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April 12, 2023

P2Pinfect - New Variant Targets MIPS Devices

A new P2Pinfect variant compiled for the Microprocessor without Interlocked Pipelined Stages (MIPS) architecture has been discovered. This demonstrates increased targeting of routers, Internet of Things (IoT) and other embedded devices by those behind P2Pinfect.
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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.
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P2PinfectDefault blog image
12
Apr 2023

Introduction: P2PInfect

Since July 2023, researchers at Cado Security Labs (now part of Darktrace) have been monitoring and reporting on the rapid growth of a cross-platform botnet, named “P2Pinfect”. As the name suggests, the malware - written in Rust - acts as a botnet agent, connecting infected hosts in a peer-to-peer topology. In early samples, the malware exploited Redis for initial access - a relatively common technique in cloud environments. 

There are a number of methods for exploiting Redis servers, several of which appear to be utilized by P2Pinfect. These include exploitation of CVE-2022-0543[1] - a sandbox escape vulnerability in the LUA scripting language (reported by Unit42 [2]), and, as reported previously by Cado Security Labs, an unauthorized replication attack resulting in the loading of a malicious Redis module.  

Researchers have since encountered a new variant of the malware, specifically targeting embedded devices based on 32-bit MIPS processors, and attempting to brute force SSH access to these devices. It’s highly likely that by targeting MIPS, the P2Pinfect developers intend to infect routers and IoT devices with the malware. Use of MIPS processors is common for embedded devices and the architecture has been previously targeted by botnet malware, including high-profile families like Mirai [3], and its variants/derivatives.

Not only is this an interesting development in that it demonstrates a widening of scope for the developers behind P2Pinfect (more supported processor architectures equals more nodes in the botnet itself), but the MIPS32 sample includes some notable defense evasion techniques. 

This, combined with the malware’s utilization of Rust (aiding cross-platform development) and rapid growth of the botnet itself, reinforces previous suggestions that this campaign is being conducted by a sophisticated threat actor.

Initial access

Cado researchers encountered the MIPS variant of P2Pinfect after triaging files uploaded via SFTP and SCP to a SSH honeypot. Although earlier variants had been observed scanning for SSH servers, and attempting to propagate the malware via SSH as part of its worming procedure, researchers had yet to observe successful implantation of a P2Pinfect sample using this method - until now.

In keeping with similar botnet families, P2Pinfect includes a number of common username/password pairs embedded within the MIPS binary itself. The malware will then iterate through these pairs, initiating a SSH connection with servers identified during the scanning phase to conduct a brute force attack. 

It was assumed that SSH would be the primary method of propagation for the MIPS variant, due to routers and other embedded devices being more likely to utilize SSH. However, additional research shows that it is in fact possible to run the Redis server on MIPS. This is achievable via an OpenWRT package named redis-server. [4]

It is unclear what use-case running Redis on an embedded MIPS device solves, or whether it is commonly encountered in the wild. If such a device is compromised by P2Pinfect and has the Redis-server package installed, it is perfectly feasible for that node to then be used to compromise new peers via one of the reported P2Pinfect attack patterns, involving exploitation of Redis or SSH brute-forcing.

Static analysis

The MIPS variant of P2Pinfect is a 32-bit, statically-linked, ELF binary with stripped debug information. Basic static analysis revealed the presence of an additional ELF executable, along with a 32-bit Windows DLL in the PE32 format - more on this later. 

This piqued the interest of Cado analysts, as it is unusual to encounter a compiled ELF with an embedded DLL. Consequently, it was a defining feature of the original P2Pinfect samples.

Embedded Windows PE32 executable
Figure 1: Embedded Windows PE32 executable

Further analysis of the host executable revealed a structure named “BotnetConf” with members consistent in naming with the original P2Pinfect samples. 

Example of a partially populated version of the BotnetConf struct 
Figure 2: Example of a partially populated version of the BotnetConf struct 

As the name suggests, this structure defines the configuration of the malware itself, whilst also storing the IP addresses of nodes identified during the SSH and Redis scans. This, in combination with the embedded ELF and DLL, along with the use of the Rust programming language allowed for positive attribution of this sample to the P2Pinfect family.

Updated evasion - consulting tracerpid

One of the more interesting aspects of the MIPS sample was the inclusion of a new evasion technique. Shortly after execution, the sample calls fork() to spawn a child process. 

The child process then proceeds to access /proc using openat(), determines its own Process Identifier (PID) using the Linux getpid() syscall, and then uses this PID to consult the relevant /proc subdirectory and read the status file within that. Note that this is likely achieved in the source code by resolving the symbolic link at /proc/self/status.

Example contents of /proc/pid/status when process not being traced
Figure 3: Example contents of /proc/pid/status when process not being traced

/proc/<pid>/status contains human-readable metadata and other information about the process itself, including memory usage and the name of the command currently being run. Importantly, the status file also contains a field TracerPID:. This field is assigned a value of 0 if the current process is not being traced by dynamic analysis tools, such as strace and ltrace.

Example MIPS disassembly showing reading of /proc/pid/status file
Figure 4: Example MIPS disassembly showing reading of /proc/pid/status file

If this value is non-zero, the MIPS variant of P2Pinfect determines that it is being analyzed and will immediately terminate both the child process and its parent. 

read(5, "Name:\tmips_embedded_p\nUmask:\t002", 32) = 32 
read(5, "2\nState:\tR (running)\nTgid:\t975\nN", 32) = 32 
read(5, "gid:\t0\nPid:\t975\nPPid:\t1\nTracerPid:\t971\nUid:\t0\t0\t0\t0\nGid:\t0\t0\t0\t0", 64) = 64 
read(5, "\nFDSize:\t32\nGroups:\t0 \nNStgid:\t975\nNSpid:\t975\nNSpgid:\t975\nNSsid:\t975\nVmPeak:\t    3200 kB\nVmSize:\t    3192 kB\nVmLck:\t       0 kB\n", 128) = 128 
read(5, "VmPin:\t       0 kB\nVmHWM:\t    1564 kB\nVmRSS:\t    1560 kB\nRssAnon:\t      60 kB\nRssFile:\t    1500 kB\nRssShmem:\t       0 kB\nVmData:\t     108 kB\nVmStk:\t     132 kB\nVmExe:\t    2932 kB\nVmLib:\t       8 kB\nVmPTE:\t      16 kB\nVmSwap:\t       0 kB\nCoreDumping:\t0\nThre", 256) = 256 
mmap2(NULL, 4096, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x77ff1000 
read(5, "ads:\t1\nSigQ:\t0/1749\nSigPnd:\t00000000000000000000000000000000\nShdPnd:\t00000000000000000000000000000000\nSigBlk:\t00000000000000000000000000000000\nSigIgn:\t00000000000000000000000000001000\nSigCgt:\t00000000000000000000000000000600\nCapInh:\t0000000000000000\nCapPrm:\t0000003fffffffff\nCapEff:\t0000003fffffffff\nCapBnd:\t0000003fffffffff\nCapAmb:\t0000000000000000\nNoNewPrivs:\t0\nSeccomp:\t0\nSpeculation_Store_Bypass:\tunknown\nCpus_allowed:\t1\nCpus_allowed_list:\t0\nMems_allowed:\t1\nMems_allowed_list:\t0\nvoluntary_ctxt_switches:\t92\nn", 512) = 512 
mmap2(NULL, 8192, PROT_READ|PROT_WRITE, MAP_PRIVATE|MAP_ANONYMOUS, -1, 0) = 0x77fef000 
munmap(0x77ff1000, 4096)                = 0 
read(5, "onvoluntary_ctxt_switches:\t0\n", 1024) = 29 
read(5, "", 995)                        = 0 
close(5)                                = 0 
munmap(0x77fef000, 8192)                = 0 
sigaltstack({ss_sp=NULL, ss_flags=SS_DISABLE, ss_size=8192}, NULL) = 0 
munmap(0x77ff4000, 12288)               = 0 
exit_group(-101)                        = ? 
+++ exited with 155 +++ 

Strace output demonstrating TracerPid evasion technique

Updated evasion - disabling core dumps

Interestingly, the sample will also attempt to disable Linux core dumps. This is likely used as an anti-forensics procedure as the memory regions written to disk as part of the core dump can often contain internal information about the malware itself. In the case of P2Pinfect, this would likely include information such as IP addresses of connected peers and the populated BotnetConf structure mentioned previously. 

It is also possible that the sample prevents core dumps from being created to protect the availability of the MIPS device itself. Low-powered embedded devices are unlikely to have much local storage available and core dumps could quickly fill what little storage they do have, affecting performance of the device itself.

A screen shot of a computer codeAI-generated content may be incorrect.
Image 5

This procedure can be observed during dynamic analysis, with the binary utilising the prctl() syscall and passing the parameters PR_SET_DUMPABLE, SUID_DUMP_DISABLE.

munmap(0x77ff1000, 4096)                = 0 
prctl(PR_SET_DUMPABLE, SUID_DUMP_DISABLE) = 0 
prlimit64(0, RLIMIT_CORE, {rlim_cur=0, rlim_max=0}, NULL) = 0 

Example strace output demonstrating disabling of core dumps

Embedded DLL

As mentioned in the Static Analysis section, the MIPS variant of P2Pinfect includes an embedded 64-bit Windows DLL. This DLL acts as a malicious loadable module for Redis, implementing the system.exec functionality to allow the running of shell commands on a compromised host.

Disassembly of the Redis module entrypoint
Figure 6: Disassembly of the Redis module entrypoint, mapping the system.exec command to a handler

This is consistent with the previous examples of P2Pinfect, and demonstrates that the intention is to utilize MIPS devices for the Redis-specific initial access attack patterns mentioned throughout this blog. 

Interestingly, this embedded DLL also includes a Virtual Machine (VM) evasion function, demonstrating the lengths that the P2Pinfect developers have taken to hinder the analysis process. In the DLLs main function, a call can be observed to a function helpfully labelled anti_vm by IDAs Lumina feature.

Decompiler output showing call to anti_vm function
Figure 7: Decompiler output showing call to anti_vm function

Viewing the function itself, it can be seen that researchers Christopher Gardner and Moritz Raabe have identified it as a known VM evasion method in other malware samples.

IDA’s graph view for the anti_vm function showing Lumina annotations
Figure 8: IDA’s graph view for the anti_vm function showing Lumina annotations

Conclusion

P2Pinfect’s continued evolution and broadened targeting appear to be the utilization of a variety of evasion techniques demonstrate an above-average level of sophistication when it comes to malware development. This is a botnet that will continue to grow until it’s properly utilized by its operators. 

While much of the functionality of the MIPS variant is consistent with the previous variants of this malware, the developer’s efforts in making both the host and embedded executables as evasive as possible show a continued commitment to complicating the analysis procedure. The use of anti-forensics measures such as the disabling of core dumps on Linux systems also supports this.

Indicators of compromise (IoCs)

Files SHA256

MIPS ELF 8b704d6334e59475a578d627ae4bcb9c1d6987635089790350c92eafc28f5a6c

Embedded DLL Redis Module  d75d2c560126080f138b9c78ac1038ff2e7147d156d1728541501bc801b6662f

References:

[1] https://nvd.nist.gov/vuln/detail/CVE-2022-0543

[2] https://unit42.paloaltonetworks.com/peer-to-peer-worm-p2pinfect/

[3] https://unit42.paloaltonetworks.com/mirai-variant-iz1h9/

[4] https://openwrt.org/packages/pkgdata/redis-server

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
The Darktrace Community

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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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About the author
Dr. Tim Bazalgette
Chief AI Officer, Darktrace

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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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About the author
Parvatha Ananthakannan
Cyber Analyst
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