RansomHouse Encryption Upgrade: How AI Stops It Early

Ransomware defenders love a clean storyline: attackers break in, drop an encryptor, files turn into gibberish, incident response begins. RansomHouse (run by a group tracked as Jolly Scorpius) is pushing a messier reality—steal data first, then encrypt, then negotiate with proof.

Here’s the part most teams underestimate: when a ransomware crew upgrades encryption, they’re not just “harder to decrypt.” They’re also signaling maturity—better engineering, better operator feedback loops, and a clearer focus on operational speed. Unit 42’s recent analysis of the RansomHouse encryptor (nicknamed Mario) shows exactly that: a move from a simple linear routine to a multi-layered, chunk-based, two-key approach.

This matters because legacy security tools tend to wake up late—often when the encryptor is already running. AI-driven detection, especially behavioral analytics, can shift the timeline earlier: during infiltration, lateral movement, and exfiltration—the phases where attackers can still be stopped with minimal blast radius.

What changed in RansomHouse, and why it’s a big deal

RansomHouse’s upgrade isn’t cosmetic. It’s a practical improvement that makes recovery harder and investigations slower.

Unit 42 identified two Mario versions:

• Original Mario: simpler, single-pass transformation (linear-style processing)
• Upgraded Mario: two-stage transformation with two keys, plus dynamic chunking and selective (sparse) encryption

Two-key encryption raises the recovery bar

The upgraded Mario generates:

• a 32-byte primary key
• an 8-byte secondary key

Then it applies a two-stage transformation—effectively requiring both keys to reverse the damage. Practically, that means defenders should assume:

• fewer crypto implementation mistakes to exploit
• lower odds of a working public decryptor
• more pressure to pay (especially when backups are targeted)

If your ransomware strategy quietly depends on “maybe we’ll decrypt later,” this upgrade is the memo that strategy is dead.

Chunking + sparse encryption is built for speed (and chaos)

Upgraded Mario doesn’t treat files as a simple stream. It uses:

• variable segment lengths
• file-size dependent logic (with an 8 GB threshold in the analyzed logic)
• non-linear chunk processing
• selective block encryption at specific offsets

Sparse encryption is often designed for one goal: break systems fast while minimizing runtime. Encrypt enough of a VM disk and the VM won’t boot—even if much of the file remains unencrypted.

That’s a huge operational advantage for attackers:

• faster impact across more systems
• less time spent on-host (reduces detection opportunities)
• more consistent disruption in virtual environments

The RansomHouse attack chain: where defenders actually have a chance

RansomHouse runs a ransomware-as-a-service model with clear separation of roles:

• Operator: runs the platform, leak site, negotiation infrastructure
• Attacker (affiliate): gains access, moves laterally, steals data, deploys ransomware
• Victim: you

Their attack chain maps to four phases:

1. Develop (operators build/maintain tooling)
2. Infiltrate (initial access + lateral movement)
3. Exfiltrate & deploy (steal data, then drop tooling)
4. Extort (leak threats, negotiation)

If you’re waiting to detect “ransomware encryption” as the primary signal, you’re choosing the worst moment to act.

Why ESXi is the priority target

RansomHouse affiliates are known for targeting VMware ESXi because one hypervisor can host dozens or hundreds of VMs. Encrypting VM files at the datastore layer causes disproportionate disruption:

• web servers, databases, domain controllers fail together
• recovery becomes a coordination nightmare
• even “good” backups may be impacted (especially if backup repos are reachable)

This is one reason ransomware keeps winning in regulated and high-availability sectors like healthcare and government: virtualization concentrates risk.

Meet the tooling: MrAgent (deployment) and Mario (encryptor)

RansomHouse’s modular architecture matters because it creates multiple detection surfaces.

MrAgent is the control plane on ESXi

MrAgent lands first, then maintains connectivity to an attacker-controlled command-and-control server. Documented capabilities include:

• collecting host identifiers (uname -a, NIC/MAC enumeration)
• collecting IP configuration
• disabling ESXi firewall
• executing attacker commands
• kicking off encryption (including changing root password and stopping management services)

From a defender’s perspective, MrAgent is often the better detection opportunity than Mario, because it needs to:

• run system discovery commands
• establish persistence-like behavior
• communicate outbound
• modify system settings and services

Mario is optimized to wreck virtualized infrastructure

Mario targets virtualization and backup-related extensions (examples include vmdk, vmem, vmsd, vmsn, vswp, and Veeam-related formats). It also drops ransom notes per directory and renames encrypted files with extensions containing mario (commonly seen as .emario).

Operationally, that’s a clear intent: break the things that make recovery possible.

Why legacy defenses fail against this style of ransomware

Most “traditional” ransomware detection patterns still lean on:

• known hashes and static signatures
• obvious file I/O spikes tied to full-file encryption
• endpoint-only telemetry (weak visibility on hypervisors and east-west traffic)

RansomHouse’s upgraded approach undermines those assumptions:

1. Two-key + complex processing reduces the usefulness of reverse engineering after the fact.
2. Sparse encryption can reduce the obviousness of “encrypt everything” patterns while still taking systems down.
3. ESXi focus hits a layer where many orgs have weaker EDR coverage and fewer high-fidelity detections.
4. Double extortion means encryption is only half the crisis; the other half is data theft and public pressure.

A blunt take: if your plan is “we’ll detect it when encryption starts,” you’ve already lost the negotiation.

How AI-driven security can catch RansomHouse earlier

AI helps most when it’s used to detect behaviors across phases, not just known malware artifacts. The goal is earlier interruption: block exfiltration, stop lateral movement, isolate the hypervisor, and prevent the encryptor from ever launching.

1) Behavioral anomaly detection across the kill chain

RansomHouse operations create patterns that are weird in combination, even if each action alone looks benign:

• new administrative sessions to ESXi from unusual sources
• bursts of enumeration commands and host inventory collection
• firewall changes on hypervisors
• management service stops at unusual times
• new outbound connections from hosts that normally don’t beacon externally

AI models trained on your baseline can flag these multi-event sequences faster than rules that look for one discrete indicator.

A useful mental model: attackers don’t just perform actions—they perform workflows. AI is good at spotting workflows.

2) Detecting exfiltration before encryption

Double extortion flips the usual IR priority: you can restore systems, but you can’t “restore confidentiality.”

AI can help identify exfiltration by correlating:

• unusual compression activity
• large, sustained outbound transfers
• rare destination infrastructure (new ASN/geo, first-time domain patterns)
• authentication anomalies that precede staging (new service accounts, odd privilege use)

If you stop exfiltration, you reduce the attacker’s leverage—even if encryption happens later.

3) Automated triage that buys back minutes (and minutes matter)

The upgraded Mario is engineered for efficiency. Defenders need the same.

AI-assisted SOC workflows can:

• prioritize alerts that match ransomware precursor chains
• auto-enrich with asset criticality (ESXi hosts should jump to the top)
• recommend containment actions (isolate host, block egress, disable compromised credentials)
• reduce the “spread window” during lateral movement

This isn’t about replacing analysts. It’s about preventing the alert queue from becoming the attacker’s best defense.

A practical defense checklist for ESXi-focused ransomware

If you want a plan that holds up against upgraded ransomware encryption, focus on preventing deployment—not decrypting afterward.

Hardening and access controls (prevention)

• Treat ESXi management interfaces like crown jewels: restrict by network segmentation and allowlists.
• Enforce MFA for all administrative access paths (especially remote).
• Audit for exposed or poorly protected virtualization management services.
• Limit who can stop management agents/services; alert on those actions.

Detection and response (speed)

• Monitor for ESXi command execution patterns tied to discovery and firewall changes.
• Alert on new outbound connections from hypervisors (they should be boring).
• Add detections for ransom note filenames and sudden renaming patterns (e.g., new extensions such as .emario).
• Pre-stage isolation playbooks specifically for hypervisors and backup repositories.

Backup resilience (recovery that actually works)

• Maintain offline/immutable backups that can’t be reached from the production identity plane.
• Test VM recovery at the datastore/cluster level, not just file restore.
• Watch for backup repository authentication anomalies (attackers go after recovery paths early).

“People also ask” questions your team should settle now

Can sparse encryption be recovered more easily?

Not reliably. Sparse encryption often corrupts file structures and metadata in ways that still prevent boot or application integrity. The goal is operational outage, not full data transformation.

If encryption is stronger, does paying become more likely?

Stronger encryption increases pressure, but paying is still a business risk (repeat extortion, unreliable decryption, legal exposure). The real leverage point is preventing exfiltration and stopping deployment.

Where should AI be deployed first?

Start where signal quality is highest: identity telemetry, network flows, and critical infrastructure (like ESXi management). AI is far less useful if it’s fed thin or inconsistent data.

What to do next if you’re worried about RansomHouse-style attacks

RansomHouse’s encryption upgrade is a warning shot: ransomware crews are investing in engineering that makes post-incident recovery harder and negotiations nastier. Teams that rely on legacy detection or “we’ll restore from backups” are betting the company on perfect execution under stress.

A better stance is simple: detect the workflow before the encryptor runs. That means prioritizing AI-driven behavioral detection for infiltration, exfiltration, and hypervisor deployment—plus having containment playbooks that don’t require a committee meeting.

If your ESXi environment lit up with unusual admin sessions tonight, would your SOC recognize it as the start of a ransomware workflow—or just another noisy alert?