September 3, 2025

From PowerShell to Payload: Darktrace’s Detection of a Novel Cryptomining Malware

Cryptojacking attacks are rising as threat actors exploit hard-to-detect cryptomining malware. Learn how Darktrace detected and contained a cryptojacking attempt in its early stages using Autonomous Response, with expert analysis of the malware itself revealing insights into a novel cryptomining strain.

No items found.

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.

No items found.

Sep 2025

What is Cryptojacking?

Cryptojacking remains one of the most persistent cyber threats in the digital age, showing no signs of slowing down. It involves the unauthorized use of a computer or device’s processing power to mine cryptocurrencies, often without the owner’s consent or knowledge, using cryptojacking scripts or cryptocurrency mining (cryptomining) malware [1].

Unlike other widespread attacks such as ransomware, which disrupt operations and block access to data, cryptomining malware steals and drains computing and energy resources for mining to reduce attacker’s personal costs and increase “profits” earned from mining [1]. The impact on targeted organizations can be significant, ranging from data privacy concerns and reduced productivity to higher energy bills.

As cryptocurrency continues to grow in popularity, as seen with the ongoing high valuation of the global cryptocurrency market capitalization (almost USD 4 trillion at time of writing), threat actors will continue to view cryptomining as a profitable venture [2]. As a result, illicit cryptominers are being used to steal processing power via supply chain attacks or browser injections, as seen in a recent cryptojacking campaign using JavaScript [3][4].

Therefore, security teams should maintain awareness of this ongoing threat, as what is often dismissed as a "compliance issue" can escalate into more severe compromises and lead to prolonged exposure of critical resources.

While having a security team capable of detecting and analyzing hijacking attempts is essential, emerging threats in today’s landscape often demand more than manual intervention.

This blog will discuss Darktrace’s successful detection of the malicious activity, the role of Autonomous Response in halting the cryptojacking attack, include novel insights from Darktrace’s threat researchers on the cryptominer payload, showing how the attack chain was initiated through the execution of a PowerShell-based payload.

Darktrace’s Coverage of Cryptojacking via PowerShell

In July 2025, Darktrace detected and contained an attempted cryptojacking incident on the network of a customer in the retail and e-commerce industry.

The threat was detected when a threat actor attempted to use a PowerShell script to download and run NBMiner directly in memory.

The initial compromise was detected on July 22, when Darktrace / NETWORK observed the use of a new PowerShell user agent during a connection to an external endpoint, indicating an attempt at remote code execution.

Specifically, the targeted desktop device established a connection to the rare endpoint, 45.141.87[.]195, over destination port 8000 using HTTP as the application-layer protocol. Within this connection, Darktrace observed the presence of a PowerShell script in the URI, specifically ‘/infect.ps1’.

Darktrace’s analysis of this endpoint (45.141.87[.]195[:]8000/infect.ps1) and the payload it downloaded indicated it was a dropper used to deliver an obfuscated AutoIt loader. This attribution was further supported by open-source intelligence (OSINT) reporting [5]. The loader likely then injected NBMiner into a legitimate process on the customer’s environment – the first documented case of NBMiner being dropped in this way.

Figure 1: Darktrace’s detection of a device making an HTTP connection with new PowerShell user agent, indicating PowerShell abuse for command-and-control (C2) communications.

Script files are often used by malicious actors for malware distribution. In cryptojacking attacks specifically, scripts are used to download and install cryptomining software, which then attempts to connect to cryptomining pools to begin mining operations [6].

Inside the payload: Technical analysis of the malicious script and cryptomining loader

To confidently establish that the malicious script file dropped an AutoIt loader used to deliver the NBMiner cryptominer, Darktrace’s threat researchers reverse engineered the payload. Analysis of the file ‘infect.ps1’ revealed further insights, ultimately linking it to the execution of a cryptominer loader.

Figure 2: Screenshot of the ‘infect.ps1’ PowerShell script observed in the attack.

The ‘infect.ps1’ script is a heavily obfuscated PowerShell script that contains multiple variables of Base64 and XOR encoded data. The first data blob is XOR’d with a value of 97, after decoding, the data is a binary and stored in APPDATA/local/knzbsrgw.exe. The binary is AutoIT.exe, the legitimate executable of the AutoIt programming language. The script also performs a check for the existence of the registry key HKCU:\\Software\LordNet.

The second data blob ($cylcejlrqbgejqryxpck) is written to APPDATA\rauuq, where it will later be read and XOR decoded. The third data blob ($tlswqbblxmmr)decodes to an obfuscated AutoIt script, which is written to %LOCALAPPDATA%\qmsxehehhnnwioojlyegmdssiswak. To ensure persistence, a shortcut file named xxyntxsmitwgruxuwqzypomkhxhml.lnk is created to run at startup.

Figure 3: Screenshot of second stage AutoIt script.

The observed AutoIt script is a process injection loader. It reads an encrypted binary from /rauuq in APPDATA, then XOR-decodes every byte with the key 47 to reconstruct the payload in memory. Next, it silently launches the legitimate Windows app ‘charmap.exe’ (Character Map) and obtains a handle with full access. It allocates executable and writable memory inside that process, writes the decrypted payload into the allocated region, and starts a new thread at that address. Finally, it closes the thread and process handles.

The binary that is injected into charmap.exe is 64-bit Windows binary. On launch, it takes a snapshot of running processes and specifically checks whether Task Manager is open. If Task Manager is detected, the binary kills sigverif.exe; otherwise, it proceeds. Once the condition is met, NBMiner is retrieved from a Chimera URL (https://api[.]chimera-hosting[.]zip/frfnhis/zdpaGgLMav/nbminer[.]exe) and establishes persistence, ensuring that the process automatically restarts if terminated. When mining begins, it spawns a process with the arguments ‘-a kawpow -o asia.ravenminer.com:3838 -u R9KVhfjiqSuSVcpYw5G8VDayPkjSipbiMb.worker -i 60’ and hides the process window to evade detection.

The program includes several evasion measures. It performs anti-sandboxing by sleeping to delay analysis and terminates sigverif.exe (File Signature Verification). It checks for installed antivirus products and continues only when Windows Defender is the sole protection. It also verifies whether the current user has administrative rights. If not, it attempts a User Account Control (UAC) bypass via Fodhelper to silently elevate and execute its payload without prompting the user. The binary creates a folder under %APPDATA%, drops rtworkq.dll extracted from its own embedded data, and copies ‘mfpmp.exe’ from System32 into that directory to side-load ‘rtworkq.dll’. It also looks for the registry key HKCU\Software\kap, creating it if it does not exist, and reads or sets a registry value it expects there.

Zooming Out: Darktrace Coverage of NBMiner

Darktrace’s analysis of the malicious PowerShell script provides clear evidence that the payload downloaded and executed the NBMiner cryptominer. Once executed, the infected device is expected to attempt connections to cryptomining endpoints (mining pools). Darktrace initially observed this on the targeted device once it started making DNS requests for a cryptominer endpoint, “gulf[.]moneroocean[.]stream” [7], one minute after the connection involving the malicious script.

Figure 5: Darktrace Advanced Search logs showcasing the affected device making a DNS request for a Monero mining endpoint.

Though DNS requests do not necessarily mean the device connected to a cryptominer-associated endpoint, Darktrace detected connections to the endpoint specified in the DNS Answer field: monerooceans[.]stream, 152.53.121[.]6. The attempted connections to this endpoint over port 10001 triggered several high-fidelity model alerts in Darktrace related to possible cryptomining mining activity. The IP address and destination port combination (152.53.121[.]6:10001) has also been linked to cryptomining activity by several OSINT security vendors [8][9].

Figure 6: Darktrace’s detection of a device establishing connections with the Monero Mining-associated endpoint, monerooceans[.]stream over port 10001.

Darktrace / NETWORK grouped together the observed indicators of compromise (IoCs) on the targeted device and triggered an additional Enhanced Monitoring model designed to identify activity indicative of the early stages of an attack. These high-fidelity models are continuously monitored and triaged by Darktrace’s SOC team as part of the Managed Threat Detection service, ensuring that subscribed customers are promptly notified of malicious activity as soon as it emerges.

Figure 7: Darktrace’s correlation of the initial PowerShell-related activity with the cryptomining endpoint, showcasing a pattern indicative of an initial attack chain.

Darktrace’s Cyber AI Analyst launched an autonomous investigation into the ongoing activity and was able to link the individual events of the attack, encompassing the initial connections involving the PowerShell script to the ultimate connections to the cryptomining endpoint, likely representing cryptomining activity. Rather than viewing these seemingly separate events in isolation, Cyber AI Analyst was able to see the bigger picture, providing comprehensive visibility over the attack.

Figure 8: Darktrace’s Cyber AI Analyst view illustrating the extent of the cryptojacking attack mapped against the Cyber Kill Chain.

Darktrace’s Autonomous Response

Fortunately, as this customer had Darktrace configured in Autonomous Response mode, Darktrace was able to take immediate action by preventing the device from making outbound connections and blocking specific connections to suspicious endpoints, thereby containing the attack.

Specifically, these Autonomous Response actions prevented the outgoing communication within seconds of the device attempting to connect to the rare endpoints.

Figure 10: Darktrace’s Autonomous Response blocked connections to the mining-related endpoint within a second of the initial connection.

Additionally, the Darktrace SOC team was able to validate the effectiveness of the Autonomous Response actions by analyzing connections to 152.53.121[.]6 using the Advanced Search feature. Across more than 130 connection attempts, Darktrace’s SOC confirmed that all were aborted, meaning no connections were successfully established.

Figure 11: Advanced Search logs showing all attempted connections that were successfully prevented by Darktrace’s Autonomous Response capability.

Conclusion

Cryptojacking attacks will remain prevalent, as threat actors can scale their attacks to infect multiple devices and networks. What’s more, cryptomining incidents can often be difficult to detect and are even overlooked as low-severity compliance events, potentially leading to data privacy issues and significant energy bills caused by misused processing power.

Darktrace’s anomaly-based approach to threat detection identifies early indicators of targeted attacks without relying on prior knowledge or IoCs. By continuously learning each device’s unique pattern of life, Darktrace can detect subtle deviations that may signal a compromise.

In this case, the cryptojacking attack was quickly identified and mitigated during the early stages of malware and cryptomining activity. Darktrace's Autonomous Response was able to swiftly contain the threat before it could advance further along the attack lifecycle, minimizing disruption and preventing the attack from potentially escalating into a more severe compromise.

Credit to Keanna Grelicha (Cyber Analyst) and Tara Gould (Threat Research Lead)

Appendices

Darktrace Model Detections

NETWORK Models:

· Compromise / High Priority Crypto Currency Mining (Enhanced Monitoring Model)

· Device / Initial Attack Chain Activity (Enhanced Monitoring Model)

· Compromise / Suspicious HTTP and Anomalous Activity (Enhanced Monitoring Model)

· Compromise / Monero Mining

· Anomalous File / Script from Rare External Location

· Device / New PowerShell User Agent

· Anomalous Connection / New User Agent to IP Without Hostname

· Anomalous Connection / Powershell to Rare External

· Device / Suspicious Domain

Cyber AI Analyst Incident Events:

· Detect \ Event \ Possible HTTP Command and Control

· Detect \ Event \ Cryptocurrency Mining Activity

Autonomous Response Models:

· Antigena / Network::Significant Anomaly::Antigena Alerts Over Time Block

· Antigena / Network::External Threat::Antigena Suspicious Activity Block

· Antigena / Network::Significant Anomaly::Antigena Enhanced Monitoring from Client Block

· Antigena / Network::External Threat::Antigena Crypto Currency Mining Block

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

· Antigena / Network::External Threat::Antigena Suspicious File Block

· Antigena / Network::Significant Anomaly::Antigena Significant Anomaly from Client Block

List of Indicators of Compromise (IoCs)

(IoC - Type - Description + Confidence)

· 45.141.87[.]195:8000/infect.ps1 - IP Address, Destination Port, Script - Malicious PowerShell script

· gulf.moneroocean[.]stream - Hostname - Monero Endpoint

· monerooceans[.]stream - Hostname - Monero Endpoint

· 152.53.121[.]6:10001 - IP Address, Destination Port - Monero Endpoint

· 152.53.121[.]6 - IP Address – Monero Endpoint

· https://api[.]chimera-hosting[.]zip/frfnhis/zdpaGgLMav/nbminer[.]exe – Hostname, Executable File – NBMiner

· Db3534826b4f4dfd9f4a0de78e225ebb – Hash – NBMiner loader

MITRE ATT&CK Mapping

(Tactic – Technique – Sub-Technique)

· Vulnerabilities – RESOURCE DEVELOPMENT – T1588.006 - T1588

· Exploits – RESOURCE DEVELOPMENT – T1588.005 - T1588

· Malware – RESOURCE DEVELOPMENT – T1588.001 - T1588

· Drive-by Compromise – INITIAL ACCESS – T1189

· PowerShell – EXECUTION – T1059.001 - T1059

· Exploitation of Remote Services – LATERAL MOVEMENT – T1210

· Web Protocols – COMMAND AND CONTROL – T1071.001 - T1071

· Application Layer Protocol – COMMAND AND CONTROL – T1071

· Resource Hijacking – IMPACT – T1496

· Obfuscated Files - DEFENSE EVASION - T1027

· Bypass UAC - PRIVILEGE ESCALATION – T1548.002

· Process Injection – PRIVILEGE ESCALATION – T055

· Debugger Evasion – DISCOVERY – T1622

· Logon Autostart Execution – PERSISTENCE – T1547.009

References

[1] https://www.darktrace.com/cyber-ai-glossary/cryptojacking#:~:text=Battery%20drain%20and%20overheating,fee%20to%20%E2%80%9Cmine%20cryptocurrency%E2%80%9D.

[2] https://coinmarketcap.com/

[3] https://www.ibm.com/think/topics/cryptojacking

[4] https://thehackernews.com/2025/07/3500-websites-hijacked-to-secretly-mine.html

[5] https://urlhaus.abuse.ch/url/3589032/

[6] https://www.logpoint.com/en/blog/uncovering-illegitimate-crypto-mining-activity/

[7] https://www.virustotal.com/gui/domain/gulf.moneroocean.stream/detection

[8] https://www.virustotal.com/gui/domain/monerooceans.stream/detection

[9] https://any.run/report/5aa8cd5f8e099bbb15bc63be52a3983b7dd57bb92566feb1a266a65ab5da34dd/351eca83-ef32-4037-a02f-ac85a165d74e

^{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 without notice.}

No items found.

Inside the SOC

No items found.

Parvatha Ananthakannan

Cyber Analyst

Watch the NIS2 Webinar

Blog

OT

September 3, 2026

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

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
Initial Access	T1189	Drive-by Compromise
Exfiltration	T1041	Exfiltration Over C2 Channel
Exfiltration	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

About the author

Parvatha Ananthakannan

Cyber Analyst

Blog

August 26, 2026

AI Agents: Securing the Path from Intent to Action

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]

About the author

Margaret Cunningham, PhD

VP, Security & AI Strategy, Field CISO

Your data. Our AI.

Elevate your network security with Darktrace AI

Get a demo

Check out this article by Darktrace: How Darktrace Detects Cryptomining: Threat Example & Walkthrough

From PowerShell to Payload: Darktrace’s Detection of a Novel Cryptomining Malware

What is Cryptojacking?

Darktrace’s Coverage of Cryptojacking via PowerShell

Inside the payload: Technical analysis of the malicious script and cryptomining loader

Zooming Out: Darktrace Coverage of NBMiner

Darktrace’s Autonomous Response

Conclusion

Appendices

List of Indicators of Compromise (IoCs)

MITRE ATT&CK Mapping

References

When AI Becomes the Lure: A Fake Gemini Installer Delivers Vidar

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

Enjoying the blog?

More in this series

When AI Agents Attack: The Case for Behavioral Anomaly Detection

Hiding in Plain Sight: Uncovering a Multi-Stage Ransomware Attack Through Behavioral Detection

9 Stages of Ransomware & How AI Responds

Blog

OT

September 3, 2026

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

Key takeaways

Mirai and the Katana variant

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

Exploitation continued

Conclusion

Appendices

List of Indicators of Compromise (IoCs)

MITRE ATT&CK Mapping

Blog

August 26, 2026

AI Agents: Securing the Path from Intent to Action

The shift from model security to agent security

Securing AI agents in operation

Development assumptions vs. real-world behavior

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

Developing continuous oversight of AI agent behavior

Traditional governance vs. behavioral security