AI vs Impersonation Attacks: Stopping Gh0st RAT

Most security teams still treat “fake software downloads” as a low-sophistication problem. The 2025 reality is harsher: one operation registered 2,500+ malicious domains in a few months, impersonated 40+ popular applications, and delivered Gh0st RAT using cloud-hosted payloads and DLL side-loading to hide behind trusted software.

This matters because impersonation at scale doesn’t just trick individuals. It reliably breaches enterprises—especially when employees install “helpful” tools (translation apps, VPNs, remote desktop utilities, secure messaging clients) on work devices. And in late 2025, the trend is clear: attackers are leaning on legitimate infrastructure and signed binaries precisely because human review and simple allowlists can’t keep up.

This post is part of our AI in Cybersecurity series, and it uses two real 2025 campaigns as a practical example of where AI-driven threat detection is the difference between catching an attack early and spending weeks on incident response.

What these 2025 Gh0st RAT campaigns teach defenders

The main lesson is simple: impersonation isn’t a “phishing problem,” it’s an infrastructure and behavior problem. Once you see that, the defensive playbook changes.

Researchers observed two connected campaigns active throughout 2025 targeting Chinese-speaking users globally. The first wave (“Campaign Trio”) scaled by brute force—thousands of lookalike domains and a centralized payload host. The second wave (“Campaign Chorus”) matured—structured attack waves, cloud redirection, multi-stage MSI logic, and DLL side-loading using a legitimate signed executable.

Here’s the defensive implication: blocking a few bad domains or warning users to “be careful” won’t work against burn-and-churn domain factories. Detection has to focus on:

• Patterns in domain registration and hosting (mass infrastructure)
• Suspicious installer behaviors (MSI custom actions, script execution, side-loading)
• Post-compromise actions (Defender exclusions, scheduled tasks, encrypted C2)

This is exactly the kind of environment where AI in cybersecurity earns its keep: it can correlate weak signals across DNS, web, endpoint telemetry, and cloud downloads—faster than a human team can triage.

Anatomy of “digital doppelgangers”: how impersonation scales

Impersonation campaigns used to be a handful of typo-squats. These 2025 campaigns look more like software-as-a-service.

Campaign Trio: 2,000+ domains, three IPs, one payload hub

The early 2025 phase registered over 2,000 domains in February–March. A standout detail: the entire lookalike network resolved to just three IP addresses:

• 154.82.84[.]227
• 156.251.25[.]43
• 156.251.25[.]112

That’s not stealthy—it’s efficient. The model is “make domains disposable.” If defenders take down 50, 500 more are waiting.

The lures were tightly chosen for Chinese-speaking users:

• i4tools (over 1,400 domains) — Apple device management utility
• Youdao (over 600 domains) — dictionary/translation app
• DeepSeek (a small number of domains) — an AI brand used to ride hype cycles

All these sites pushed downloads from a centralized host domain (a single distribution point), often delivering a trojanized installer in a ZIP archive.

Campaign Chorus: 40+ impersonated apps and wave-based infrastructure

Starting May 2025, the operation expanded to impersonate over 40 applications, including enterprise tools, secure messaging apps, VPNs, and gaming platforms.

Two execution waves stood out:

• Wave 1: 40 domains using a consistent prefix (registered May 15)
• Wave 2: 51 domains using a different prefix (registered May 26–28)

This kind of wave structure matters because it hints at how to detect it: domain naming conventions, registration bursts, shared redirectors, and repeated hosting patterns.

AI-driven DNS and URL analysis can flag these campaigns earlier by learning what “normal” looks like for your environment and then surfacing anomalies like:

• Sudden spikes in users visiting newly registered domains
• Clusters of domains sharing hosting, certificates, or naming patterns
• Redirect chains that end in cloud storage downloads not previously seen

Why Gh0st RAT delivery keeps working: the installer trap

The payload in both campaigns was a variant of Gh0st RAT, a long-running remote access Trojan family favored by Chinese-nexus actors. Its capabilities are classic but devastating:

• Keystroke logging
• Screenshot capture
• Remote shell
• Additional payload delivery

The more interesting part is how it’s delivered.

MSI custom actions: hiding malicious logic in “normal installer noise”

MSI-based delivery is effective because installers naturally perform many actions—file writes, registry updates, process launches. Attackers exploit that baseline.

In the earlier chain, the MSI triggered a secondary executable (one sample cited was a 1.7 MB file) that:

1. Downloads an obfuscated binary from a staging server
2. Decodes it
3. Executes the decoded payload (Gh0st RAT)

For defenders, “user ran an installer” is not a useful alert by itself. What matters is the sequence: msiexec → script/child process → outbound download → persistence changes.

VBScript + split payload assembly: designed to beat static inspection

In the later chain, the MSI embedded a VBScript custom action that behaved like a mini build system:

• Reads multiple data chunks from the installer’s embedded cabinet
• Reassembles them into a binary
• Decrypts using a stored password
• Drops the next stage

This is explicitly designed to defeat simplistic detection that looks for “one suspicious blob” inside an installer.

AI-assisted sandboxing and detonation helps here because it doesn’t need to “see” the final payload statically. It can score behavior: file assembly patterns, script-driven decryption, and process ancestry.

DLL side-loading: hiding inside trusted, signed software

The most mature move in Campaign Chorus was DLL side-loading.

The chain dropped:

• A legitimate signed executable (wsc_proxy.exe)
• A malicious DLL (wsc.dll)

When the signed executable runs, Windows loads the local DLL first. The malware runs inside a process that looks trustworthy on paper.

This is why “allow signed binaries” is not a security strategy. It’s a comfort blanket.

Modern endpoint AI models can spot side-loading by correlating:

• A signed process loading an unexpected DLL from a user-writable path
• Rare parent/child process relationships
• DLL load events that don’t match the software’s typical execution profile

What AI can detect that humans and rules usually miss

AI doesn’t replace fundamentals—patching, least privilege, application control—but it fills the gap where volume and complexity win.

1) Impersonation pattern detection across DNS and web telemetry

Humans can investigate a suspicious domain. They can’t investigate 2,000.

AI can cluster domains by shared traits and produce a high-confidence lead such as:

• “These 312 domains were registered in a 48-hour burst, share a naming template, resolve to the same small IP pool, and serve software download pages.”

That’s an actionable incident, not a hunch.

2) Cloud-hosted payload abuse without blanket blocking

Campaign Chorus shifted payload hosting into public cloud buckets via redirect domains. Blocking “cloud storage” broadly breaks business. So the question becomes: which cloud downloads are suspicious?

AI-driven network detection can prioritize by:

• Newly observed download sources
• Rare file types (ZIP/MSI) fetched after visiting a newly registered domain
• Mismatched referrers (download initiated from an unexpected redirect chain)

3) Behavioral detection of post-infection actions

These Gh0st RAT samples were observed creating scheduled tasks for persistence and using PowerShell to add Windows Defender exclusions.

Those are high-signal behaviors—especially when they follow installer execution. AI-based correlation is what turns separate low-priority events into one clear storyline.

A useful rule of thumb: when an installer causes security controls to be weakened, treat it as malicious until proven otherwise.

Practical defense checklist for enterprises (what to do this week)

You don’t need a massive project plan to reduce exposure to these campaigns. You need a few disciplined controls and better detection logic.

Harden the software acquisition path

• Block installation from user download folders where feasible, or require admin elevation with justification.
• Enforce approved software catalogs for common “utility” categories: VPN, remote desktop, translation, messaging.
• Require software to be installed through managed tooling (MDM/RMM/software center), not ad-hoc web downloads.

Detect the behaviors that matter

Prioritize alerts and hunting queries around:

• msiexec spawning script interpreters (VBScript) or suspicious child processes
• New scheduled tasks created shortly after an installer runs
• PowerShell commands modifying Defender preferences/exclusions
• Signed binaries loading DLLs from unusual directories (side-loading signals)

Make DNS and domain intelligence part of your SOC workflow

• Monitor for newly registered domains visited by endpoints
• Flag domain bursts and naming-template clusters
• Track redirect chains that lead to installer downloads

Prepare for the “burn-and-churn” reality

Blocklists are still useful, but don’t rely on them alone. Build response that assumes:

• Domains will rotate weekly
• Payload hosting may move between self-hosted and cloud
• “Trusted” binaries can still execute attacker code

Where this fits in the AI in Cybersecurity story

Impersonation campaigns distributing Gh0st RAT are a clean case study for why AI-driven threat detection and response is becoming table stakes.

Humans are good at careful analysis. Attackers are good at volume, repetition, and small variations. AI closes that gap by spotting infrastructure patterns, anomalous installer chains, and endpoint behaviors early—then helping automate containment steps like isolating endpoints, blocking domains, and triaging similar events across the fleet.

If your current strategy depends on users noticing subtle domain differences, you’re betting against the attacker’s strongest advantage: scale. The better bet is building detection that assumes deception will succeed—and catches what happens next.

Where are you most exposed right now: unmanaged installs, weak DNS visibility, or limited endpoint behavior analytics?