Gaming the system: how a Chinese-speaking actor turned Brazilian government sites into an SEO weapon

Research by: Amit Yardeni

Key Points

• A Chinese-speaking actor is now targeting Brazil. Check Point Research has uncovered a sustained campaign against Brazilian organizations, primarily government and educational institutions since mid-2025. We dubbed this group Gambling Goblin: a Chinese-speaking cybercrime cluster connected to a previously documented group, Earth Berberoka, that targeted gambling sites across Asia. It marks a shift from Brazil’s usual home-grown banking-trojan threats to a foreign operator moving in
• Compromised web servers turned into stealthy proxies. The attackers compile and install malicious Apache modules on victim servers that silently reverse-proxy visitors to attacker-controlled phishing pages, while the traffic still appears to originate from the legitimate domain, with the site’s own security headers stripped so injected content runs freely.
• Large-scale SEO manipulation. The phishing pages pose as trusted app stores such as Google Play, Microsoft Store, and Amazon. Behind that facade, they push online gambling and sports betting, and they chain together compromised high-reputation domains, many of them Brazilian government sites, to inflate search rankings and hijack traffic at scale.
• A broad, heavily obfuscated Linux toolkit. Once inside a host, the group deploys custom tools – downloader (DownPro), multiple backdoors including the modular AlphaAgent and the oRAT RAT, a 3snake-based credential stealer, an SSH brute-forcer, and a plugin-driven reconnaissance agent. Most of them are wrapped in packing and virtualization layers to slow analysis and evade detection.
• The operation reaches well beyond Brazil. We identified parallel phishing networks localized in Vietnamese, Spanish, and English, alongside infrastructure that generates fresh domains daily – evidence the model is built to scale and be exported to new regions.
• One step from direct malware delivery. Because the pages already mimic app-download destinations, the same infrastructure sits a single configuration change away from pushing malware straight to victims, a latent escalation risk beyond the current search-fraud scheme.

Introduction

Since mid-2025, Check Point Research has tracked a sustained campaign against Brazilian organizations. The tradecraft points to a Chinese-speaking cybercrime group connected to Earth Berberoka, an actor first documented targeting gambling sites across Asia.

Once inside a victim, the group deploys a broad Linux toolkit: a custom downloader, several backdoors, and familiar offensive utilities. Most of it arrives heavily obfuscated – wrapped in layered virtualization and packing to slow analysis and evade detection.

The purpose becomes clear at the network layer. The attackers install custom Apache modules that quietly proxy visitors to a sprawling set of phishing pages. Many of those pages sit on Brazilian government domains that appear to have been compromised and repurposed without their owners’ knowledge.

The reach extends beyond Brazil. We uncovered a second phishing network run by the same actor; this one is built for Vietnamese victims.

The likely goal is SEO manipulation at scale. By hijacking trusted, high-reputation domains, many of them Brazilian government sites, the operators borrow that reputation to push their own content up the search rankings and hijack the traffic that follows. But the same infrastructure could serve a more dangerous end: the phishing pages impersonate app-download destinations such as Google Play, the Microsoft Store, and Amazon, which leaves the operators one step from pushing malware straight to victims.

Infection Flow

Initial Access

We have not directly observed this group’s initial access, but a revealing artifact surfaced on one of their servers: an exposed open directory hosting an ELF binary written in Go that bundles numerous reconnaissance and scanning plugins. The toolset reads like a complete attack-surface-mapping pipeline for internet-facing targets.

The group refers to this agent as “cluster-asset-mapping”, or “cam-agent” for short. It runs with a handful of flags:

• default – long-lived worker session for orchestrated task dispatch
• f – foreground mode without logging
• flog – enable logging (use with f)
• h – show help
• v – show version

The agent carries a configuration that includes:

• worker_endpoint
• server_id
• project
• agent_token
• embedded PEM certificates and keys for the server and agent
• a plugin list
• report policies

It logs to payload-run.log under the default directory of /tmp/asset-scan. The agent reads the JSON report policies to decide how to run its scan. The policies are driven by the following fields:

• common web ports
• batch_size
• retry_count
• retry_backoff_seconds
• level

The agent communicates with its server over gRPC, authenticating with the certificates and keys from its own configuration. It uses many known open-source pentesting tools as modules:

• dirprobe – takes URLs and a directory list or profile, sends HTTP requests, and records the status code, response length, and title for each probed path.
• httpx – takes URLs, ports, and HTTP options, then collects the status code, response length, title, protocol, TLS details, and banners from each target.
• naabu – takes IPs or hostnames, port ranges, and a scan mode, attempts TCP connections across all targets, and marks each port as open, closed, or filtered.
• nuclei (v3) – takes URLs, paths, and workflows, executes HTTP/DNS/TCP checks as defined by templates, and emits a structured result for each match (template ID, severity, affected URL, evidence).
• subfinder – takes root domains, resolvers, and a depth, then enumerates subdomains via DNS brute force, certificate transparency, and passive sources, returning the discovered subdomains.
• whatweb – a Wappalyzer-style fingerprinter that issues HTTP requests to each target and applies rules to identify web servers, frameworks, CMS platforms, JavaScript libraries, and more.

Stealth phishing structure

Apache Modules

The group automates deployment of its malicious Apache module through a Bash installer. The script first confirms it is running as root, then fingerprints the host as either Debian/Ubuntu or CentOS/RedHat and pulls in the matching Apache development packages so the module can be compiled on the victim itself. It downloads the module’s C source, opsproxy.c, from a hardcoded staging server and, notably, patches the source on the fly to insert a missing macro definition so the code compiles cleanly. This is a small touch that shows the operators built the module to run across a range of victim configurations.

Compilation and installation are handled in a single step via Apache’s own apxs tooling, which also wires the module into the server’s configuration. What follows is a deliberate effort to hide the intrusion: the script deletes the source and all build artifacts, then timestomps the resulting .so and its load-configuration files to match legitimate, pre-existing Apache modules such as mod_ssl or mod_suexec, so the malicious files blend in during a casual review. It then enables the stock proxy, headers, and rewrite modules the malicious module depends on, tests the configuration, and restarts Apache to bring everything live.

Throughout, the script’s status messages are written in Chinese and decorated with emoji, a style that may point to AI-assisted development.

The source file, opsproxy.c, reveals a purpose-built reverse proxy that quietly grafts attacker-controlled content onto a compromised web server. The module registers itself at Apache’s name-translation stage and inspects every incoming request for one of a small set of hardcoded URL prefixes which in our samples, /wps, /bmw, and /card. When a request matches, the module rewrites it into a reverse-proxy request to a corresponding upstream server hardcoded into the source, silently relaying the visitor to attacker infrastructure while the request still appears, to the outside world, to come from the legitimate compromised domain.

To make that relayed content render without interference, the module strips the upstream site’s Content-Security-Policy headers. It replaces them with a deliberately permissive policy that allows inline and dynamically evaluated scripts, third-party assets, and data: and blob: sources. This removes the restrictions a browser’s CSP normally enforces, allowing injected or externally hosted scripts to execute freely.

The module also forwards the original Host header and adds standard proxy headers so the upstream sees a convincing request. The effect is a compromised, reputable server acting as a stealthy front door: certain paths transparently serve attacker content, and the browser protections that would ordinarily block foreign scripts are switched off for exactly those paths.

A second ELF Apache module used by the group disguises itself as a basic filter module while registering request and response hooks that examine visitor headers, URI paths, referrers, and client IPs. It carries a static configuration, decrypts it with RC4, and parses it into two rule types:

1. rule1 – an array of matching rules (path, referrer, or User-Agent, paired with a proxy URL)
2. rule3 – an optional response-filtering or injection configuration

Using a compiled-in regex for <body.*?> to locate its injection point, the module expands placeholders such as {host}, {hip}, {url}, and {name}, fetches remote content with libcurl, and writes that content into Apache responses via ap_rwrite and bucket manipulation. This gives a remote service control over what selected visitors and crawlers see on the compromised server. This is a behavior consistent with SEO cloaking and content-injection malware.

Brazilian infrastructure

Fetching the content served from the three upstream IP addresses hard-coded in the proxy module reveals the phishing infrastructure itself. Each address hosts a page impersonating a trusted app-distribution platform, localized in Brazilian Portuguese (lang="pt-BR") and dressed up with fabricated ratings, review counts, and structured schema.org metadata to appear legitimate to both users and search-engine crawlers.

All those IPs lean heavily on Bing’s thumbnail service (tse-mm.bing.com) to source imagery, tag their Open Graph and Twitter cards with @GooglePlay and @microsoftstore handles, and consistently theme around online gambling and sports betting aimed at a Brazilian audience – the actual monetization behind the campaign’s search-manipulation scheme. Tellingly, the pages carry Chinese-language CSS comments (for example a comment translating to “bottom navigation bar — fixed to the bottom on mobile, hidden on desktop”), the same operator fingerprint seen across the group’s server-side tooling.

Inspecting the domain used by the second Apache module brought us to a domain called playfootball[.]info that has a phishing page similar to the earlier ones. Unlike the earlier upstream samples that pulled assets from Bing thumbnails and a fake CDN, this one loads Google’s real production assets – the actual gstatic.com Play Store CSS bundle, Material Icons fonts, and the genuine Google Play logo SVG.

The most revealing finding from this page is that the app tiles and nav links don’t point to a single server; they point to dozens of real Brazilian domains, the majority of them legitimate .gov.br government sites, each serving the attacker’s gambling pages under paths like /jogos and /nova. The compromised institutions span every level of Brazilian government. At the federal level, they include a government ministry and a national public agency. At the state level, victims include a state legislative assembly, state courts of accounts, and a state-owned utility. The largest share, however, is local government: municipal administrations spread across numerous cities and multiple states. A smaller set of commercial .com.br sites such as local news outlets, health clinics, and business associations rounds out the victims.

Beyond Brazil

As we pivoted through the phishing infrastructure, the trail led well beyond Brazil. Several of the IP addresses hosted subdomain and domain generators, giving the operators a fresh supply of domains every day – a rotation scheme built to outpace blocklists and takedowns.

Some of the generated domains pointed to adult-content and gambling sites aimed at a Chinese-speaking audience, tying the infrastructure back to the operators’ origin and their long-running focus on the gambling sector.

Figure 11 – A gambling site in Chinese from the domain generator list

More telling, we found phishing pages built on the same template as the Brazilian ones, but localized in Vietnamese, Spanish, and English. The Brazilian operation is not a one-off: the same playbook is being adapted for other regions, and the infrastructure is clearly built to scale.

The Attacker’s Arsenal

Across these intrusions, the group draws on two kinds of tooling: well-known offensive utilities that any attacker might reach for, such as netcat, fscan, and pwnkit, and a broad set of custom tools written by the operators themselves: a downloader, several backdoors, a credential stealer, and purpose-built reconnaissance scripts. The sections below focus on that custom toolkit, which is where the group’s tradecraft shows.

DownPro

A downloader written in Go, referred to internally as DownPro. Its job is to pull the rest of the toolkit onto a freshly compromised host and launch it.

The binary is driven by a handful of flags, and a telling detail stands out immediately: their help strings are written in both English and Chinese. The flags are:

• u – URL of the main backdoor to download
• id – URL of the ChUser payload
• up – URL of the unix_updates payload (the PasswordHarvester)
• j – offline URL encryptor mode: it takes a plaintext URL via u and outputs the ciphertext to use as the flag value in real runs
• logs – where to write logs

The values passed to these flags are AES-GCM encrypted with a hardcoded key and Base64-encoded, so the operator supplies pre-encrypted URLs at runtime rather than leaving them in the clear.

DownPro then decides where to drop its payload based on its effective UID, preparing two sets of candidate destination paths: one for root, one for non-root. Running as root, it selects one of:

• /usr/local/bin/systemd-udevd
• /usr/local/bin/rsync-tsl
• /usr/local/bin/tcp-tsl
• /usr/local/bin/snapd-ext
• /usr/local/bin/fsck-disk
• /usr/local/bin/nftables-init

These names are chosen to blend into a Linux server environment, either mimicking legitimate system components or looking like ordinary utility and network helpers. Running without root, it instead generates one of two temp-style names designed to pass as routine disk clutter:

• /tmp/php_sess_<32_hex_chars> – mimicking a PHP session file
• /tmp/private-tmp-<5_alnum_chars> – looking like an ephemeral temp artifact

With the destination chosen, it downloads the file from the -u URL and executes it with the argument -si.

The two optional payloads are handled separately. When the -id flag is set, DownPro downloads a file to /usr/bin/chuser, sets its permissions to 0755, changes its owner to root, and timestomps it to match /bin/ls and turning it into a setuid helper that serves as a persistent local privilege-escalation backdoor. When the -up flag is set, it downloads a file to /usr/sbin/unix_updates and runs it with -v FuckMe#988, then strips the setuid bit from /usr/bin/pkexec.

ChUser

A simple backdoor that masquerades as a chuser utility. It executes commands passed through the -c flag, but only after passing one of two activation checks:

1. Remote HTTP activation – the backdoor builds a curl command using the -x <version> flag and runs it. Activation succeeds only if the command’s output matches the expected value, chuser no version.
2. Local MD5-based activation – the backdoor concatenates a user-supplied secret (from the -s <secret> flag) with a hardcoded salt, FuCkMe#, computes the MD5 of secret + salt, and compares it against a hardcoded target hash. Activation succeeds only on a match.

PasswordHarvester

A credential stealer based on 3snake that monitors newly executed authentication programs, including sshd, sudo, su, doas, ssh, ssh-add, passwd, kinit, and login.

On startup, it sets a clean PATH environment variable and installs signal handlers so the daemon can log and exit cleanly. It runs only as root, exiting otherwise, and gates execution behind a covert activation switch: the CRC32 of the -v argument must match a hardcoded value.

Once the CRC gate passes, the stealer resolves the host’s name and IPv4 addresses, then daemonizes by forking, calling umask(0) so it can freely control file permissions, changing its working directory to /tmp, and redirecting stdout and stderr to a file.

To hide itself, it picks at random from roughly 29 fake process names, such as:

• [kworker/1:2]
• [ksoftirqd/0]
• [watchdog/0]
• [systemd]
• [dbus-daemon]
• [journald]
• [migration/0]
• [ksmd]

It overwrites the original argv with the chosen name and calls prctl to change the kernel-visible task name to match.

The core logic then opens a netlink socket and subscribes to process events (PROC_CN_MCAST_LISTEN). On every process execution or UID change event, it checks whether the process name or command line matches one of the target programs listed above. When a match falls outside the expected path prefixes, it enters the interceptor flow: it attaches to the target with ptrace, reads the credential buffers, and exfiltrates them to its C2, RC4-encrypted and Base64-encoded.

AlphaAgent

A modular backdoor written in Go, built to land quietly, blend into a busy host, take orders over an encrypted channel, and hand its operator everything they need to work through a network.

On launch, AlphaAgent first checks whether it was invoked to finish an upgrade, so an in-progress self-update can complete cleanly. It then parses its command-line flags, validates its configured role and transport, and generates a Device ID from either the victim’s MAC address or the username combined with a hardcoded salt (e*f#1%0d$6&5=6). After checking its debug flags (DEBUG, VERBOSE, or neither), it decrypts its configuration strings using AES-GCM with a hardcoded key.

[Figure 19 – Device ID generation](Gaming the system how a Chinese-speaking actor tur/image_(4)

The configuration holds the region blocklist, the transport role and mode, the C2 domain, the TLS SNI camouflage value used for the certificates, and the directory, filename, and loader names for the rootkit.

With its configuration in hand, the agent goes to ground. It renames its own process to pass as a kernel thread or a system daemon, choosing the disguise from its configuration profile and applying it by rewriting argv[0] or calling prctl. The profiles are:

• aws → /usr/sbin/amazon-master or /usr/local/sbin/amazon-proxy
• google → /usr/bin/google_user_agent or /usr/bin/google_proxy_agent
• aliyun → rsyslogd
• general → one of a set of kernel-thread-style names:

"dbus-daemon -n%d"
"scsi_eh_%d"
"[migration/%d]"
"[cpuhp/%d]"
"[kworker/u%d:1]"
"[watchdog/%d]"
"[kswapd%d]"

• When not running as root, it falls back to php-fpm: pool www or nginx: worker process.

AlphaAgent then detaches into the background and writes a PID lock file under an innocuous path so that only one copy runs. It sleeps for a randomized interval which is long enough to outlast a quick sandbox detonation, and checks where it is running: if the host’s country matches the operators’ blocklist (China, in the samples we analyzed), the agent simply exits. Only after clearing that geofence does it enter its connect-and-retry loop and reach out to the server.

Finally, if the -r flag is set at execution, AlphaAgent checks whether the rootkit’s kernel module is already loaded. If it is not, the agent installs it; the rootkit ships embedded inside the binary via Go’s embed.FS API. In all the samples we analyzed, we haven’t found any rootkits, only placeholders.

Once connected, the agent enrolls, starts a heartbeat, and subscribes for jobs. How it talks to its server is a build-time choice, and each option is designed to look like something benign.

The primary channel is gRPC over HTTPS. The agent’s gRPC transport is built as a publish/subscribe service. The agent subscribes to receive jobs and publishes results back, and on top of that base, it opens dedicated streams for each interactive function rather than multiplexing everything through one pipe. There are separate streams for the web terminal, for uploads, for downloads, and for keepalive pings, and each exists in two directions an operator-facing set and an agent-facing set. That separation keeps a live terminal session responsive while a large file transfer runs in parallel.

Three design choices make this channel hard to spot on the wire:

• uTLS fingerprint mimicry. The agent uses a library that forges the TLS handshake of a real browser, so fingerprint-based detection (JA3/JA4-style) sees a normal Chrome-like client, not a Go program.
• Google and Cloudflare camouflage. It presents api.google.com as its server name, serves a .google.com certificate, and dresses its HTTPS heartbeats as Google traffic with decoy cookies (NID, SID, and similar) plus a custom proof scheme carried in Cloudflare-style parameters (_cf_auth_ts, _cf_auth_nonce, _cf_auth_method). The heartbeat side exposes handler paths like /agent/heartbeat and /notifications/v1/push to complete the illusion of a Google notification service.
• Encryption beneath the encryption. Job and result messages are themselves AES-GCM encrypted before they travel inside the TLS session. Even an analyst who terminates the TLS still faces an encrypted payload.

The Message fields of the communication:

Message Message
  field 1: string cid            (label=optional) - connection ID
  field 2: string mid            (label=optional) - Message ID
  field 3: int32  command        (label=optional) - specific command to run
  field 4: bytes  data           (label=optional) - data for command
  field 5: string topic          (label=optional) - channel name
  field 6: bytes  encrypted_data (label=optional) - encrypted payload
  field 7: string sid            (label=optional) - stream ID
  field 8: string file_name      (label=optional) - if there is a file
  field 9: string file_action    (label=optional) - can be upload / download / delete / list

The alternative channel is DNS. Here the same commands travel inside DNS queries: each job is encrypted, Base32-encoded, and split across DNS labels, then exchanged as TXT-style traffic on port 53. Many environments scrutinize outbound web sessions but wave DNS through, which is exactly the point. A separate variant of the toolkit keeps things simpler still, tunneling its protocol over a plain HTTP connection with certificate checks disabled.

The alternative channel is DNS. Here the same commands travel inside DNS queries: each job is encrypted, Base32-encoded, and split across DNS labels, then exchanged as TXT-style traffic on port 53. Many environments scrutinize outbound web sessions but wave DNS through which is exactly the point. A separate variant of the toolkit keeps things simpler still, tunneling its protocol over a plain HTTP connection with certificate checks disabled.

Whichever channel it uses, the agent bootstraps through public DoH and GeoIP providers such as Cloudflare, Google, ipinfo, and others, both to resolve its server and to run the geofence check described above.

At the center of the agent is a single job dispatcher. The server sends a numbered command; the dispatcher routes it to the matching handler. That design keeps the protocol compact and makes the feature set easy to summarize. The sections below cover the ones that matter most.

• Remote shell and interactive terminal – The workhorse is remote command execution. A shell job is joined into a single string and run through /bin/sh -c, and the combined output is captured and returned to the operator. The agent takes care to keep this quiet. It sets HISTFILE=/dev/null so commands leave no shell history behind. For interactive work, the agent goes beyond one-shot commands. It can allocate a real pseudo-terminal, launch a shell inside it, and stream that terminal to the operator as a browser-based “webtty” session. This gives an attacker a live, interactive shell with full terminal behavior, not just fire-and-forget commands, which is what you want for hands-on-keyboard operations.
• File Operations – File handling is complete in both directions. The agent can download files to the host and upload files from it, with both direct and streamed transfer paths for larger data transfers. Alongside transfer, a file browser lets the operator list directories and walk the filesystem interactively before deciding what to take. Together, these turn the backdoor into a remote file manager for the compromised host.
• Tunneling and pivoting – This is where the agent shows its intent to move laterally. It bundles a SOCKS5 proxy, a yamux-based multiplexer, and a Ligolo-style relay, turning the compromised host into a pivot point for the operators’ traffic. A dedicated relay mode lets the agent listen for inbound connections and forward them, so one foothold can open a path into the rest of an internal network. In the tunneling paths, certificate verification is deliberately turned off to keep the relay flexible.
• Relay Tunneling – AlphaAgent can also be deployed not as an implant but as a relay node. The agent validates a configured role at startup, and, in its tunnel-edge role, starts a listener and forwards traffic upstream to the command-and-control server on a different port, preserving the same gRPC streams. It uses its own embedded node token to identify itself in this mode. In other words, the operators can seed both endpoints – victims that call home and relay nodes that concentrate and forward that traffic – from one codebase. One build even carries a tag pointing to a specific tunnel geography ([dns-hktun / Hong Kong]), suggesting the relay tier is planned around location.
• Discovery and collection – The reconnaissance is aimed squarely at spreading. Beyond a standard host and network inventory: hostname, users, running services, active network connections, interface addresses, and virtualization hints, the agent reads login history from wtmp, utmp, and the system authentication logs, and enumerates current SSH sessions. It can then archive a victim’s .ssh directory and .bash_history into a compressed bundle for exfiltration. Read who logged in, grab their keys and history, and use the tunnel to reach the next host: the collection features are built to feed lateral movement, not just to profile a single machine. Worth flagging: the host inventory the agent sends home includes a virtualization role field, meaning the agent reports back whether it believes it is running inside a virtual machine or sandbox. That gives the operators a chance to abandon or lie low on analysis systems before doing anything noisy.
• AI Plugin – The newest build we found, introduces something the earlier versions do not have: an AI plugin execution path. The evidence is currently limited to internal strings. The agent logs executing AI plugins when it runs one and recovers from failures through an AI plugin panic handler, so we can confirm the capability exists and is guarded like a first-class feature, but the sample does not reveal what the plugin is or does. In the code AlphaAgent gets scripts probably written by an AI orchestrator on the server side named “ai_plugin_%s.sh”, runs them and sends the result to the C2.
• Evasions – The agent invests heavily in remaining unseen. It renames its process to impersonate legitimate kernel threads and services entries like [kworker/...], [kswapd...], nginx: worker process, or rsyslogd and overwrites its own command-line arguments so tools that read them see the disguise too. It suppresses its own output to /dev/null and detaches as a daemon. Some builds go further and hide the process outright. On command, the agent can bind-mount over its own /proc entry, making itself invisible to anything that reads the process table – a lightweight but effective trick that needs no kernel module. Other builds do carry a kernel-module component, controlled through custom device commands, that hides processes and network connections at the kernel level and can stage an additional loader fetched from the operator. The encrypted configuration and the traffic camouflage described earlier round out an evasion posture that spans disk, process table, and network. One variant is packaged to defeat analysis itself. It is wrapped in a protector that strips the file’s structure, unpacks the real payload only in memory, obfuscates its internals, and watches for a debugger, popping a decoy error and exiting the moment it detects one. Same feature set underneath, hardened against the analyst.

The full list of commands:

oRAT

oRAT is a Go-based Linux remote access trojan built for full remote administration of a compromised host.

It starts with decrypting the configuration baked into the binary that contains: the C2 address, the install paths, the process disguise, and the hiding flags all live inside one encrypted blob and are only unpacked in memory.

Unless told to skip it, the agent then runs its preparation routine, and this is where most of the damage is done before any traffic leaves the box. It configures logging to /dev/null by default, daemonizes, disables SELinux enforcement (setenforce 0), installs itself to a persistent location, registers a service, writes a GUID, takes a file lock so only one copy runs, deletes its original on-disk copy if it was relocated, and optionally hides its own process. Only after all of that does it enter its main loop of communication.

The agent’s communication routine supports three transports, selected by config:

• tcp – a raw TCP connection
• stcp – TLS over TCP
• sudp – QUIC over UDP, using the quic-go library

On top of whichever transport it picks, oRAT layers a multiplexed session and speaks HTTP through it. It uses a standard Go HTTP client, but rewrites the client’s dialer so every request is carried inside the established oRAT session instead of hitting the network directly. The agent registers with the server by posting a join request to /join, then serves operator commands as REST-style routes over that same tunnel.

Because oRAT exposes its capabilities as HTTP routes, its feature set reads almost like API documentation. The operator API includes:

oRAT offers two paths to run commands, and the second is the more interesting.

The direct path is a command route that hands operator input to sh -c and returns the output, standard RAT behavior.

The richer path is a fully embedded SSH server. oRAT builds its own SSH service into the agent, complete with a hardcoded RSA host key, password authentication, port-forwarding, and an SFTP handler. When an operator connects, the agent spawns an interactive shell: trying zsh, then bash, then sh with proper pseudo-terminal handling. In practice, the operator gets a real SSH session and SFTP file access on the target, tunneled through the C2 channel rather than exposed on a listening port.

oRAT’s persistence is quiet and well chosen. It installs itself to /usr/local/bin/xtables-addons and registers a systemd service named xtables-addons, wired into the standard multi-user target so it starts on boot as root. xtables-addons is a real netfilter/iptables extension package, so an administrator glancing at the process list or the service table sees what looks like legitimate firewall tooling. If the agent lacks the privileges for a system-wide install, it falls back to per-user persistence through a user service and a cron entry.

It also hides its identity in an unexpected place. The agent stores its GUID by appending a # GUID: <uuid> comment line to /etc/protocols, a legitimate system file no one thinks to check. Its lock file sits at /tmp/.lock.

The evasion posture is layered and Linux-native:

• Process masquerade. The agent sets its process name to sshd: root@pts/0, so it reads in the process table as an interactive root SSH session. One build reinforces this by spoofing its executable path as /usr/sbin/sshd.
• Procfs hiding. When its mount mode is enabled, the agent bind-mounts over its own /proc/<pid> entry, disrupting inspection of the running process through the proc filesystem.
• Silent by default. Logging goes to /dev/null unless a specific debug environment variable is set.

Together, these span the process table, the filesystem, the security policy, and the kernel’s view of the process, a broad effort to make the agent hard to notice and harder to inspect.

BruteForcer

An SSH credential-checking and brute-force utility. It reads target IP addresses (-f flag), usernames (-u flag), passwords (-p flag), or pre-combined user:pass pairs (-up flag) from operator-supplied files, then attempts concurrent SSH logins against each target.

Successful credentials are printed and appended in plaintext to a local results file, res.txt. The binary has no hardcoded C2 infrastructure or persistence mechanism and its sole purpose is credential access against remote SSH services.

Recon Scripts

In some of the attacks we observed a number of Bash scripts with Chinese-language comments used by the attackers.

The first, info.sh, proceeds in four stages. It first pulls recent login activity to profile who uses the box. It then walks every user’s home directory, including root’s, to inventory .ssh folders, flag any files containing private keys, and comb .bash_history for sensitive commands involving SSH, SCP, database clients, cloud tooling, credentials, and kubectl, a fast way to harvest reusable secrets and understand the victim’s workflows. The third stage is the most refined: a storage analysis that hunts for remote network mounts (NFS, CIFS/Samba, WebDAV, cloud FUSE) and Docker volumes while deliberately filtering out overlay, tmpfs, and container-ID noise, so the operator sees only genuine lateral-movement targets rather than local container clutter – a sign the author iterated on the tool to cut false positives. Finally, it gathers classic lateral-movement intelligence: /etc/hosts entries, local listening TCP ports, and the ARP neighbor table to reveal adjacent hosts on the network.

The second script, findweb.sh, surveys a compromised host’s web-server landscape and maps out every site it serves. It first detects which web servers are running (Nginx, Apache, or httpd) using several fallback methods, and extends the check to containerized deployments by inspecting Docker for web-server images and any containers publishing ports 80 or 443 to the host. Where possible, it reports the ports each server listens on. It then parses the server configurations directly: for Nginx it walks the common configuration directories, resolving symbolic links and de-duplicating by real path, then extracts each virtual host’s domain (server_name), web root, and any proxy_pass upstreams; for Apache and httpd it does the equivalent, pulling DocumentRoot, ServerName, and ServerAlias from every VirtualHost block across the standard Debian, RedHat, and common control-panel configuration paths.

The result is a concise inventory of every domain hosted on the machine, where each site’s files live on disk, and where any existing reverse-proxy rules already point. In the context of this campaign, that inventory is exactly what an operator needs to weaponize a compromised server: it reveals which trusted domains are available to abuse, the exact web roots to plant content in, and where to graft the malicious proxy module so that attacker pages are served under a legitimate site’s name.

Attribution and links to prior work

We assess with medium-to-high confidence that Gambling Goblin is tied to Earth Berberoka – a Chinese-speaking threat cluster first documented by Trend Micro in 2022. Earth Berberoka is known for targeting online gambling platforms that serve Chinese-speaking users and operators, and for working across Windows, Linux, and macOS with a mix of aged commodity RATs and purpose-built tooling. Our assessment rests on three independent overlaps: the malware, the operator artifacts, and the network infrastructure.

Tooling. The group’s use of oRAT is the clearest link. oRAT was tied to Earth Berberoka in 2022, and the variant we analyzed shares the same orat/cmd/agent codebase and REST-style operator routes. The connection extends to the group’s custom malware: one of the AlphaAgent samples we recovered was uploaded in the same archive as other tools previously attributed to Earth Berberoka, placing AlphaAgent directly alongside the group’s known toolset rather than merely resembling it.

Operator artifacts. The focus on the online gambling sector and the Chinese-language strings scattered across this campaign’s tooling (dual-language flag descriptions, Chinese script comments, and Chinese-language page artifacts) align with operator fingerprints seen in the group’s past campaigns.

Infrastructure. The group has a documented habit of registering domains that impersonate trusted platforms. Trend Micro reported github[.]wiki as an Earth Berberoka domain while the infrastructure behind Gambling Goblin follows the same playbook: lookalike domains such as github[.]la and gitlab[.]bet closely mirror that tradecraft. Reinforcing the link, many of the C2 servers in this campaign are hosted on the same Amazon ASN (AS16509) the group has relied on before.

Conclusion

This campaign marks a shift in who targets Brazil, and why. For years, the threats facing Brazilian users came mostly from home grown banking trojan crews. Brazil is a natural target for this kind of operator. It has become one of the world’s fastest-growing online-betting markets, with a vast base of mobile users accustomed to installing apps on the spot, which is exactly the audience a gambling-driven fraud operation wants to reach. For a Chinese-speaking group that has spent a decade monetizing the gambling sector, the money now runs through Brazil, and the infrastructure to exploit it is often trusted but under-secured. A vast base of mobile users conditioned to install apps on sight, and a sprawl of trusted but under-secured web servers, most of them on government .gov.br domains whose search reputation is exactly what a large-scale SEO-fraud operation needs. Compromise those servers, graft on a malicious Apache module, and the attacker turns a nation’s legitimate infrastructure into a distribution network for gambling pages and fake app stores. That is the notable part: this is not opportunistic crime but patient, industrialized abuse of reputation, and it is run with espionage-grade Linux tooling in the service of financially motivated fraud, blurring the line between cybercrime and APT.

We expect the operation to grow rather than fade. The same infrastructure that inflates search rankings today is one configuration change away from serving malware tomorrow: the phishing pages already impersonate Google Play, the Microsoft Store, and Amazon, putting the operators a single step from pushing malicious apps straight to Brazilian victims. The Vietnamese, Spanish, and English pages we uncovered show the model is being exported, and the daily domain generators show it is built to scale. Countries should expect more of this, aimed higher, not only at customers and banking credentials, but at the government institutions whose domains lend the campaign its trust. None of it depends on novel exploits. It runs on unpatched internet-facing services, weak SSH credentials, and Apache modules that no one thinks to watch. If organizations, and public-sector operators in particular, do not close those gaps – patching exposed services, auditing Apache and SSH configurations, and hunting for rogue modules and masqueraded processes, then this actor and the wider wave of global cybercrime it represents, will keep finding an open door.

IOCs

Hashes:
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96488c59287889fcd3b9952ec78b78914fabb901c8b61a7354552439170ed148

Domains:
rb[.]aliyuntsl[.]com 
br[.]team-c2[.]com 
hwlocal[.]team-hw[.]com 
br[.]team-hw[.]com 
data[.]mirrors-inc[.]com 
team-hw[.]com 
update[.]team-c2[.]com 
devops[.]aliyuntsl[.]com  
bageyi[.]kernel-lib[.]com  
8yiu[.]kernel-lib[.]com
dnslog[.]kernel-lib[.]com
js[.]ai-jquery[.]com
api[.]onlinevrgame[.]com 
file[.]ijjjst23m[.]com
kerneltty[.]com
80[.]443[.]team
up[.]443[.]team
404[.]443[.]team
data[.]windows-update-cdn[.]com 
microsoft-azure-loadbalance[.]com
update[.]aliyun[.]la
api[.]gitlab[.]bet
github[.]la
update[.]opentls2[.]com

IPs:
154[.]84[.]62[.]160 
154[.]84[.]62[.]128 
154[.]84[.]62[.]149 
154[.]84[.]62[.]145 
15[.]228[.]251[.]82 
56[.]124[.]87[.]60  
18[.]229[.]255[.]14 
18[.]166[.]208[.]57 
18[.]228[.]136[.]28 
43[.]198[.]248[.]193
43[.]199[.]133[.]195
18[.]166[.]243[.]179
18[.]164[.]116[.]24 
13[.]203[.]9[.]172 
43[.]198[.]30[.]170 
18[.]162[.]210[.]53 
56[.]125[.]218[.]234
18[.]228[.]195[.]216
56[.]124[.]49[.]89 
54[.]207[.]196[.]189
165[.]22[.]101[.]200
172[.]80[.]8[.]202 
104[.]206[.]37[.]134
108[.]187[.]28[.]158
202[.]146[.]222[.]18
192[.]253[.]229[.]23
16[.]162[.]255[.]92
13[.]250[.]18[.]158
18[.]163[.]182[.]231
204[.]16[.]172[.]106

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