January 31, 2024

How Darktrace Defeated SmokeLoader Malware

Read how Darktrace's AI identified and neutralized SmokeLoader malware. Gain insights into their proactive approach to cybersecurity.

Written by

Patrick Anjos

Senior Cyber Analyst

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

Patrick Anjos

Senior Cyber Analyst

Jan 2024

What is Loader Malware?

Loader malware is a type of malicious software designed primarily to infiltrate a system and then download and execute additional malicious payloads.

In recent years, loader malware has emerged as a significant threat for organizations worldwide. This trend is expected to continue given the widespread availability of many loader strains within the Malware-as-a-Service (MaaS) marketplace. The MaaS marketplace contains a wide variety of innovative strains which are both affordable, with toolkits ranging from USD 400 to USD 1,650 [1], and continuously improving, aiming to avoid traditional detection mechanisms.

SmokeLoader is one such example of a MaaS strain that has been observed in the wild since 2011 and continues to pose a significant threat to organizations and their security teams.

How does SmokeLoader Malware work?

SmokeLoader’s ability to drop an array of different malware strains onto infected systems, from backdoors, ransomware, cryptominers, password stealers, point-of-sale malware and banking trojans, means its a highly versatile loader that has remained consistently popular among threat actors.

In addition to its versatility, it also exhibits advanced evasion strategies that make it difficult for traditional security solutions to detect and remove, and it is easily distributed via methods like spam emails or malicious file downloads.

Between July and August 2023, Darktrace observed an increasing trend in SmokeLoader compromises across its customer base. The anomaly-based threat detection capabilities of Darktrace, coupled with the autonomous response technology, identified and contained the SmokeLoader infections in their initial stages, preventing attackers from causing further disruption by deploying other malicious software or ransomware.

SmokeLoader Malware Attack Details

PROPagate Injection Technique

SmokeLoader utilizes the PROPagate code injection technique, a less common method that inserts malicious code into existing processes in order to appear legitimate and bypass traditional signature-based security measures [2] [3]. In the case of SmokeLoader, this technique exploits the Windows SetWindowsSubclass function, which is typically used to add or change the behavior of Windows Operation System. By manipulating this function, SmokeLoader can inject its code into other running processes, such as the Internet Explorer. This not only helps to disguise the malware's activity but also allows attackers to leverage the permissions and capabilities of the infected process.

Obfuscation Methods

SmokeLoader is known to employ several obfuscation techniques to evade the detection and analysis of security teams. The techniques include scrambling portable executable files, encrypting its malicious code, obfuscating API functions and packing, and are intended to make the malware’s code appear harmless or unremarkable to antivirus software. This allows attackers to slip past defenses and execute their malicious activities while remaining undetected.

Infection Vector and Communication

SmokeLoader typically spreads via phishing emails that employ social engineering tactics to convince users to unknowingly download malicious payloads and execute the malware. Once installed on target networks, SmokeLoader acts as a backdoor, allowing attackers to control infected systems and download further malicious payloads from command-and-control (C2) servers. SmokeLoader uses fast flux, a DNS technique utilized by botets whereby IP addresses associated with C2 domains are rapidly changed, making it difficult to trace the source of the attack. This technique also boosts the resilience of attack, as taking down one or two malicious IP addresses will not significantly impact the botnet's operation.

Continuous Evolution

As with many MaaS strains, SmokeLoader is continuously evolving, with its developers regularly adding new features and techniques to increase its effectiveness and evasiveness. This includes new obfuscation methods, injection techniques, and communication protocols. This constant evolution makes SmokeLoader a significant threat and underscores the importance of advanced threat detection and response capabilities solution.

Darktrace’s Coverage of SmokeLoader Attack

Between July and August 2023, Darktrace detected one particular SmokeLoader infection at multiple stages of its kill chain on a customer network. This detection was made possible by Darktrace DETECT’s anomaly-based approach and Self-Learning AI that allows it to identify subtle deviations in device behavior.

One of the key components of this process is the classification of endpoint rarity and determining whether an endpoint is new or unusual for any given network. This classification is applied to various aspects of observed endpoints, such as domains, IP addresses, or hostnames within the network. It thereby plays a vital role in identifying SmokeLoader activity, such as the initial infection vector or C2 communication, which typically involve a device contacting a malicious endpoint associated with SmokeLoader.

The First Signs of Infection SmokeLoader Infection

Beginning in July 2023, Darktrace observed a surge in suspicious activities that were assessed with moderate to high confidence to be associated with SmokeLoader malware.

For example on July 30, a device was observed making a successful HTTPS request to humman[.]art, a domain that had never been seen on the network, and therefore classified as 100% rare by DETECT. During this connection, the device in question received a total of 6.0 KiB of data from the unusual endpoint. Open-source intelligence (OSINT) sources reported with high confidence that this domain was associated with the SmokeLoader C2 botnet.

The device was then detected making an HTTP request to another 100% rare external IP, namely 85.208.139[.]35, using a new user agent. This request contained the URI ‘/DefenUpdate.exe’, suggesting a possible download of an executable (.exe) file. This was corroborated by the total amount of data received in this connection, 4.3 MB. Both the file name and its size suggest that the offending device may have downloaded additional malicious tooling from the SmokeLoader C2 endpoint, such as a trojan or information stealer, as reported on OSINT platforms [4].

Figure 1: Device event log showing the moment when a device made its first connection to a SmokeLoader associated domain, and the use of a new user agent. A few seconds later, the DETECT model “Anomalous Connection / New User Agent to IP Without Hostname” breached.

The observed new user agent, “Mozilla/5.0 (Windows NT 10.0; Win64; x64; Trident/7.0; rv:11.0) like Gecko” was identified as suspicious by Darktrace leading to the “Anomalous Connection / New User Agent to IP Without Hostname” DETECT model breach.

As this specific user agent was associated with the Internet Explorer browser running on Windows 10, it may not have appeared suspicious to traditional security tools. However, Darktrace’s anomaly-based detection allows it to identify and mitigate emerging threats, even those that utilize sophisticated evasion techniques.

This is particularly noteworthy in this case because, as discussed earlier, SmokeLoader is known to inject its malicious code into legitimate processes, like Internet Explorer.

Figure 2: Darktrace detecting the affected device leveraging a new user agent and establishing an anomalous HTTP connection with an external IP, which was 100% rare to the network.

C2 Communication

Darktrace continued to observe the device making repeated connections to the humman[.]art endpoint. Over the next few days. On August 7, the device was observed making unusual POST requests to the endpoint using port 80, breaching the ‘Anomalous Connection / Multiple HTTP POSTs to Rare Hostname’ DETECT model. These observed POST requests were observed over a period of around 10 days and consisted of a pattern of 8 requests, each with a ten-minute interval.

Figure 3: Model Breach Event Log highlighting the Darktrace DETECT model breach ‘Anomalous Connection / Multiple HTTP POSTs to Rare Hostname’.

Upon investigating the details of this activity identified by Darktrace DETECT, a particular pattern was observed in these requests: they used the same user-agent, “Mozilla/5.0 (Windows NT 10.0; Win64; x64; Trident/7.0; rv:11.0) like Gecko”, which was previously detected in the initial breach.

Additionally, they the requests had a constantly changing eferrer header, possibly using randomly generated domain names for each request. Further examination of the packet capture (PCAP) from these requests revealed that the payload in these POST requests contained an RC4 encrypted string, strongly indicating SmokeLoader C2 activity.

Figure4: Advanced Search results display an unusual pattern in the requests made by the device to the hostname humman[.]art. This pattern shows a constant change in the referrer header for each request, indicating anomalous behavior.

Figure 5: The PCAP shows the payload seen in these POST requests contained an RC4 encrypted string strongly indicating SmokeLoader C2 activity.

Unfortunately in this case, Darktrace RESPOND was not active on the network meaning that the attack was able to progress through its kill chain. Despite this, the timely alerts and detailed incident insights provided by Darktrace DETECT allowed the customer’s security team to begin their remediation process, implementing blocks on their firewall, thus preventing the SmokeLoader malware from continuing its communication with C2 infrastructure.

Darktrace RESPOND Halting Potential Threats from the Initial Stages of Detection

With Darktrace RESPOND, organizations can move beyond threat detection to proactive defense against emerging threats. RESPOND is designed to halt threats as soon as they are identified by DETECT, preventing them from escalating into full-blown compromises. This is achieved through advanced machine learning and Self-Learning AI that is able to understand the normal ‘pattern of life’ of customer networks, allowing for swift and accurate threat detection and response.

One pertinent example was seen on July 6, when Darktrace detected a separate SmokeLoader case on a customer network with RESPOND enabled in autonomous response mode. Darktrace DETECT initially identified a string of anomalous activity associated with the download of suspicious executable files, triggering the ‘Anomalous File / Multiple EXE from Rare External Locations’ model to breach.

The device was observed downloading an executable file (‘6523.exe’ and ‘/g.exe’) via HTTP over port 80. These downloads originated from endpoints that had never been seen within the customer’s environment, namely ‘hugersi[.]com’ and ‘45.66.230[.]164’, both of which had strongly been linked to SmokeLoader by OSINT sources, likely indicating the initial infection stage of the attack [5].

Figure 6: This figure illustrates Darktrace DETECT observing a device downloading multiple .exe files from rare endpoints and the associated model breach, ‘Anomalous File / Multiple EXE from Rare External Locations’.

Around the same time, Darktrace also observed the same device downloading an unusual file with a numeric file name. Threat actors often employ this tactic in order to avoid using file name patterns that could easily be recognized and blocked by traditional security measures; by frequently changing file names, malicious executables are more likely to remain undetected.

Figure 7: Graph showing the unusually high number of executable files downloaded by the device during the initial infection stage of the attack. The orange and red circles represent the number of model breaches that the device made during the observed activity related to SmokeLoader infection.

Figure 8: This figure illustrates the moment when Darktrace DETECT identified a suspicious download with a numeric file name.

With Darktrace RESPOND active and enabled in autonomous response mode, the SmokeLoader infection was thwarted in the first instance. RESPOND took swift autonomous action by blocking connections to the suspicious endpoints identified by DETECT, blocking all outgoing traffic, and enforcing a pre-established “pattern of life” on offending devices. By enforcing a patten of life on a device, Darktrace RESPOND ensures that it cannot deviate from its ‘normal’ activity to carry out potentially malicious activity, while allowing the device to continue expected business operations.

Figure 9: A total of 8 RESPOND actions were applied, including blocking connections to suspicious endpoints and domains associated with SmokeLoader.

In addition to the autonomous mitigative actions taken by RESPOND, this customer also received a Proactive Threat Notification (PTN) informing them of potentially malicious activity on their network. This prompted the Darktrace Security Operations Center (SOC) to investigate and document the incident, allowing the customer’s security team to shift their focus to remediating and removing the threat of SmokeLoader.

Conclusion

Ultimately, Darktrace showcased its ability to detect and contain versatile and evasive strains of loader malware, like SmokeLoader. Despite its adeptness at bypassing conventional security tools by frequently changing its C2 infrastructure, utilizing existing processes to infect malicious code, and obfuscating malicious file and domain names, Darktrace’s anomaly-based approach allowed it to recognize such activity as deviations from expected network behavior, regardless of their apparent legitimacy.

Considering SmokeLoader’s wide array of functions, including C2 communication that could be used to facilitate additional attacks like exfiltration, or even the deployment of information-stealers or ransomware, Darktrace proved to be crucial in safeguarding customer networks. By identifying and mitigating SmokeLoader at the earliest possible stage, Darktrace effectively prevented the compromises from escalating into more damaging and disruptive compromises.

With the threat of loader malware expected to continue growing alongside the boom of the MaaS industry, it is paramount for organizations to adopt proactive security solutions, like Darktrace DETECT+RESPOND, that are able to make intelligent decisions to identify and neutralize sophisticated attacks.

Credit to Patrick Anjos, Senior Cyber Analyst, Justin Torres, Cyber Analyst

Appendices

Darktrace DETECT Model Detections

- Anomalous Connection / New User Agent to IP Without Hostname

- Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

- Anomalous File / Multiple EXE from Rare External Locations

- Anomalous File / Numeric File Download

List of IOCs (IOC / Type / Description + Confidence)

- 85.208.139[.]35 / IP / SmokeLoader C2 Endpoint

- 185.174.137[.]109 / IP / SmokeLoader C2 Endpoint

- 45.66.230[.]164 / IP / SmokeLoader C2 Endpoint

- 91.215.85[.]147 / IP / SmokeLoader C2 Endpoint

- tolilolihul[.]net / Hostname / SmokeLoader C2 Endpoint

- bulimu55t[.]net / Hostname / SmokeLoader C2 Endpoint

- potunulit[.]org / Hostname / SmokeLoader C2 Endpoint

- hugersi[.]com / Hostname / SmokeLoader C2 Endpoint

- human[.]art / Hostname / SmokeLoader C2 Endpoint

- 371b0d5c867c2f33ae270faa14946c77f4b0953 / SHA1 / SmokeLoader Executable

References:

[1] https://bazaar.abuse.ch/sample/d7c395ab2b6ef69210221337ea292e204b0f73fef8840b6e64ab88595eda45b3/#intel

[2] https://malpedia.caad.fkie.fraunhofer.de/details/win.smokeloader

[3] https://www.darkreading.com/cyber-risk/breaking-down-the-propagate-code-injection-attack

[4] https://n1ght-w0lf.github.io/malware%20analysis/smokeloader/

[5] https://therecord.media/surge-in-smokeloader-malware-attacks-targeting-ukrainian-financial-gov-orgs

MITRE ATT&CK Mapping

Model: Anomalous Connection / New User Agent to IP Without Hostname

ID: T1071.001

Sub technique: T1071

Tactic: COMMAND AND CONTROL

Technique Name: Web Protocols

‍

Model: Anomalous Connection / Multiple HTTP POSTs to Rare Hostname

ID: T1185

Sub technique: -

Tactic: COLLECTION

Technique Name: Man in the Browser

‍

ID: T1071.001

Sub technique: T1071

Tactic: COMMAND AND CONTROL

Technique Name: Web Protocols

‍

Model: Anomalous File / Multiple EXE from Rare External Locations

ID: T1189

Sub technique: -

Tactic: INITIAL ACCESS

Technique Name: Drive-by Compromise

‍

ID: T1588.001

Sub technique: - T1588

Tactic: RESOURCE DEVELOPMENT

Technique Name: Malware

‍

Model: Anomalous File / Numeric File Download

ID: T1189

Sub technique: -

Tactic: INITIAL ACCESS

Technique Name: Drive-by Compromise

‍

ID: T1588.001

Sub technique: - T1588

Tactic: RESOURCE DEVELOPMENT

Technique Name: Malware

Written by

Patrick Anjos

Senior Cyber Analyst

Inside the SOC

Written by

Patrick Anjos

Senior Cyber Analyst

Product Marketing Manager, Network Detection & Response

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Blog

Network

November 13, 2025

Unmasking Vo1d: Inside Darktrace’s Botnet Detection

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

‍

About the author

Christina Kreza

Cyber Analyst

Blog

Network

November 6, 2025

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

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

About the author

Mikey Anderson

Product Marketing Manager, Network Detection & Response

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How Darktrace Defeated SmokeLoader Malware

What is Loader Malware?

How does SmokeLoader Malware work?

SmokeLoader Malware Attack Details

PROPagate Injection Technique

Obfuscation Methods

Infection Vector and Communication

Continuous Evolution

Darktrace’s Coverage of SmokeLoader Attack

The First Signs of Infection SmokeLoader Infection

C2 Communication

Darktrace RESPOND Halting Potential Threats from the Initial Stages of Detection

Conclusion

Appendices

Darktrace DETECT Model Detections

List of IOCs (IOC / Type / Description + Confidence)

References:

MITRE ATT&CK Mapping

Unmasking Vo1d: Inside Darktrace’s Botnet Detection

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

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Unmasking Vo1d: Inside Darktrace’s Botnet Detection

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Blog

Network

November 13, 2025

Unmasking Vo1d: Inside Darktrace’s Botnet Detection

What is vo1d APK malware?

Darktrace’s coverage

Conclusion

Appendices

Darktrace Model Detections

List of Indicators of Compromise (IoCs)

Selected DGA IoCs

MITRE ATT&CK Mapping

Blog

Network

November 6, 2025

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

Darktrace: The only Customers’ Choice for NDR in 2025

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

A Customers’ Choice across the network and the inbox

Thank you to our customers