November 12, 2025

Unmasking Vo1d: Inside Darktrace’s Botnet Detection

Earlier this year, Darktrace investigated the Vo1d malware campaign, tracing its activity from DGA-based DNS beaconing to major cloud infrastructure and ultimately to its C2 server communications. This blog explores how Darktrace detected Vo1d and presents a detailed timeline of Cyber AI Analyst’s investigation.

Written by

Christina Kreza

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

Christina Kreza

Cyber Analyst

Unmasking Vo1d: Inside Darktrace’s Botnet Detection

Nov 2025

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)

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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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Written by

Christina Kreza

Cyber Analyst

Inside the SOC

Written by

Christina Kreza

Cyber Analyst

The Darktrace Community

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Network

March 31, 2026

Phantom Footprints: Tracking GhostSocks Malware

Why are attackers using residential proxies?

In today's threat landscape, blending in to normal activity is the key to success for attackers and the growing reliance on residential proxies shows a significant shift in how threat actors are attempting to bypass IP detection tools.

The increasing dependency on residential proxies has exposed how prevalent proxy services are and how reliant a diverse range of threat actors are on them. From cybercriminal groups to state‑sponsored actors, the need to bypass IP detection tools is fundamental to the success of these groups. One malware that has quietly become notorious for its ability to avoid anomaly detection is GhostSocks, a malware that turns compromised devices into residential proxies.

What is GhostSocks?

Originally marketed on the Russian underground forum xss[.]is as a Malware‑as‑a‑Service (MaaS), GhostSocks enables threat actors to turn compromised devices into residential proxies, leveraging the victim's internet bandwidth to route malicious traffic through it.

How does Ghostsocks malware work?

The malware offers the threat actor a “clean” IP address, making it look like it is coming from a household user. This enables the bypassing of geographic restrictions and IP detection tools, a perfect tool for avoiding anomaly detection. It wasn’t until 2024, when a partnership was announced with the infamous information stealer Lumma Stealer, that GhostSocks surged into widespread adoption and alluded to who may be the author of the proxy malware.

Written in GoLang, GhostSocks utilizes the SOCKS5 proxy protocol, creating a SOCKS5 connection on infected devices. It uses a relay‑based C2 implementation, where an intermediary server sits in between the real command-and-control (C2) server and the infected device.

How does Ghostsocks malware evade detection?

To further increase evasion, the Ghostsocks malware wraps its SOCKS5 tunnels in TLS encryption, allowing its malicious traffic to blend into normal network traffic.

Early variants of GhostSocks do not implement a persistence mechanism; however, later versions achieve persistence via registry run keys, ensuring sustained proxy operational time [1].

While proxying is its primary purpose, GhostSocks also incorporates backdoor functionality, enabling malicious actors to run arbitrary commands and download and deploy additional malicious payloads. This was evident with the well‑known ransomware group Black Basta, which reportedly used GhostSocks as a way of maintaining long‑term access to victims’ networks [1].

Darktrace’s detection of GhostSocks Malware

Darktrace observed a steady increase in GhostSocks activity across its customer base from late 2025, with its Threat Research team identifying multiple incidents involving the malware. In one notable case from December 2025, Darktrace detected GhostSocks operating alongside Lumma Stealer, reinforcing that the partnership between Lumma and GhostSocks remains active despite recent attempts to disrupt Lumma’s infrastructure.

Darktrace’s first detection of GhostSocks‑related activity came when a device on the network of a customer in the education sector began making connections to an endpoint with a suspicious self‑signed certificate that had never been seen on the network before.

The endpoint in question, 159.89.46[.]92 with the hostname retreaw[.]click, has been flagged by multiple open‑source intelligence (OSINT) sources as being associated with Lumma Stealer’s C2 infrastructure [2], indicating its likely role in the delivery of malicious payloads.

Figure 1: Darktrace’s detection of suspicious SSL connections to retreaw[.]click, indicating an attempted link to Lumma C2 infrastructure.

Less than two minutes later, Darktrace observed the same device downloading the executable (.exe) file “Renewable.exe” from the IP 86.54.24[.]29, which Darktrace recognized as 100% rare for this network.

Figure 2: Darktrace’s detection of a device downloading the unusual executable file “Renewable.exe”.

Both the file MD5 hash and the executable itself have been identified by multiple OSINT vendors as being associated with the GhostSocks malware [3], with the executable likely the backdoor component of the GhostSocks malware, facilitating the distribution of additional malicious payloads [4].

Following this detection, Darktrace’s Autonomous Response capability recommended a blocking action for the device in an early attempt to stop the malicious file download. In this instance, Darktrace was configured in Human Confirmation Mode, meaning the customer’s security team was required to manually apply any mitigative response actions. Had Autonomous Response been fully enabled at the time of the attack, the connections to 86.54.24[.]29 would have been blocked, rendering the malware ineffective at reaching its C2 infrastructure and halting any further malicious communication.

Figure 3: Darktrace’s Autonomous Response capability suggesting blocking the suspicious connections to the unusual endpoint from which the malicious executable was downloaded.

As the attack was able to progress, two days later the device was detected downloading additional payloads from the endpoint www.lbfs[.]site (23.106.58[.]48), including “Setup.exe”, “,.exe”, and “/vp6c63yoz.exe”.

Figure 4: Darktrace’s detection of a malicious payload being downloaded from the endpoint www.lbfs[.]site.

Once again, Darktrace recognized the anomalous nature of these downloads and suggested that a “group pattern of life” be enforced on the offending device in an attempt to contain the activity. By enforcing a pattern of life on a device, Darktrace restricts its activity to connections and behaviors similar to those performed by peer devices within the same group, while still allowing it to carry out its expected activity, effectively preventing deviations indicative of compromise while minimizing disruption. As mentioned earlier, these mitigative actions required manual implementation, so the activity was able to continue. Darktrace proceeded to suggest further actions to contain subsequent malicious downloads, including an attempt to block all outbound traffic to stop the attack from progressing.

Figure 5: An overview of download activity and the Autonomous Response actions recommended by Darktrace to block the downloads.

Around the same time, a third executable download was detected, this time from the hostname hxxp[://]d2ihv8ymzp14lr.cloudfront.net/2021-08-19/udppump[.]exe, along with the file “udppump.exe”.While GhostSocks may have been present only to facilitate the delivery of additional payloads, there is no indication that these CloudFront endpoints or files are functionally linked to GhostSocks. Rather, the evidence points to broader malicious file‑download activity.

Shortly after the multiple executable files had been downloaded, Darktrace observed the device initiating a series of repeated successful connections to several rare external endpoints, behavior consistent with early-stage C2 beaconing activity.

Cyber AI Analyst’s investigation

Figure 7: Darktrace’s detection of additional malicious file downloads from malicious CloudFront endpoints.

Throughout the course of this attack, Darktrace’s Cyber AI Analyst carried out its own autonomous investigation, piecing together seemingly separate events into one wider incident encompassing the first suspicious downloads beginning on December 4, the unusual connectivity to many suspicious IPs that followed, and the successful beaconing activity observed two days later. By analyzing these events in real-time and viewing them as part of the bigger picture, Cyber AI Analyst was able to construct an in‑depth breakdown of the attack to aid the customer’s investigation and remediation efforts.

Figure 8: Cyber AI Analyst investigation detailing the sequence of events on the compromised device, highlighting its extensive connectivity to rare endpoints, the related malicious file‑download activity, and finally the emergence of C2 beaconing behavior.

Conclusion

The versatility offered by GhostSocks is far from new, but its ability to convert compromised devices into residential proxy nodes, while enabling long‑term, covert network access—illustrates how threat actors continue to maximise the value of their victims’ infrastructure. Its growing popularity, coupled with its ongoing partnership with Lumma, demonstrates that infrastructure takedowns alone are insufficient; as long as threat actors remain committed to maintaining anonymity and can rapidly rebuild their ecosystems, related malware activity is likely to persist in some form.

Credit to Isabel Evans (Cyber Analyst), Gernice Lee (Associate Principal Analyst & Regional Consultancy Lead – APJ)
Edited by Ryan Traill (Content Manager)

Appendices