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April 7, 2020

Four Ways Cyber-Criminals Fly Under the Radar

Learn how cyber criminals evade detection. Darktrace analyses the four ways they operate under the radar. Read here to stay vigilant against cyber attacks.
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
Oliver Rochford
Technical Director
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07
Apr 2020

The challenge of reliably attributing cyber-threats has amplified in recent years, as adversaries adopt a collection of techniques to ensure that even if their attacks are caught, they themselves escape detection and avoid punishment.

Detecting a threat is, of course, a very different technical challenge compared to tracing that activity back to a human operator. Nevertheless, at some point after the dust has settled, during the post-hoc incident analysis for example, someone somewhere may need to know who the suspects are. And in spite of all of our other advances, and also some recent successes in attributing offensive and cyber-criminal acts, only three out of every 100,000 cyber-crimes are prosecuted. Put simply, this is still an unsolved set of problems. Many of the successes we do have can be attributed more to operational security fails on the criminals’ end than any other active approaches. In fact, some recent trends have actually made reliable attribution even more challenging.

The four cyber-threat trends that make attribution difficult

There are four related trends in how threat-actors can procure and obtain attack capabilities that have resulted in an increase in complexity when attempting to reliably identify Tools, Techniques, and Procedures (TTPs) and attributing them to distinct threat-actors.

A Cybercrime-as-a-Service economy and supply chain allowing cyber-criminals to mix and match off the shelf offensive cyber capabilities.

Expansion of ‘Living off the Land’ (LoL) tool usage by threat-actors to evade traditional, signature-based security defenses, and to obfuscate their activity.

While Code Reuse has always existed in the hacker community, copying nation-state-grade attack code has recently become possible.

The barrier to entry for criminally motivated operators has been lowered, providing the means for less technical criminals, who are only limited by time and their imagination.

Figure 1: The four cyber-threat trends

Threat-actors can mix and match attack tools, creating attack stacks that can be tailored for a variety of campaigns.

Between a professional marketplace of cyber-crime tools and services, the increasing adoption of ‘Living off the Land’ techniques, and the reusing of code leaked from nation-state intelligence services, threat-actors with even the most limited technical ability can conduct highly sophisticated criminal campaigns. Prospective cyber-criminals now have four primary types of attack tools to choose from – with three of them brand new or greatly enhanced. Even more importantly, these threat-actors can mix and match attack tools, creating tactically flexible attack stacks that can be tailored for a variety of campaigns against a diverse set of victims.

Off the shelf attacks

The burgeoning and increasingly professional Cybercrime-as-a-Service market (estimated at $1.6B) provides a thriving marketplace of microservices, attack code, and attack platforms. Anyone with a motive and enough bitcoin and enthusiasm can become the next ‘cyber Don Corleone’. Many of these services offer dedicated account management and professional support 24 hours a day. The commercialization of the cyber-crime supply chain has raised the barrier to entry for Cybercrime-as-a-Service vendors, while at the same time lowering it for cyber-criminal operators.

Living off the Land

‘Living off the Land’ (LoL) and “malware-less” attacks have been on the rise for some time now. What makes these attack methods so dangerous is that they leverage standard operating system tools to conduct their nefarious business, making signature-based approaches that look for malware heuristics ineffective – including signature-based Intrusion Protection Systems.

These attacks in particular demonstrate the need for an approach to cyber security that goes beyond looking at what malware is being used. Rather than relying on static blacklists, security teams are instead turning to a more sophisticated approach that learns ‘normal’ for every user and device across an entire business. From that evolving baseline, this approach to defense can identify and contain anomalous activity indicative of a cyber-threat – all in real time.

Code reuse and repurpose

What is new, and unprecedented, is that cyber-criminals are gaining access to intelligence and nation-state grade attack code.

Hackers have always begged, borrowed, and stolen code from others, including attack code – just two notable examples include the Zeus trojan and RIG exploit kit code leaks that provided the code base for much of the current generation of threats. What is new and unprecedented is that, whether through malice or incompetence, cyber-criminals are gaining access to intelligence and nation-state grade attack code. The Shadowbroker leaks that resulted in Wannacry is one recent example of this trend, and one we expect to accelerate – especially with intelligence services actively outing each other’s methods.

Custom and bespoke techniques

The practice of hackers creating their own tools and researching their own exploits has a long and hallowed tradition, with headline-grabbing zero-days becoming more and more common. Nation-state actors in particular often make a distinction between attack operators and attack code developers, with the ability to request tailored and bespoke code and tools – not unlike the model that has been replicated in the Cybercrime-as-a-Service market. Even when developing custom tools, threat-actors frequently integrate code and exploits from other parties.

Figure 2: The four main attack tool types

When determining who is actually behind these attacks, though, what is most important is the ability to combine all four types of attack tools – this provides a further layer of obfuscation against methods that rely on pattern matching for detection whilst causing additional confusion for would-be investigators. An attacker can use any combination and variation of these tool types to create a different “Chimera” attack stack – making it that much more difficult to identify who is really the operator. Telling apart the operator from the Cybercrime-as-a-Service vendor, for example, is difficult when most of the TTPs that are evaluated are technical and derive from the tooling.

Figure 3: The TTP and Attribution Confusion Chain

Conclusion

As the challenge of attribution intensifies, our focus must turn to defending against cyber-attacks themselves.

The combination of the four threat trends outlined above has lowered the barrier to entry for criminally motivated operators. Less technical adversaries are now able to launch attacks at a speed and scale previously confined to the most organized and well-financed cyber-criminal rings. This change in circumstances has made attribution of offensive cyber activity drastically more complex, and it may be some time before the prosecution rate for cyber-crime gets good enough that it can act as a greater disincentive.

As the challenge of attribution intensifies, our focus must turn to defending against cyber-attacks themselves. You may not ever know who is attacking you, but if you can successfully thwart the full range of threats, new and old, your organization can continue to operate as normal.

Fortunately, defenders’ abilities to detect and respond to cyber-threats have significantly advanced in recent years, thanks to the latest developments in AI and machine learning. Over 3,500 organizations now rely on Cyber AI to detect and contain cyber-threats – whether attackers use pre-existing OS tools to masquerade their attacks or use bespoke and entirely new techniques to bypass rules and signatures. When a threat is identified, AI can respond autonomously by enforcing a user or device’s ‘pattern of life’, allowing ‘business as usual’ whilst ensuring the organization is protected from harm.

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
Oliver Rochford
Technical Director

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September 2, 2026

Botnet Behind the Camera: Mirai Katana Activity on a Video Recording Device

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Key takeaways

  • Darktrace identified a camera device infected with the Mirai/Katana botnet in a sports-sector customer environment, showing how exposed IoT devices can become active participants in wider attack chains.
  • The compromise involved suspicious Wget behavior, file downloads from rare external IPs, unusual incoming HTTP connections to video recorder management interfaces, and large outbound data transfers to infrastructure associated with botnet activity.
  • The incident highlights the importance of extending visibility and response beyond traditional endpoints, as unmanaged or overlooked connected devices can be exploited for command-and-control, malware delivery, and data exfiltration.

Mirai and the Katana variant

Mirai is a botnet that first emerged in August 2016 and is well known for launching large-scale distributed-denial-of-service (DDoS) attacks, typically targeting exposed Internet of Things (IoT) devices. It identifies vulnerable IoT devices ,often by abusing default credentials or exposed services, and recruiting them into a remotely controlled botnet that can be used in DDoS campaigns [1].

Katana, one of the many variants that arose after Mirai’s source code was released publicly, was first observed in late 2020 and has been seen using more advanced capabilities, including custom command-and-control (C2), persistence mechanisms, and DDoS functionality [2].

In March 2026, research from the Nokia Deepfield Emergency Response Team (ERT) identified Katana as a Mirai-derived DDoS botnet targeting Android-based TV set-top boxes through exposed Android Debug Bridge (ADB) access.  Observed capabilities included custom C2, runtime domain rotation, multiple DDoS methods, and an on-device compiled kernel rootkit used for persistence and stealth [3].

Darktrace’s detection of Mirai Botnet activity on a camera device

In early 2026, Darktrace identified a Network/Digital Video Recorder (NVR/DVR) on the network of a sports-sector customer that had been infected with the Mirai Katana botnet and subsequently used to exfiltrate data from the customer’s environment. Seemingly related follow-up activity was observed on the same device several months later.

In both instances, the Darktrace Security Operations Centre (SOC) alerted the customer as part of the Managed Threat Detection (MTD) service. However, as Darktrace’s Autonomous Response capability was not fully enabled on the affected device, Darktrace was unable to proactively block the suspicious activity or prevent the compromise from continuing and recurring.

The initial compromise appears to have occurred when the affected device was seen using Wget to download Linux-based Executable and Linkable Format (ELF) files from a rare external IP, 195.177.94[.]105, which had not previously been observed in the customer’s network. Further analysis downloaded file hashes identified files related to the Mirai botnet.

Figure 1: Darktrace’s Real-Time AI Analyst investigation into the unusual outbound connection where the ELF files were downloaded.

Within a few hours, Darktrace detected the device uploading close to 3GB of data to another external IP, 50.7.49[.]4:3017 (ASN AS30058 FDCSERVERS), suggesting that the activity was likely routed via a virtual private server (VPS) hosted by FDC Servers [2]. Attackers often abuse VPS infrastructure from legitimate cloud providers to blend in with legitimate traffic and evade IP reputation and geolocation-based detections.

Figure 2:  Darktrace’s detection of the unusual data upload activity by the affected camera device.

Darktrace continued to observe similar data transfers to multiple rare endpoints  including 171.225.223[.]53, 95.161.128[.]62, 61.7.209[.]88, 95.161.128[.]62, which have been linked to Mirai by open-source intelligence (OSINT).

Figure 3: Darktrace’s detection of spikes in unusual external data transfer activity from the camera device.

Exploitation continued

Several months later, Darktrace identified the same exfiltration pattern on the device again, this time with stronger indications of associations with Mirai Katana botnet infection.

The device received incoming HTTP connections from 129.121.114[.]124, an external IP known to be associated with the Katana botnet IP [3]. The connections targeted the ‘/dvr/cmd’ path using the root username and user agent Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.246.

The ‘/dvr/cmd’ path appears to be associated with the affected device’s web management functionality. This API endpoint has historically been targeted by Mirai and other IoT botnets through the exploitation of critical command injection vulnerabilities and automated botnet exploitation [4].

Figure 4: Darktrace’s  detection of HTTP connectivity from the external IP associated with Mirai Katana Botnet.

A few days later, Darktrace observed the Wget utility being used to download ELF files, including “/lil”,  from the IP 129.121.114[.]124. OSINT reporting has since associated this IP address with the Mirai Katana botnet. Notably, the IP observed earlier in the year, 195.177.94[.]105, had also hosted a file named “lil”, indicating a link between the observed activity.

Over the following days, the device received a sudden spike in connections from multiple rare external endpoints, suggesting a possible successful brute force attack. Darktrace also observed the device exfiltrating just under 4GB of data to another Mirai-associated IP address,  66.92.198[.]194, over ports 3344, 954922, and 80. Finally, the device was seen uploading data to the Mirai botnet IP 5.175.249[.]53 over port138 and exhibited an increase in UDP connections to 34.18.28[.]10 over port 9068.

Following both file download events, Darktrace identified spikes in external data transfers and connection attempts to rare destinations. While Darktrace’s Threat Research team could not confirm with high confidence that this to activity was directly associated with Mirai, it may indicate that Mirai Katana includes data exfiltration functionality.

Darktrace’s threat researchers also identified an internet-facing NTP server belonging to a separate customer receiving incoming connection attempts from the same initially observed IP, 195.177.94[.]105,over the port 123. This suggests that Mirai Katana may not exclusively target IoT devices.

Conclusion

This case demonstrates how threat actors can exploit overlooked IoT and OT devices to support broader malicious objectives. Here, a camera device infected with a botnet was used to exfiltrate data from the customer's environment, showing how peripheral assets can become active participants in an attack chain.

This case also reinforces a challenge many organizations face today: extending security visibility beyond traditional endpoints and servers. Cameras, sensors, and other connected devices often operate with limited monitoring and may fall outside established security processes, despite maintaining network connectivity and access to potentially sensitive environments. This is particularly relevant in the sports sector, where growing reliance on connected cameras, smart stadium technologies, and other IoT devices continues to expand the attack surface, as highlighted in Darktrace's Sports Sector Threat Report.

As botnets like Kata and Mirai continue to evolve, defenders need visibility across unmanaged IoT and edge devices, as well as security solutions that can recognize subtle deviations in device behavior that may indicate an emerging compromise.

Credit to Parvatha Ananthakannan (Cyber Analyst), Signe Zaharka (Principal Analyst)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External Locations

·      Device / Initial Attack Chain Activity

·      Unusual Activity / Unusual External Data to New Endpoint

·      Anomalous Connection / Data Sent to Rare Domain

·      Unusual Activity / Enhanced Unusual External Data Transfer

·      Anomalous Connection / Uncommon 1 GiB Outbound

·      Device / Significant UDP Increase

·      Anomalous Connection / Low and Slow Exfiltration to IP

·      Compromise / Large Number of Suspicious Failed Connections

·      Compromise / Large Number of Suspicious Successful Connections

·      Unusual Activity / Unusual External Activity

·      Compliance / SSH to Rare External Destination

·      Unusual Activity / Unusual DNS

·      Device / External Network Scan

·      Device / Suspicious DNS Activity

·      Device / Large Number of Model Alerts

List of Indicators of Compromise (IoCs)

Indicator of Compromise Type Description
195.177.94[.]105 IP C2 endpoint
50.7.49[.]4:30171 IP Possible C2 endpoint
129.121.114[.]124 IP C2 endpoint
hxxp://195.177.94[.]105/n3 URL Likely C2 endpoint
hxxp://195.177.94[.]105/n2 URL Likely C2 endpoint
hxxp://129.121.114[.]124/lil URL Likely C2 endpoint
hxxp://129.121.114[.]124/HHn URL Possible C2 endpoint
hxxp://129.121.114[.]124/JFc URL Possible C2 endpoint
hxxp://129.121.114[.]124/jum URL Likely C2 endpoint
hxxp://129.121.114[.]124/OaSf URL Likely C2 endpoint
hxxp://129.121.114[.]124/OPWg URL Possible C2 endpoint
hxxp://129.121.114[.]124/vHwK URL Possible C2 endpoint
hxxp://129.121.114[.]124/VLv URL Possible C2 endpoint
hxxp://129.121.114[.]124/WbJ URL Possible C2 endpoint
hxxp://129.121.114[.]124/zkR URL Possible C2 endpoint
Ab17883ae4c3bc6afa18c439166eeeb4b03186e3093d984e3a95f573e0fcb7d8 SHA-256 Mirai payload
3d587e809dac49d34a3f717e072fd0aebe5e71db63333e45c81577d6b4266f87 SHA-256 Mirai payload
Bf6e81733a7e209d3dce80d15bf3c5d300752d961fae6b45d90c9bbe7f8c89a2 SHA-256 Possible payload
f25488303813ab1ec0eaa71562938601aac185e8aaf93adb84522557f7cf4dd6 SHA-256 Possible payload
0cb4ff6b71f4423184bfa35c34e9090297637208b0e30205d4b224e56abde2ef SHA-256 Possible payload
19c24cbeaf06b2e7697083f33a85521a9315105c784691bde7420fde4cc69410 SHA-256 Likely Mirai payload
1e74f734fff8df91f4f7172d0de10c421eca78aeb800e8a48e16bc5dbde5d20e SHA-256 Possible payload
6e71f7763d1f29d5712106ebb122e281c32787540aa2342b0fe5351d585d18d7 SHA-256 Possible payload
71f4ff7cdb6d6a7d2673c543c5d2535093afbd707b20a5b9ddf735466c1105c1 SHA-256 Possible payload
76db7ee73ebf15e48a3cb24a074d92248671ef2c6ed3bc3e708377341fb7674d SHA-256 Possible payload
da87a65f7beb438e61f0b61964fed8aa305a380f569042f84c55eca8fa7929b8 SHA-256 Possible payload
e15809eb6ba66477175270d62cfa53e4bf278595f69938708c81c4bc457930fe SHA-256 Mirai payload

MITRE ATT&CK Mapping

Tactic Technique ID Technique / Sub-technique
Initial Access T1659 Content Injection
T1189 Drive-by Compromise
Exfiltration T1041 Exfiltration Over C2 Channel
T1048.003 Exfiltration Over Unencrypted Non-C2 Protocol
Command and Control T1105 Ingress Tool Transfer
T1095 Non-Application Layer Protocol
T1571 Non-Standard Port
Reconnaissance T1595.001 Scanning IP Blocks
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About the author
Parvatha Ananthakannan
Cyber Analyst

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August 26, 2026

AI Agents: Securing the Path from Intent to Action

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The UK’s National Cyber Security Centre (NCSC) recently published guidance on managing the cyber risk of agentic AI. While the document is framed as interim advice as more formal guidance is developed, the framing reflects the current state of the industry: organizations are already deploying agents into production environments while standards, controls, and operating models for autonomous systems remain unsettled. Governance is evolving alongside adoption rather than preceding it, a reality which underscores the importance of robust controls.  

The NCSC’s guidance recommends aligning controls to an agent's level of autonomy, assigning distinct identities, limiting permissions, constraining access to systems and data, monitoring activity, maintaining human oversight, and preserving the ability to intervene when necessary. Most of these recommendations will sound familiar to security teams. The challenge is not the novelty of the controls. It is the type of system those controls now need to govern.

The shift from model security to agent security

For several years, AI security discussions have focused heavily on models. Can a model be manipulated? Jailbroken? Trusted? Can it expose information it should not? Those questions remain important, but they capture only part of the problem. A model generating text is one thing. A system connected to identities, applications, tools, workflows, and business data is another.

The difference becomes clearer when comparing a chatbot that answers questions with an agent that can retrieve customer records, update tickets, invoke tools, trigger workflows, and interact with external systems. The underlying model may be identical. Its access is not. The security question begins to shift from what the model knows to what the system can do.

The same theme appears in the Five Eyes statement released earlier this year, describing AI as a force multiplier that is accelerating both offensive and defensive cyber operations. The NCSC guidance explores what that reality looks like when autonomous systems begin operating inside enterprise environments.

Securing AI agents in operation

The NCSC spends relatively little time debating model behavior and considerably more time discussing identity, permissions, monitoring, oversight, containment, and response. Agents are treated as participants within an environment rather than isolated pieces of technology.  

That's broadly consistent with how we think about the problem at Darktrace.

An agent should not be treated as an extension of a user account. It develops its own behavioral patterns. It accesses systems, interacts with data, invokes tools, and moves across workflows in ways that can be observed independently. Understanding what an agent is permitted to do matters. Understanding how it actually behaves once deployed, and whether that behavior aligns with business intent, matters just as much.

Identity provides an obvious example. The NCSC recommends assigning distinct identities to agents rather than allowing them to disappear into surrounding human or service accounts. Most importantly, assigning agents distinct identities enables independent behavioral monitoring.

Development assumptions vs. real-world behavior

The same principle extends to monitoring. NCSC guidance places agent activity within normal security operations rather than treating it as a separate AI governance function. Many of the controls described are put in place before an agent begins operating. Sandboxing, credential design, approval workflows and human oversight all reflect judgments about how the system is expected to behave and what risks it is likely to create.

Actual use may challenge those assumptions. Access patterns change. Workflows expand. Systems begin interacting with resources they have never touched before. Processes that appeared reasonable during design behave differently in production. Human oversight requirements may turn out to be either excessive or inadequate once the system is operating at scale and operating within the context of unique business processes.

The Five Eyes statement points to a similar issue: organizations need confidence that controls continue to work as intended once systems are exposed to real users, data, tools and operational pressures. Often, the question is not whether an agent is technically allowed to perform an action, but whether its behavior remains consistent with the role it was intended to play.

Monitoring and governance of AI agents go hand-in-hand

This problem is exactly why monitoring and governance should be treated as part of the same process. Governance sets the initial parameters for deployment, while monitoring provides evidence about whether those parameters remain appropriate. That evidence should, in turn, inform changes to permissions, controls and oversight.

This matters increasingly as autonomous systems are integrated into business processes. The relevant risk is shaped not only by the model or agent itself, but by what it can access, what actions it can take, and how its behavior changes in practice.

Developing continuous oversight of AI agent behavior

The implication is clear: governance cannot end at deployment. Organizations need a way to understand how agents behave after deployment, test whether controls remain appropriate, and adjust them as conditions change. That requires visibility not just into technical activity, but into whether that activity makes sense in the context of the business process the agent is intended to support.

This is where business-centric behavioral security can become critical. Risk does not emerge from the model itself: it emerges from the actions an autonomous system takes within the enterprise and the downstream consequences of those actions.  

An agent can operate exactly as intended and still create risk if it accesses sensitive information in an unexpected context, exercises permissions in ways that create unintended exposure, or influences business processes in ways that were not anticipated during design and review.

Traditional governance vs. behavioral security

Traditional governance frameworks provide assurance at a point in time. Behavioral security can provide ongoing visibility into how autonomous systems interact with the organization they are meant to serve. Rather than focusing exclusively on model performance or policy compliance, organizations need to understand whether an agent's behavior aligns with business intent, operational expectations, and acceptable risk tolerances as conditions change.

As enterprises move from isolated AI deployments to interconnected ecosystems of agents, visibility into behavior becomes as important as visibility into code. Governance determines what an autonomous system is permitted to do. Behavioral analytics helps determine what it is doing, what business outcomes it is producing, and whether those outcomes remain aligned with the organization's objectives.

[related-resource]

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About the author
Margaret Cunningham, PhD
VP, Security & AI Strategy, Field CISO
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