AI Detection for RansomHouse’s New ESXi Encryption

RansomHouse has at least 123 victims listed since December 2021—and that number matters less than what changed this week: its encryptor got smarter. The group (tracked by some researchers as Jolly Scorpius) upgraded its “Mario” ransomware from a relatively straightforward, linear routine into a more complex, multi-layered encryption process designed to slow analysis and complicate recovery.

Here’s the uncomfortable truth: when encryption gets harder to break, prevention becomes non-negotiable. You don’t want to be the organization arguing about decryption options after your ESXi fleet is down and your backups are also targeted.

This post uses the RansomHouse encryption upgrade as a case study to show where modern ransomware is heading—and why AI섭driven ransomware detection (behavioral, not signature-only) is now the practical way to catch attacks before the last step: mass encryption on hypervisors.

What changed in RansomHouse—and why defenders should care

RansomHouse didn’t just “update code.” It optimized for outcomes: faster impact, harder investigation, and more pressure during negotiations.

At a high level, the operation follows a familiar ransomware-as-a-service pattern:

• Operators run the program (infrastructure, leak site, payment flows, tooling).
• Affiliates/attackers break in, move laterally, steal data, and deploy ransomware.
• Victims get hit with double extortion: data theft + encryption + leak threats.

Where this case stands out is the target: VMware ESXi. Hypervisors are the “one-to-many” choke point. Compromise one ESXi host and you can disrupt dozens or hundreds of VMs in a single move.

The upgrade also signals a broader trend: ransomware developers are acting like product teams.

• They measure what slows defenders down.
• They add complexity where it increases payment probability.
• They invest in features (like chunking, progress output, layered keys) that support reliable encryption at scale.

If your defensive plan assumes ransomware is mostly “a noisy file locker on endpoints,” you’re planning for last decade.

The RansomHouse attack chain in plain terms (and where AI helps)

RansomHouse’s chain maps cleanly into four phases: develop → infiltrate → exfiltrate & deploy → extort. The mistake I see in many environments is treating ransomware as a single event (“encryption happened”) instead of a sequence of detectable behaviors.

Phase 1–2: Infiltrate (access, recon, privilege, lateral movement)

Affiliates typically enter via spear phishing, social engineering, or exploitation of exposed/vulnerable systems. After that, it’s the usual playbook: enumerate the domain, identify backup servers, map virtualization infrastructure, collect credentials, and expand privileges.

Where AI earns its keep here: human teams can’t reliably baseline every identity and machine. AI-assisted detection can flag:

• Anomalous authentication paths (new geo/time/device combos)
• Privilege escalation sequences that don’t match the user’s historical pattern
• Lateral movement bursts (host-to-host, service account reuse)
• New tooling patterns (even if the exact binaries differ)

This isn’t about “AI that knows ransomware.” It’s about AI that knows your environment well enough to spot when behavior turns operationally malicious.

Phase 3: Exfiltrate & deploy (MrAgent + Mario)

RansomHouse uses a modular setup:

• MrAgent: a deployment/management tool that maintains persistence on ESXi and communicates with a C2 server.
• Mario: the encryptor that targets VM and backup-related files.

MrAgent’s behaviors are especially defender-relevant because they’re operational actions that often happen right before encryption:

• Host identification and network enumeration
• IP discovery on ESXi
• Disabling the ESXi firewall
• Receiving remote instructions (including arbitrary command execution)

AI detection value: this is classic “rare but high-risk” activity. Many organizations have some admin activity on ESXi, but far fewer have patterns like:

• Repeated esxcli interrogation sequences at odd hours
• Firewall disable commands on multiple hosts in a short window
• Remote management tampering (services stopped, config overwritten)

When you model these sequences as behavioral chains, you can trigger response before Mario runs.

Phase 4: Extort (double extortion pressure)

Encryption is only half the squeeze. The other half is stolen data and the threat of publication. That changes what “containment” means: you’re not only restoring systems; you’re managing a data exposure event.

AI can’t negotiate for you, but it can reduce how often you end up negotiating by improving the odds you:

• Detect exfiltration patterns earlier
• Contain before staging servers fill up
• Disrupt outbound transfers and unusual compression/archiving flows

Inside the Mario upgrade: what “more complex encryption” really means

The biggest misconception about ransomware encryption upgrades is thinking the only impact is “harder decryption.” That’s part of it. The more important impact is that complexity often comes with speed, resilience, and defensive friction.

Based on public technical reporting, two versions of Mario stand out:

• Original Mario: simpler, single-pass encryption with more linear file processing.
• Upgraded Mario: multi-stage transformation, more structured memory usage, chunking with dynamic sizing, and selective encryption behavior.

Two-stage encryption keys: designed to resist analysis

Upgraded Mario uses two keys:

• A 32-byte primary key
• An 8-byte secondary key

That design increases the work required for defenders attempting to reconstruct decryption logic without cooperation. More importantly, it signals intent: the developers expect deeper analysis and are raising the cost.

Defender implication: don’t bank on “we’ll figure out decryption later.” Your win condition is earlier: stop the process from ever writing encrypted output.

Chunk processing + dynamic sizing: faster, stealthier, and harder to reason about

Original Mario processes files in more predictable fixed-size segments and uses a size threshold around 536,870,911 bytes for certain logic changes.

Upgraded Mario shifts to:

• Variable segment lengths
• A larger size threshold (reported as 8 GB)
• Sparse encryption: encrypting specific blocks at offsets instead of every byte

Sparse encryption is nasty in virtual environments. It can be enough to break VM functionality while reducing total time spent encrypting—meaning defenders have less time to detect and interrupt the process.

Output format and progress reporting: operational maturity

Upgraded Mario provides more detailed “processed chunk” progress and richer per-file completion output.

That sounds cosmetic, but it hints at real-world use: affiliates want tooling that’s reliable during chaotic operations. Better progress reporting means fewer failed runs, fewer corrupted states, and faster extortion timelines.

Why AI-driven ransomware detection beats signature-only controls here

Signature-based tools still matter, but they struggle when:

• Binaries are obfuscated with junk code
• Deployers execute native commands (esxcli, service stops)
• Encryption methods change faster than rule updates

AI-driven threat detection shines when you focus it on behavioral invariants—things attackers must do regardless of their encryption internals.

Here are the invariants in this case:

1. Hypervisor reconnaissance: repeated ESXi host queries and inventory-style commands
2. Security control tampering: firewall disables, remote management disruption
3. Command-and-control patterns: persistent external comms from infrastructure that normally shouldn’t talk out
4. Burst operations: rapid, repeated changes across multiple ESXi hosts
5. File operation anomalies: sudden spikes in writes/renames on vmdk, vswp, vmsn, vbk, and related extensions

A practical stance: detection should focus on the run-up, not the ransom note.

If you’re detecting ransomware when the ransom note appears, you’re measuring failure—just faster than your competitors.

A defensive checklist for ESXi ransomware (what I’d do first)

If you’re responsible for virtualization security and ransomware resilience, these steps give you outsized risk reduction quickly.

1) Baseline ESXi “normal” so anomalies stand out

You can’t detect abnormal behavior without a definition of normal.

• Track typical admin login times and source hosts
• Record common esxcli command patterns used by your team
• Flag any ESXi host with unexpected outbound network connectivity

AI-assisted baselining is useful here because ESXi environments are noisy—and humans are terrible at spotting slow drift.

2) Treat ESXi management as a high-sensitivity zone

• Restrict management access to dedicated admin jump hosts
• Enforce strong identity controls for admin accounts (MFA where supported, strict PAM workflows)
• Segment ESXi management networks and restrict outbound egress

RansomHouse’s play relies on reaching ESXi and then operating freely. Make that hard.

3) Detect “pre-encryption” actions, not just encryption

Create detections for:

• ESXi firewall disable events
• Stopping remote management services
• Unexpected changes to root credentials
• New persistence artifacts or unknown binaries on hypervisors

AI is especially good at correlating these as a sequence. A single event might be admin work. A sequence is an attack story.

4) Make backups harder to sabotage

Because Mario targets virtualization files and Veeam-related artifacts (vbk, vbm), assume backups are in scope.

• Use immutable backups where feasible
• Separate backup credentials from day-to-day admin credentials
• Monitor backup repository access patterns (especially mass delete/rename)

5) Practice the decision tree before you need it

Ransomware response is mostly decision latency.

• Define what triggers “containment now” vs “investigate first”
• Pre-approve disruptive actions (isolating ESXi hosts, blocking egress)
• Run a tabletop specifically focused on hypervisor-level ransomware

If you wait for perfect certainty, encryption wins.

Quick Q&A: what security leaders ask about this trend

Does stronger encryption mean we’re doomed if it hits?

No—but it means your recovery plan must assume decryption won’t be an option. Plan for restore, rebuild, and data exposure handling.

Why is ESXi such a popular target?

Because it’s high leverage. One hypervisor can host many critical systems. Attackers get maximum downtime per compromised credential.

Where does AI fit without becoming “another tool”?

AI works when it’s tied to specific operational decisions: isolate this host, block this egress, disable this credential path, open this incident. If AI output doesn’t map to actions, it becomes dashboard noise.

What to do next if you’re serious about stopping ransomware earlier

RansomHouse’s Mario upgrade is a reminder that attackers iterate faster than most security programs. They’re optimizing encryption routines, deployment tooling, and extortion workflows—especially against virtual infrastructure where blast radius is huge.

The better approach is to shift detection earlier in the chain: identity anomalies, ESXi management tampering, unusual outbound comms, and behavioral signs of mass deployment. That’s exactly the zone where AI-driven ransomware detection is strongest.

If your team had to answer this honestly—would we detect the MrAgent stage before Mario starts encrypting?—what would you say?