst Content Lead)

Appendices

Darktrace Model Detections

  • Anomalous Connection / Devices Beaconing to New Rare IP
  • Anomalous Connection / Multiple Connections to New External TCP Port
  • Anomalous Connection / Multiple Failed Connections to Rare Endpoint
  • Compromise / DGA Beacon
  • Compromise / Domain Fluxing
  • Compromise / Fast Beaconing to DGA
  • Unusual Activity / Unusual External Activity

List of Indicators of Compromise (IoCs)

  • 3.132.75[.]97 – IP address – Likely Vo1d C2 infrastructure
  • g[.]sxim[.]me – Hostname – Likely Vo1d C2 infrastructure
  • snakeers[.]com – Hostname – Likely Vo1d C2 infrastructure

Selected DGA IoCs

  • semhz60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
  • ggqrb60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
  • eusji60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
  • uacfc60b33d7929a[.]com – Hostname – Possible Vo1d C2 DGA endpoint
  • qilqxfc975904fc9[.]top – Hostname – Possible Vo1d C2 DGA endpoint

MITRE ATT&CK Mapping

  • T1071.004 – Command and Control – DNS
  • T1568.002 – Command and Control – Domain Generation Algorithms
  • T1568.001 – Command and Control – Fast Flux DNS
  • T1571 – Command and Control – Non-Standard Port

[1] https://news.drweb.com/show/?lng=en&i=14900

[2] https://blog.xlab.qianxin.com/long-live-the-vo1d_botnet/

[3] https://mybroadband.co.za/news/broadcasting/596007-warning-for-south-africans-using-specific-types-of-tv-sticks.html

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About the author
Christina Kreza
Cyber Analyst

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November 6, 2025

Darktrace Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response

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Darktrace: The only Customers’ Choice for NDR in 2025

In a year defined by rapid change across the threat landscape, recognition from those who use and rely on security technology every day means the most.

That’s why we’re proud to share that Darktrace has been named the only Customers’ Choice in the 2025 Gartner® Peer Insights™ Voice of the Customer for Network Detection and Response (NDR).

Out of 11 leading NDR vendors evaluated, Darktrace stood alone as the sole Customers’ Choice, a recognition that we feel reflects not just our innovation, but the trust and satisfaction of the customers who secure their networks with Darktrace every day.

What the Gartner® Peer Insights™ Voice of the Customer means

“Voice of the Customer” is a document that synthesizes Gartner Peer Insights reviews into insights for buyers of technology and services. This aggregated peer perspective, along with the individual detailed reviews, is complementary to Gartner expert research and can play a key role in your buying process. Peers are verified reviewers of a technology product or service, who not only rate the offering, but also provide valuable feedback to consider before making a purchase decision. Vendors placed in the upper-right “Customers’ Choice” quadrant of the “Voice of the Customer” have scores that meet or exceed the market average for both axes (User Interest and Adoption, and Overall Experience).It’s not just a rating. We feel it’s a reflection of genuine customer sentiment and success in the field.

In our view, Customers consistently highlight Darktrace’s ability to:

  • Detect and respond to unknown threats in real time
  • Deliver unmatched visibility across IT, OT, and cloud environments
  • Automate investigations and responses through AI-driven insights

We believe this recognition reinforces what our customers already know: that Darktrace helps them see, understand, and stop attacks others miss.

A rare double: recognized by customers and analysts alike

This distinction follows another major recogniton. Darktrace’s placement as a Leader in the Gartner® Magic Quadrant™ for Network Detection and Response earlier this year.

That makes Darktrace the only vendor to achieve both:

  • A Leader status in the Gartner Magic Quadrant for NDR, and
  • A Customers’ Choice in Gartner Peer Insights 2025

It’s a rare double that we feel reflects both industry leadership and customer trust, two perspectives that, together, define what great cybersecurity looks like.

A Customers’ Choice across the network and the inbox

To us, this recognition also builds on Darktrace’s momentum across multiple domains. Earlier this year, Darktrace was also named a Customers’ Choice for Email Security Platforms in the Gartner® Peer Insights™ report.

With more than 1,000 verified reviews across Network Detection and Response, Email Security Platforms, and Cyber Physical Systems (CPS), we at Darktrace are proud to be trusted across the full attack surface, from the inbox to the industrial network.

Thank you to our customers

We’re deeply grateful to every customer who shared their experience with Darktrace on Gartner Peer Insights. Your insights drive our innovation and continue to shape how we protect complex, dynamic environments across the world.

Discover why customers choose Darktrace for network and email security.

Gartner® Peer Insights™ content consists of the opinions of individual end users based on their own experiences, and should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, 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 and Peer Insights are registered trademarks of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

Gartner, Voice of the Customer for Network Detection and Response, By Peer Community Contributor, 30 October 2025

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About the author
Mikey Anderson
Product Marketing Manager, Network Detection & Response
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