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August 17, 2023

Successfully Containing an Admin Credential Attack

Discover how Darktrace's anomaly-based threat detection thwarted a cyber-attack on a customer's network, stopping a malicious actor in their tracks.
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
Zoe Tilsiter
Cyber Analyst
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17
Aug 2023

What is Admin Credential Abuse?

In an effort to remain undetected by increasingly vigilant security teams, malicious actors across the threat landscape often resort to techniques that allow them to remain ‘quiet’ on the network and carry out their objectives subtly. One such technique often employed by attackers is using highly privileged credentials to carry out malicious activity.

This emphasizes the need to be hyper vigilant and not assume that ‘administrative’ activity using privileged credentials is legitimate. In this way, both internal visibility and defense in-depth are needed, as well as a strong understanding of ‘normal’ administrative activity to then identify any deviations from this.  

In one recent example, Darktrace identified a threat actor attempting to use privileged administrative credentials to move laterally through a customer’s network and compromise two further critical servers. Darktrace DETECT™ identified that this activity was unusual and alerted the customer to early signs of compromise, reconnaissance and lateral movement to the other critical devices, while Darktrace RESPOND™ acted autonomously to inhibit the spread of activity and allowed the customer to quarantine the compromised devices.

Attack Overview and Darktrace Coverage

Over the course of a week in late May 2023, Darktrace observed a compromise on the network of a customer in the Netherlands. The threat actors primarily used living off the land techniques, abusing legitimate administrative credentials and executables to perform unexpected activities. This technique is intended to go under the radar of traditional security tools that are often unable to distinguish between the legitimate or malicious use of privileged credentials.

Darktrace was the only security solution in the customer’s stack that way able to detect and contain the attack, preventing it from spreading through their digital estate.

1. Device Reactivated

On May 22, 2023, Darktrace began to observe traffic originating from a File Server device which prior to this, had been been inactive on the network for some time, with no incoming or outgoing traffic recently observed for this IP. Therefore, upon initiating connections again, Darktrace’s AI tagged the device with the “Re-Activated Device” label. It also tagged the device as an “Internet Facing System”, which could represent an initial point of compromise.

Following this, the device was observed using an administrative credential that was commonly used across network, with no clear indications of brute-force activity or successive login failures preceeding this activity. The unusual use of a known credential on a network can be very difficult to detect for traditional security tools. Darktrace’s anomaly-based detection allows it to recognize subtle deviations in device behavior meaning it is uniquely placed to recognize this type of activity.

2. Reconaissance  

On the following day, the affected device began to perform SMB scans for open 445 ports, and writing files such as srvsvc and winreg, both of which are indicative of network  reconnaissance. Srvsvc is used to enumerate available SMB shares on destination devices which could be used to then write malicious files to these shares, while Winreg (Windows Registry) is used to store information that configures users, applications, and hardware devices [1]. Darktrace also observed the device carrying out DCE_RPC activity and making Windows Management Instrumentation (WMI) enumeration requests to other internal devices.

3. Lateral Movement via SMB

On May 24 and May 30, Darktrace observed the same device writing files over SMB to a number of other internal devices, including an SMB server and the Domain Controller. Darktrace identified that these writers were to privileged credential paths, such as C$ and ADMIN$, and it further recognized that the device was using the compromised administrative credential.

The files included remote command executable files (.exe) and batch scripts which execute commands upon clicking in a serial order. This behavior is indicative of a threat actor performing lateral movement in an attempt to infect other devices and strengthen their foothold in the network.

Files written:

·       LogConverter.bat

·       sql.bat

·       Microsoft.NodejsTools.PressAnyKey.exe

·       PSEXESVC.exe

·       Microsoft.NodejsTools.PressAnyKey.lnk

·       CG6oDkyFHl3R.t

5. Reconnaissance Spread

Around the same time as the observed lateral movement activity, between May 24 and May 30, the initially compromised device continued SMB and DCE_RPC activity, mainly involving SMB writes of files such as srvsvc, and PSEXESVC.exe.

Then, on May 28, Darktrace identified another internal Domain Controller engaging in similar suspicious behavior to the original compromised device. This included network scanning, enumeration and service control activity, indicating a spread of further malicious reconnaissance.

Following the successful detection of this activity, Darktrace’s Cyber AI Analyst launched autonomous investigations which was able to correlate incidents from multiple affected devices across the network, in doing so connecting multiple incidents into one security event.

Figure 1: Cyber AI Analyst connecting multiple events into one incident
Figure 2: Cyber AI Analyst investigation process to identify suspicious activity.

6. Lateral Movement

Alongside these SMB writes, the initially compromised device was seen connecting to various internal devices over ports associated with administrative protocols such as Remote Desktop Protocol (RDP). It also made a high volume of NTLM login failures for the credential ‘administrator’, suggesting that the malicious actor was attempting to brute-force an administrative credential.

7. Suspicious External Activity

Following earlier SMB writes from the initially compromised device to the Domain Controller server, the Domain Controller was seen making an unusual volume of external connections to rare endpoints which could indicate malicious command and control (C2) communication.

Alongside this activity, between May 30 and June 1, Darktrace also observed an unusually large number (over 12 million) of incoming connections from external endpoints. This activity is likely indicative of an attempted Denial of Service (DoS) attack.

Endpoints include:

·       45.15.145[.]92

·       198.2.200[.]89

·       162.211.180[.]215

Figure 3: Graphing function in the Darktrace UI showing the observed spike of inbound communication from external endpoints, indicating a potential DoS attack.

8. Reconnaissance and RDP activity

On May 31, the initially compromised device was seen creating an administrative RDP session with cookie ‘Administr’. Using the initially compromised administrative credential, further suspicious SMB activity was observed from the compromised devices on the same day including further SMB Enumeration, service control, PsExec remote command execution, and writes of another suspicious batch script file to various internal devices.

Darktrace RESPOND Coverage

Darktrace RESPOND’s autonomous response capabilities allowed it to take instantaneous preventative action against the affected devices as soon as suspicious activity was identified, consequently inhibiting the spread of this attack.

Specifically, Darktrace RESPOND was able to block suspicious connections to multiple internal devices and ports, among them port 445 which was used by threat actors to perform SMB scanning, for one hour. As a result of the autonomous actions carried out by Darktrace, the attack was stopped at the earliest possible stage.

Figure 4: Autonomous RESPOND actions taken against initially compromised devices.

In addition to these autonomous actions, the customer was able to further utilize RESPOND for containment purposes by manually actioning some of the more severe actions suggested by RESPOND, such as quarantining compromised devices from the rest of the network for a week.

Figure 5: Manually applied RESPOND actions to quarantine compromised devices for one week.

Conclusion

As attackers continue to employ harder to detect living off the land techniques to exploit administrative credentials and move laterally across networks, it is paramount for organizations to have an intelligent decision maker that can recgonize the subtle deviations in device behavior.

Thanks to its Self-Learning AI, Darktrace is uniquely placed to understand its customer’s networks, allowing it to recognize unusual or uncommon activity for individual devices or user credentials, irrespective of whether this activity is typically considered as legitimate.

In this case, Darktrace was the only solution in the customer’s security stack that successfully identified and mitigated this attack. Darktrace DETECT was able to identify the the early stages of the compromise and provide full visibility over the kill chain. Meanwhile, Darktrace RESPOND moved at machine-speed, blocking suspicious connections and preventing the compromise from spreading across the customer’s network.

Appendices

Darktrace DETECT Model Breaches

Anomalous Connection / High Volume of New or Uncommon Service Control

Anomalous Connection / New or Uncommon Service Control

Anomalous Connection / SMB Enumeration

Anomalous Connection / Unusual Admin RDP Session

Anomalous Connection / Unusual Admin SMB Session

Anomalous File / Internal / Executable Uploaded to DC

Anomalous File / Internal / Unusual SMB Script Write

Anomalous Server Activity / Outgoing from Server

Anomalous Server Activity / Possible Denial of Service Activity

Antigena / Network / Insider Threat / Antigena Network Scan Block

Antigena / Network / Insider Threat / Antigena SMB Enumeration Block

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Antigena / Network / External Threat / Antigena File then New Outbound Block

Compliance / Outgoing NTLM Request from DC

Compliance / SMB Drive Write

Device / Anomalous NTLM Brute Force

Device / ICMP Address Scan  

Device / Internet Facing Device with High Priority Alert

Device / Large Number of Model Breaches

Device / Large Number of Model Breaches from Critical Network Device

Device / Multiple Lateral Movement Model Breaches

Device / Network Scan

Device / New or Uncommon SMB Named Pipe

Device / New or Uncommon WMI Activity

Device / New or Unusual Remote Command Execution

Device / Possible SMB/NTLM Brute Force

Device / RDP Scan

Device / SMB Lateral Movement

Device / SMB Session Brute Force (Admin)

Device / Suspicious SMB Scanning Activity

Darktrace RESPOND Model Breaches

Antigena / Network / Insider Threat / Antigena Network Scan Block

Antigena / Network / Insider Threat / Antigena SMB Enumeration Block

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Antigena / Network / External Threat / Antigena File then New Outbound Block

Cyber AI Analyst Incidents

Extensive Suspicious Remote WMI Activity

Extensive Unusual Administrative Connections

Large Volume of SMB Login Failures from Multiple Devices

Port Scanning

Scanning of Multiple Devices

SMB Writes of Suspicious Files

Suspicious Chain of Administrative Connections

Suspicious DCE_RPC Activity

TCP Scanning of Multiple Devices

MITRE ATT&CK Mapping

RECONNAISSANCE
T1595 Active Scanning
T1589.001 Gathering Credentials

CREDENTIAL ACCESS
T1110 Brute Force

LATERAL MOVEMENT
T1210 Exploitation of Remote Services
T1021.001 Remote Desktop Protocol

COMMAND AND CONTROL
T1071 Application Layer Protocol

IMPACT
T1498.001 Direct Network Flood

References

[1] https://learn.microsoft.com/en-us/troubleshoot/windows-server/performance/windows-registry-advanced-users

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
Zoe Tilsiter
Cyber Analyst

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November 19, 2025

Securing Generative AI: Managing Risk in Amazon Bedrock with Darktrace / CLOUD

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Security risks and challenges of generative AI in the enterprise

Generative AI and managed foundation model platforms like Amazon Bedrock are transforming how organizations build and deploy intelligent applications. From chatbots to summarization tools, Bedrock enables rapid agent development by connecting foundation models to enterprise data and services. But with this flexibility comes a new set of security challenges, especially around visibility, access control, and unintended data exposure.

As organizations move quickly to operationalize generative AI, traditional security controls are struggling to keep up. Bedrock’s multi-layered architecture, spanning agents, models, guardrails, and underlying AWS services, creates new blind spots that standard posture management tools weren’t designed to handle. Visibility gaps make it difficult to know which datasets agents can access, or how model outputs might expose sensitive information. Meanwhile, developers often move faster than security teams can review IAM permissions or validate guardrails, leading to misconfigurations that expand risk. In shared-responsibility environments like AWS, this complexity can blur the lines of ownership, making it critical for security teams to have continuous, automated insight into how AI systems interact with enterprise data.

Darktrace / CLOUD provides comprehensive visibility and posture management for Bedrock environments, automatically detecting and proactively scanning agents and knowledge bases, helping teams secure their AI infrastructure without slowing down expansion and innovation.

A real-world scenario: When access goes too far

Consider a scenario where an organization deploys a Bedrock agent to help internal staff quickly answer business questions using company knowledge. The agent was connected to a knowledge base pointing at documents stored in Amazon S3 and given access to internal services via APIs.

To get the system running quickly, developers assigned the agent a broad execution role. This role granted access to multiple S3 buckets, including one containing sensitive customer records. The over-permissioning wasn’t malicious; it stemmed from the complexity of IAM policy creation and the difficulty of identifying which buckets held sensitive data.

The team assumed the agent would only use the intended documents. However, they did not fully consider how employees might interact with the agent or how it might act on the data it processed.  

When an employee asked a routine question about quarterly customer activity, the agent surfaced insights that included regulated data, revealing it to someone without the appropriate access.

This wasn’t a case of prompt injection or model manipulation. The agent simply followed instructions and used the resources it was allowed to access. The exposure was valid under IAM policy, but entirely unintended.

How Darktrace / CLOUD prevents these risks

Darktrace / CLOUD helps organizations avoid scenarios like unintended data exposure by providing layered visibility and intelligent analysis across Bedrock and SageMaker environments. Here’s how each capability works in practice:

Configuration-level visibility

Bedrock deployments often involve multiple components: agents, guardrails, and foundation models, each with its own configuration. Darktrace / CLOUD indexes these configurations so teams can:

  1. Inspect deployed agents and confirm they are connected only to approved data sources.
  2. Track evaluation job setups and their links to Amazon S3 datasets, uncovering hidden data flows that could expose sensitive information.
  3. Maintain full awareness of all AI components, reducing the chance of overlooked assets introducing risk.

By unifying configuration data across Bedrock, SageMaker, and other AWS services, Darktrace / CLOUD provides a single source of truth for AI asset visibility. Teams can instantly see how each component is configured and whether it aligns with corporate security policies. This eliminates guesswork, accelerates audits, and helps prevent misaligned settings from creating data exposure risks.

 Agents for bedrock relationship views.
Figure 1: Agents for bedrock relationship views

Architectural awareness

Complex AI environments can make it difficult to understand how components interact. Darktrace / CLOUD generates real-time architectural diagrams that:

  1. Visualize relationships between agents, models, and datasets.
  1. Highlight unintended data access paths or risk propagation across interconnected services.

This clarity helps security teams spot vulnerabilities before they lead to exposure. By surfacing these relationships dynamically, Darktrace / CLOUD enables proactive risk management, helping teams identify architectural drift, redundant data connections, or unmonitored agents before attackers or accidental misuse can exploit them. This reduces investigation time and strengthens compliance confidence across AI workloads.

Figure 2: Full Bedrock agent architecture including lambda and IAM permission mapping
Figure 2: Full Bedrock agent architecture including lambda and IAM permission mapping

Access & privilege analysis

IAM permissions apply to every AWS service, including Bedrock. When Bedrock agents assume IAM roles that were broadly defined for other workloads, they often inherit excessive privileges. Without strict least-privilege controls, the agent may have access to far more data and services than required, creating avoidable security exposure. Darktrace / CLOUD:

  1. Reviews execution roles and user permissions to identify excessive privileges.
  2. Flags anomalies that could enable privilege escalation or unauthorized API actions.

This ensures agents operate within the principle of least privilege, reducing attack surface. Beyond flagging risky roles, Darktrace / CLOUD continuously learns normal patterns of access to identify when permissions are abused or expanded in real time. Security teams gain context into why an action is anomalous and how it could affect connected assets, allowing them to take targeted remediation steps that preserve productivity while minimizing exposure.

Misconfiguration detection

Misconfigurations are a leading cause of cloud security incidents. Darktrace / CLOUD automatically detects:

  1. Publicly accessible S3 buckets that may contain sensitive training data.
  2. Missing guardrails in Bedrock deployments, which can allow inappropriate or sensitive outputs.
  3. Other issues such as lack of encryption, direct internet access, and root access to models.  

By surfacing these risks early, teams can remediate before they become exploitable. Darktrace / CLOUD turns what would otherwise be manual reviews into automated, continuous checks, reducing time to discovery and preventing small oversights from escalating into full-scale incidents. This automated assurance allows organizations to innovate confidently while keeping their AI systems compliant and secure by design.

Configuration data for Anthropic foundation model
Figure 3: Configuration data for Anthropic foundation model

Behavioral anomaly detection

Even with correct configurations, behavior can signal emerging threats. Using AWS CloudTrail, Darktrace / CLOUD:

  1. Monitors for unusual data access patterns, such as agents querying unexpected datasets.
  2. Detects anomalous training job invocations that could indicate attempts to pollute models.

This real-time behavioral insight helps organizations respond quickly to suspicious activity. Because it learns the “normal” behavior of each Bedrock component over time, Darktrace / CLOUD can detect subtle shifts that indicate emerging risks, before formal indicators of compromise appear. The result is faster detection, reduced investigation effort, and continuous assurance that AI-driven workloads behave as intended.

Conclusion

Generative AI introduces transformative capabilities but also complex risks that evolve alongside innovation. The flexibility of services like Amazon Bedrock enables new efficiencies and insights, yet even legitimate use can inadvertently expose sensitive data or bypass security controls. As organizations embrace AI at scale, the ability to monitor and secure these environments holistically, without slowing development, is becoming essential.

By combining deep configuration visibility, architectural insight, privilege and behavior analysis, and real-time threat detection, Darktrace gives security teams continuous assurance across AI tools like Bedrock and SageMaker. Organizations can innovate with confidence, knowing their AI systems are governed by adaptive, intelligent protection.

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About the author
Adam Stevens
Senior Director of Product, Cloud | Darktrace

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November 19, 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

The content provided in this blog is published by Darktrace for general informational purposes only and reflects our understanding of cybersecurity topics, trends, incidents, and developments at the time of publication. While we strive to ensure accuracy and relevance, the information is provided “as is” without any representations or warranties, express or implied. Darktrace makes no guarantees regarding the completeness, accuracy, reliability, or timeliness of any information presented and expressly disclaims all warranties.

Nothing in this blog constitutes legal, technical, or professional advice, and readers should consult qualified professionals before acting on any information contained herein. Any references to third-party organizations, technologies, threat actors, or incidents are for informational purposes only and do not imply affiliation, endorsement, or recommendation.

Darktrace, its affiliates, employees, or agents shall not be held liable for any loss, damage, or harm arising from the use of or reliance on the information in this blog.

The cybersecurity landscape evolves rapidly, and blog content may become outdated or superseded. We reserve the right to update, modify, or remove any content.

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About the author
Christina Kreza
Cyber Analyst
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