AI Defense Against GrayBravo’s CastleLoader MaaS

Most security teams still treat phishing like a volume problem: block bad senders, quarantine attachments, run awareness training, repeat. GrayBravo’s CastleLoader ecosystem shows why that’s not enough anymore.

This operation (first publicly tracked in 2025) isn’t a single campaign—it’s a malware-as-a-service (MaaS) platform with multiple distinct “customers” running different playbooks. Same loader. Different lures, infrastructure, and payloads. That structure is exactly what makes it dangerous… and exactly what makes it a good case study for AI in cybersecurity.

Here’s the practical takeaway: AI is the only scalable way to detect MaaS behavior across email, endpoints, identity, and network telemetry—fast enough to matter. CastleLoader’s clusters are a blueprint for what modern detection has to look like: pattern-based, cross-signal, and adaptive.

What makes CastleLoader a modern MaaS problem

CastleLoader is built to be a “first step” malware layer—get execution, establish command-and-control, then pull down whatever the operator wants next. That matters because it changes your defensive goal.

Instead of asking “Can we detect every payload?” you should ask “Can we detect the loader and the infrastructure behaviors that multiple payloads depend on?” That’s the defensive choke point.

GrayBravo’s ecosystem shows classic MaaS traits:

• Multiple activity clusters using the same loader but different lures and infrastructure
• A large, multi-tier infrastructure (victim-facing nodes plus higher-tier systems)
• A rotating cast of second-stage malware (including infostealers and RATs)
• Rapid iteration when public reporting forces change

The operational reality is that MaaS produces reuse at the platform layer (panels, hosting habits, protocol quirks) and variation at the campaign layer (brands impersonated, victim sectors, landing pages). AI is well-suited to spotting that “reuse + variation” combo.

The technique to watch: ClickFix (copy/paste execution)

Across multiple clusters, CastleLoader is delivered with ClickFix—a social engineering method that convinces a user to copy and paste a command (often PowerShell) into a prompt to “fix” access, verify identity, or view a document.

This is nasty for two reasons:

1. It sidesteps a lot of traditional “malicious attachment” logic.
2. It turns the user into the execution mechanism, which can blur the line between legitimate admin behavior and compromise.

AI-based detection can help here by focusing on behavioral sequences rather than a single indicator: landing page → clipboard activity → shell execution → outbound retrieval → unusual child processes.

Four CastleLoader clusters—and the business risk they represent

Recorded research identified four distinct CastleLoader activity clusters, each with different targeting and tradecraft. The important point for defenders: your industry is not the deciding factor. The deciding factor is whether attackers can credibly imitate your workflows.

Cluster 1 (TAG-160): Logistics lures and freight-platform abuse

This cluster is opinionated and targeted. It impersonates logistics firms, sends freight quote or rate confirmation emails, and uses ClickFix flows styled with familiar branding (including document-signing themes).

The part that should make logistics and supply-chain leaders uncomfortable: the cluster has been observed abusing freight matching platforms and using logistics-specific language and urgency. That’s not generic phishing. That’s process-aware intrusion.

Business impact isn’t limited to malware infection:

• Compromised email threads can trigger fraudulent load coordination
• Stolen credentials can enable account takeover on logistics platforms
• Follow-on access (RAT/RMM tools) can support shipment hijacking scenarios

If you run a 24/7 logistics operation, you already know the hard truth: when a fake rate confirmation hits the right inbox at the wrong time, people click fast.

Cluster 2 (TAG-161): Booking.com impersonation and “mailer manager” tooling

This cluster broadens the target set by using a theme that works across many industries: hospitality and booking workflows. It impersonates Booking.com, uses ClickFix, and has been seen delivering CastleLoader alongside Matanbuchus (a well-known downloader MaaS with high rental pricing in underground markets).

More interesting—and operationally useful for defenders—is the discovery of phishing email management panels (“Email Manager” / “Redirect and Email Manager” / “Booking-Mailer”) used to generate redirects, manage SMTP pools, coordinate proxies, and track sending.

That tooling matters because it creates defensive opportunities:

• Repeatable infrastructure patterns (ports, panel fingerprints, hosting clusters)
• Operational mistakes (credentials, cloud keys, proxy lists exposed)
• Shared domain generation logic (consistent redirect formats)

AI-driven threat intelligence systems are strong at turning these patterns into continuous detections rather than one-time incident notes.

Cluster 3: Booking.com lures plus Steam dead-drop resolving

Cluster 3 also impersonates Booking.com, but appears independent of Cluster 2. Its standout technique: using Steam Community profiles as a dead drop resolver for command-and-control domains.

That’s a modern “living off legitimate internet services” move:

• Malware checks a Steam profile
• The profile contains (directly or indirectly) the current C2 domain
• Operators can rotate infrastructure by updating a profile, not rebuilding malware

This is exactly the kind of technique that punishes static defenses.

A practical stance: if your network monitoring treats Steam as “low priority consumer traffic,” you’re donating concealment. AI-based network anomaly detection can flag unexpected Steam access from server segments, unusual frequency patterns, or endpoints that access Steam only during suspicious process execution windows.

Cluster 4: Malvertising and fake software updates

Cluster 4 is the “scale play.” It uses malvertising and fake installers for legitimate tools (including IT-adjacent software), delivering CastleLoader and remote access tooling.

This is where end-of-year seasonality matters: December is when teams are tired, change windows tighten, and contractors rotate. Fake updates work better when defenders are distracted. Attackers know that.

Defensively, Cluster 4 reinforces a simple rule: treat “downloaded installer + new certificate + silent execution” as a high-risk event—especially when it comes from search ads, lookalike repositories, or newly registered domains.

How AI detects MaaS behavior that rule sets miss

Rules and blocklists still matter. But GrayBravo’s ecosystem is designed to make point-in-time indicators expire quickly. AI detection works best when it focuses on relationships and sequences.

Here are four AI-friendly detection angles that map directly to CastleLoader-style operations.

1) AI-powered phishing detection that understands “workflow impersonation”

Traditional email security overweights obvious markers (bad reputation, suspicious attachments). CastleLoader clusters often rely on contextual credibility—industry language, expected document flows, and time pressure.

AI models can score emails based on:

• Conversation and workflow mismatch (rate confirmation language sent to roles that don’t handle loads)
• Brand/URL inconsistencies (DocuSign-style UI but non-DocuSign domains)
• Urgency and expiry pressure patterns that correlate with prior malicious campaigns
• Typosquatting similarity at scale (englandloglstics-style lookalikes)

The goal isn’t “AI replaces email gateways.” The goal is AI gives you better prioritization—which messages deserve immediate investigation and user isolation.

2) Behavior-based endpoint detection for ClickFix execution chains

ClickFix produces a recognizable chain on endpoints:

1. User opens a browser to a landing page
2. User copies/pastes a command
3. PowerShell or a shell spawns unusual child processes
4. A downloader retrieves and executes content (often from external hosts)
5. Defender exclusions or persistence changes appear

AI-based EDR analytics can catch the shape of that chain even when the command text changes.

If you can only add one control quickly, add high-signal detections for:

• powershell.exe spawned from browsers or Office apps in user context
• Rapid follow-on use of pythonw.exe or script hosts
• Defender exclusion changes correlated with new outbound connections
• Repeated UAC prompts or “prompt flooding” patterns (common in bypass attempts)

3) Network intelligence at the scale MaaS requires

CastleRAT and CastleLoader infrastructure has depth: multiple tiers, redundancy strategies, and clusters that share traits like encryption keys and hosting behavior.

AI helps by correlating:

• New domain registrations that match known naming patterns
• Infrastructure adjacency (sequential IP allocations in the same subnet)
• Recurring hosting ecosystems (bulletproof hosting ASNs, repeated providers)
• Unusual legitimate internet services used as dead drops or staging

Humans can investigate one cluster. AI can monitor the ecosystem continuously and surface the few changes that actually matter.

4) Automated response that’s cautious, not chaotic

A lot of teams fear automation because they’ve seen it break business workflows. That’s fair. The right approach is a tiered response.

For CastleLoader-style threats, automation should do three things well:

• Contain endpoints that match a high-confidence chain (ClickFix + retrieval + suspicious child process)
• Block newly observed infrastructure when it clusters strongly with prior malicious hosting patterns
• Open an investigation package automatically (timeline, parent/child process tree, network destinations, affected identity)

AI is useful here because it can assign confidence scores and reduce false positives. Automation is useful because it buys you time.

A practical defense checklist for security leaders

If you’re building an AI-driven security program (or trying to justify one), CastleLoader is a strong example to use internally because it connects cleanly to real controls.

Here’s what I’d implement first:

1. ClickFix hardening

   • Restrict PowerShell where possible (constrained language mode, script block logging)
   • Alert on browser → PowerShell execution patterns
   • Add user-facing friction for copy/paste into system prompts (policy + tooling)

2. Credential and platform protection (especially for logistics and hospitality)

   • Enforce phishing-resistant MFA for admin and platform accounts
   • Monitor for abnormal logins to freight/booking platforms (new geo, new device, impossible travel)
   • Watch for data exfil signals following infostealer infection

3. Domain and infrastructure monitoring that prioritizes patterns

   • Track typosquats of your brand and key vendors
   • Monitor lookalike domains tied to your operational workflows (rate confirmations, guest verification)
   • Alert when users reach newly registered domains in high-risk categories

4. EDR analytics tuned for loader behavior

   • Detect silent background execution patterns
   • Flag processes that add Defender exclusions during initial infection windows
   • Hunt for staged downloads and execution from unusual external services

5. Incident response playbooks for MaaS

   • Assume second-stage payload diversity (RAT + infostealer + downloader)
   • Build playbooks around capabilities (credential theft, persistence, remote control), not just malware names

Where this is going next—and what to do about it

GrayBravo’s CastleLoader clusters point to a bigger shift: attackers are industrializing initial access while customizing social engineering to match specific industries. The operation scales because it’s MaaS, but it succeeds because the lures match how work actually happens.

For organizations following the AI in Cybersecurity series, this is the throughline: AI isn’t just for faster alerting—it’s for connecting weak signals across systems that humans can’t reasonably correlate in time. MaaS ecosystems depend on that gap.

If you want a clear next step, pick one workflow attackers love to impersonate in your business (document signing, booking verification, freight confirmations, invoice approvals) and run an AI-assisted detection review around it: email signals, endpoint chains, identity anomalies, and network destinations. You’ll learn quickly whether your current stack would spot a CastleLoader-style intrusion before it becomes a business incident.

What would happen in your environment if an attacker didn’t send malware… and instead got your staff to paste it in themselves?