AI Detection Playbook for GrayBravo CastleLoader

Most security teams still treat phishing, loaders, and remote access trojans as separate problems—different dashboards, different detections, different owners. GrayBravo’s CastleLoader ecosystem is the reminder (and frankly, the proof) that attackers don’t operate that way.

Recorded Future’s December 2025 research on GrayBravo (formerly TAG-150) describes four distinct activity clusters using CastleLoader across multiple industries—logistics, travel/hospitality impersonation, and malvertising/fake installers. The takeaway isn’t just “new malware family.” It’s that malware-as-a-service (MaaS) is now an operations model, and it scales faster than most SOC processes.

This post is part of our AI in Cybersecurity series, and I’m going to take a stance: you won’t keep up with an ecosystem like GrayBravo using rules-first thinking alone. You need AI-driven threat detection and response that can connect weak signals—email content patterns, infrastructure reuse, endpoint behavior, and network anomalies—into one story.

GrayBravo’s real innovation: operational scale, not just malware

GrayBravo isn’t interesting because it has one clever RAT. It’s interesting because the research shows a multi-layered infrastructure and multiple “customers” or affiliates running different campaigns with the same loader.

Here’s what that means in practice:

• CastleLoader acts as the “delivery platform.”
• Second-stage payloads vary by cluster (for example, CastleRAT, Matanbuchus, infostealers, NetSupport RAT).
• Infrastructure is tiered (victim-facing C2 plus deeper tiers that help keep operations resilient).
• The actor is responsive to exposure, changing quickly after public reporting.

This matters because defenders often build detections around a single stable artifact (a domain, a hash, a subject line). GrayBravo’s ecosystem is designed to rotate artifacts while keeping the business running.

AI’s advantage: machine learning models and graph-based analytics don’t require one static indicator to be effective. They can flag campaigns based on relationships: shared hosting patterns, repeated lure mechanics (like ClickFix), and consistent behavioral sequences on endpoints.

Why clusters matter more than “the malware name”

The report identifies four activity clusters using CastleLoader, each with different victim profiles and tactics:

1. TAG-160: logistics impersonation + freight platform abuse
2. TAG-161: Booking.com impersonation + ClickFix + novel phishing mailer tooling
3. Cluster 3: Booking.com impersonation + Steam Community “dead drop” resolver
4. Cluster 4: malvertising + fake software updates + signed MSI installers

Treating these as separate threats is a mistake. The better model is: one shared loader ecosystem feeding multiple monetization paths. That’s exactly where AI-driven threat intelligence and detection pipelines shine.

ClickFix is the tell: a user-execution pattern AI can spot

ClickFix (malicious copy/paste execution) is a consistent theme across clusters. It’s also one of the easiest places to apply AI because it creates a repeatable chain across email → web → endpoint.

Answer first: ClickFix campaigns succeed because they turn the victim into the execution engine, bypassing many attachment-based controls.

In the logistics cluster (TAG-160), victims are guided through a fake “document signing” flow that results in executing a command that silently downloads, extracts, and runs malware (often using pythonw.exe for stealth).

In Booking.com-themed clusters, the same social technique appears: “verify,” “confirm,” “access,” and “guest portal” lures that push users into running PowerShell.

What AI should watch for (instead of one-off signatures)

If you want durable coverage, detect the pattern, not the lure.

Email + web signals (behavioral):

• Sudden spikes in messages referencing “rate confirmation,” “guest verification,” “check-in,” “booking message,” or “portal access” aimed at specific departments
• Links that resolve through unusual redirect chains
• Landing pages that present copy/paste instructions (often with “step-by-step” UI)

Endpoint signals (sequence-based):

• Browser → clipboard activity → PowerShell execution within a short window
• PowerShell launching download + unzip + execution patterns
• pythonw.exe executing from user-writable locations after a web visit
• Defender exclusions being added (notably described in Cluster 3 behavior)

Network signals (anomaly-based):

• New outbound connections to suspicious first-seen domains after a copy/paste execution event
• Hosts contacting multiple C2 servers nearly simultaneously (the report describes redundant CastleRAT C2 behavior)

AI-based detections work well here because they can learn “normal” for a business unit, then flag cross-surface anomalies: an accounts payable workstation suddenly behaving like a script runner; a logistics coordinator browsing a “rate confirmation” link and executing PowerShell; a helpdesk machine pulling MSI installers from new infrastructure.

Cluster spotlight: logistics attacks that blend cybercrime and cargo theft

Answer first: TAG-160 is dangerous because it targets the workflows that keep freight moving—and those workflows create built-in trust.

The research describes TAG-160 impersonating logistics firms (notably England Logistics-themed domains) and abusing freight-matching platforms like DAT Freight & Analytics and Loadlink. The attacker doesn’t just send spam; they build credibility, sometimes starting with benign contact and following up later.

This is the modern playbook in logistics:

• Compromise or spoof an email identity
• Reference real operational concepts (rate confirmations, DPE forms)
• Add urgency (“link expiring”)
• Use a familiar brand element (DocuSign logo)
• Push ClickFix execution and deliver a loader

If you’re defending logistics, this isn’t theoretical. Proofpoint has also reported similar patterns where cyber intrusion supports physical cargo theft. The operational overlap is the point: attackers are monetizing access, not just machines.

Where AI helps logistics teams specifically

Traditional email security helps, but logistics needs more than that. You want AI models that understand:

• Seasonality and workflow rhythms (end-of-quarter freight surges, holiday shipping peaks)
• Language patterns typical of legitimate broker-carrier communication
• Behavioral baselines for dispatch and operations endpoints

Practical examples that work:

• Anomaly detection on sender identity drift: same “company name,” new domain age, new mail routing behavior
• Workflow-aware phishing detection: rate confirmation emails going to roles that don’t handle rate confirmations
• Automated takedown + blocklists: when one lure domain is confirmed, AI can suggest adjacent domains (typosquats, re-registrations) before they’re used

Booking.com impersonation at scale: when attackers build their own mail platform

Answer first: TAG-161 stands out because it pairs impersonation with dedicated campaign tooling—essentially a phishing operations stack.

The research describes Booking.com-themed domains and a previously unreported phishing email management tool with Russian-language titles (for example, “Redirect and Email Manager”). The tooling supports:

• redirect generation
• SMTP configuration
• bulk email distribution
• proxy management
• templating and logging

That’s not a one-off phish. That’s an internal product.

Why this changes detection priorities

When adversaries build tooling like this, they create repetition:

• Similar HTML structures and UI components across panels
• Reused infrastructure patterns (ASNs, hosting ranges)
• Consistent redirect formats and subdomain schemes

This is where AI-assisted threat intel pays off. Instead of waiting for user reports, you can:

• Cluster infrastructure by hosting patterns and TLS/certificate metadata
• Identify “campaign families” based on redirect mechanics
• Detect mail waves using content embeddings (semantic similarity), even when subject lines differ

If your team is still measuring success as “blocked X malicious domains,” you’re undercounting the real win: disrupting the attacker’s ability to run repeatable campaigns.

Steam Community as C2 infrastructure: defenders must expect “weird but legitimate”

Answer first: Cluster 3’s use of Steam Community profiles as a dead drop resolver is a clear sign that attackers are optimizing for agility.

Instead of hard-coding C2 domains permanently, the malware checks a Steam profile to retrieve updated infrastructure. That allows attackers to rotate domains without changing payloads.

This is a practical headache for defenders because Steam is a legitimate service. Blanket blocking may be unacceptable.

AI-based policy: allow the service, flag the behavior

A better approach is conditional trust:

• Baseline which endpoints and user groups normally access Steam
• Flag Steam access from servers or finance endpoints
• Correlate Steam access with suspicious follow-on events (PowerShell execution, new scheduled tasks, Defender exclusion edits, unusual outbound connections)

This is a core theme in AI in cybersecurity: you can’t block every “legitimate internet service” used in attacks, so you detect abnormal use of it.

A practical AI-driven playbook to defend against GrayBravo-style MaaS

Answer first: To defend against MaaS ecosystems, you need detection and response that’s fast, multi-signal, and automated.

Here’s a field-tested checklist you can implement without rewriting your entire security program.

1) Build a “loader chain” detection view (email → web → endpoint)

Unify telemetry so your SOC can see one timeline. At minimum:

• Email gateway logs (sender, domain age, authentication failures)
• Secure web gateway/DNS logs (redirect chains, first-seen domains)
• EDR process trees (PowerShell, pythonw.exe, MSI execution)

Then apply AI correlation to answer: did an email-driven web session produce script execution within 0–30 minutes?

2) Treat ClickFix as a high-confidence behavior

Create detections for:

• Clipboard-to-shell execution patterns
• PowerShell with download + extract + execute sequences
• pythonw.exe launched from unusual paths

Reduce noise by scoping to users who don’t normally run scripts.

3) Use graph analytics for infrastructure clustering

Don’t just block single domains. Cluster by relationships:

• shared hosting ranges and ASNs
• repeated brand impersonation themes (logistics firms, Booking.com)
• redirect format reuse
• admin panel fingerprints (HTML titles, reused assets)

4) Automate containment for “probable loader” events

When the loader pattern triggers, speed matters more than perfect attribution:

• isolate host
• kill suspicious process tree
• revoke active tokens / force password reset (especially for email access)
• hunt for lateral movement signals

5) Operationalize threat intelligence as machine-readable controls

The GrayBravo research provides extensive IoCs and detection logic (Snort, Sigma, YARA). The right move is to integrate them into:

• SIEM correlation rules
• EDR custom detections
• DNS/firewall block policies (with validation + expiry)
• continuous exposure monitoring

AI can help here by recommending which indicators are most relevant to your environment based on observed traffic and asset criticality.

A simple standard: if your threat intel doesn’t become a detection, a hunt, or a block within 72 hours, it’s not operational.

What to do next if you want leads, not just awareness

GrayBravo’s CastleLoader clusters are a clean case study for why AI-driven threat detection has become table stakes: attackers iterate quickly, reuse infrastructure creatively, and run multiple campaigns from the same ecosystem.

If you want to pressure-test your readiness, start with two questions:

1. Can you reconstruct an email-to-endpoint attack chain in minutes, not hours?
2. Can you detect “weird but legitimate” services being used as infrastructure (Paste sites, community profiles, file-sharing platforms) without blocking them outright?

If the honest answer is “not consistently,” you’re not alone. It’s the most common gap I see—and it’s fixable with the right telemetry, correlation, and automation strategy.