AI Detection for Impersonation Attacks Like Gh0st RAT

Attackers registered 2,000+ lookalike domains in roughly eight weeks and funneled victims toward trojanized installers that eventually dropped Gh0st RAT. That’s not “spray and pray.” It’s an assembly line.

And the part that should make security teams uncomfortable is this: the campaign didn’t stay static. By May 2025, it had evolved into a more evasive, multi-stage chain that hid payloads inside MSI internals, pulled downloads from cloud buckets, and executed via DLL side-loading under a legitimate signed process.

This post is part of our AI in Cybersecurity series, and I’m going to take a stance: these are exactly the campaigns where AI-driven anomaly detection earns its keep. Not because AI magically “stops malware,” but because it’s one of the few practical ways to spot fast-changing impersonation infrastructure and suspicious behavior patterns before your users do the attacker’s job for them.

What these Gh0st RAT campaigns reveal about modern impersonation

Two connected 2025 campaigns (often described as an early “Trio” phase and a later “Chorus” phase) show a clear playbook: mass impersonation + user execution + stealthy staging + long-lived remote access.

Here’s what stands out for defenders:

• Scale is the feature. Over 2,000 domains were registered early on, often with patterns like brand name + random suffix and cheap TLDs.
• Targeting is deliberate. Lures emphasized software popular with Chinese-speaking users globally, including translation tools, browsers, messaging apps, VPN-like utilities, and even trending AI software.
• Evasion matures fast. The shift from basic droppers to MSI custom actions, embedded VBScript assembly/decryption, cloud-hosted payload delivery, and DLL side-loading signals iterative improvement.

If your defenses depend heavily on static lists (known bad domains, known bad hashes), you’re playing catch-up. This matters because these actors are effectively optimizing for your gaps: blocklists, reputation filters, and superficial “signed = safe” assumptions.

The attack chain, simplified: where defenders actually have leverage

The infection chain looks complex when diagrammed. Operationally, it boils down to four moments where detection and prevention are realistic.

1) Initial access: fake download portals at industrial scale

The first phase relied on thousands of impersonation domains that mimicked legitimate software download pages. Victims arrived via common traffic-shaping techniques (search manipulation, ads, social posts, forums, and sometimes email).

Two things make this hard:

• Domain churn means takedowns don’t end the campaign.
• The pages look “right enough” for hurried users.

AI opportunity: detect patterns rather than single domains. A good model (or rules backed by ML clustering) can flag lookalike domain families by:

• lexical similarity to brands (homoglyphs, prefixes/suffixes, random strings)
• TLD patterns (cheap/high-abuse TLD concentration)
• registration bursts (hundreds in a week)
• shared infrastructure (same small IP set hosting huge domain sets)

That’s not theory. In the early phase, 2,000+ domains mapped to only three IP addresses—exactly the kind of “statistically weird” footprint anomaly detection is built to catch.

2) User execution via trojanized MSI installers

In the early phase, victims downloaded ZIP archives containing a malicious MSI or executable. The MSI used custom actions to launch a secondary executable (separating visible installer behavior from the real malicious logic).

Why MSIs are attractive to attackers:

• MSI installs generate lots of normal-looking activity.
• Custom actions can hide among many legitimate actions.
• Users expect installers to request privileges.

AI opportunity: baseline MSI behavior for your environment and alert on outliers, such as:

• MSI spawning script engines (wscript.exe, cscript.exe) unexpectedly
• MSI spawning PowerShell for Defender tampering
• installer writing unusual binary blobs to temp/user profile paths
• high-entropy embedded resources suggestive of packed/encrypted payload chunks

3) Staging and persistence: Gh0st RAT behaviors are still detectable

The payload observed was Gh0st RAT (and variants), a long-running RAT family with predictable goals: remote control, data theft, screenshots, keylogging, and additional payload delivery.

Notably, samples were observed to:

• create scheduled tasks for persistence
• use PowerShell to add Windows Defender exclusions
• communicate with C2 via encrypted TCP traffic over port 8080

AI opportunity: behavior analytics on endpoints and network traffic, especially:

• parent/child process anomalies (installer → PowerShell → Defender config)
• new scheduled tasks created shortly after an installer runs
• “rare” destination domains following a fresh install event
• encrypted outbound to uncommon hosts shortly after execution (sequence matters)

This is where AI-driven correlation shines: any single event may look benign. The sequence is what convicts it.

4) The big evolution: cloud delivery + VBScript + DLL side-loading

By May 2025, the second phase expanded to 40+ impersonated applications and introduced a much more evasive chain:

• landing pages redirected through intermediary domains
• payload ZIP files were pulled from public cloud storage buckets
• the MSI executed an embedded VBScript custom action
• payload components were split across multiple MSI data files, then assembled and decrypted
• execution happened via DLL side-loading using a legitimate signed executable (e.g., a signed wsc_proxy.exe) loading a malicious wsc.dll

Attackers love this combination because it pressures weak assumptions:

• “Cloud-hosted download = safer”
• “Signed executable = trustworthy”
• “MSI activity = messy but normal”

AI opportunity: trust is contextual. AI-based systems can score risk based on mismatches, like:

• a signed binary executing from a user-writable folder rather than its expected installation path
• a signed executable loading a DLL from the local directory when it usually loads from System32
• first-seen DLL names with suspicious proximity to legit binary names
• unusual file-write patterns during MSI execution (chunk assembly is noisy if you watch)

A snippet-worthy rule of thumb:

Signed code isn’t safe code; it’s code that deserves stricter “expected behavior” checks.

Why AI is the practical answer to “burn-and-churn” infrastructure

These campaigns were described with a “burn-and-churn” tempo: disposable domains and hosting, separated from more valuable operational infrastructure (payload/C2). That strategy is designed to outpace human-led triage.

AI helps most when it does three jobs at once:

1. Compresses time (detect domain clusters in minutes, not days)
2. Connects weak signals (DNS oddities + endpoint process trees + download lineage)
3. Prioritizes response (10,000 alerts are useless; 10 correlated incidents are actionable)

If you’re evaluating AI in cybersecurity tools, I’ve found this question cuts through marketing fast:

Can it explain why something is anomalous in my environment, and can it group related artifacts into one incident?

If the answer is no, you’ll still drown—just with fancier dashboards.

Defensive playbook: controls that work even when the lures change

Here are practical steps that map directly to the tactics used in these Gh0st RAT impersonation campaigns.

Reduce the chance users ever reach the trap

• Enforce “known source” software installs: restrict installs to managed app catalogs or approved vendor repositories.
• Harden DNS and web filtering: prioritize detection of newly registered domains, lookalike domains, and suspicious TLD patterns.
• Add download provenance checks: if installers come from the web, require validation steps (hash verification from trusted channels, code signing reputation, and origin checks).

Make MSI-based malware louder

• Monitor msiexec.exe spawning:
  • powershell.exe
  • wscript.exe / cscript.exe
  • unusual child processes from temp directories
• Alert on MSI custom actions that write executable content followed by immediate execution.
• Treat any installer that modifies Defender exclusions as a high-severity event.

Detect DLL side-loading as a behavior pattern

• Baseline legitimate signed executables’ normal DLL load locations.
• Alert when a signed executable loads a DLL from:
  • the current directory
  • user-writable paths
  • newly created directories shortly before execution
• Correlate “downloaded archive → extracted signed EXE → immediate execution” sequences.

Use AI for correlation, not just classification

Most teams already have point detections. The win is in correlation:

• DNS: new domain family appears → download events spike
• Endpoint: MSI install event → script engine execution → Defender change
• Network: first-time outbound encrypted session to rare destination within minutes

When your platform can stitch that together automatically, you move from “alert review” to “incident handling.”

Quick Q&A (the stuff teams ask in real meetings)

Does blocking the listed IoCs solve it?

It helps, but it doesn’t solve it. The domain strategy is explicitly designed to rotate infrastructure. Use IoCs as seeds for similarity detection and cluster expansion, not as your only control.

Why do cloud buckets matter so much here?

Because many environments implicitly trust major cloud providers. Attackers exploit that trust to blend malicious downloads into normal SaaS traffic. Your best defense is behavioral context: what downloaded it, what executed it, and what happened next.

What’s the single most reliable endpoint signal in this chain?

Installer-driven defense tampering. If a freshly executed installer results in Defender exclusions, scheduled task creation, and outbound C2-style traffic, you have enough to isolate first and ask questions later.

What to do next (and how this fits the AI in Cybersecurity series)

These impersonation campaigns aren’t interesting because Gh0st RAT is “new.” They’re interesting because they show how quickly attackers adapt when defenders get comfortable: mass domains when reputation filters dominate, cloud delivery when IP blocklists improve, and DLL side-loading when allow-lists tighten.

If you’re building your 2026 security roadmap right now, prioritize AI-driven threat detection that can:

• cluster lookalike domains and infrastructure automatically
• correlate endpoint, DNS, and network telemetry into a single narrative
• detect behavioral mismatches in signed-binary execution and DLL load patterns

If you want a concrete starting point, audit one workflow: how software gets installed in your organization. Where do users download installers? Which teams approve exceptions? What telemetry do you retain around msiexec.exe, script engines, and Defender configuration changes?

That’s the question I’d leave you with: if a user installs a “popular app” from a convincing clone site on Monday morning, how quickly can your defenses tell the difference between a normal install and the first five minutes of a multi-stage compromise?