RansomHouse Encryption Upgrade: What AI Can Catch

RansomHouse has logged at least 123 victims since December 2021, and it’s not slowing down. What changed this week isn’t the headline-grabbing double extortion tactic (steal data, encrypt data, threaten to leak). The real shift is quieter and more dangerous: RansomHouse upgraded its encryptor from a simpler “linear” routine to a multi-layer, chunk-based approach designed to be harder to analyze and harder to recover from.

If your ransomware playbook assumes that encryption is a predictable, single-pass process—and that backups will save you—you’re betting against reality. The upgrade is a reminder that ransomware operators iterate like product teams. Defenders have to do the same.

This post is part of our AI in Cybersecurity series, and I’m going to take a stance: AI is no longer “nice to have” for ransomware defense when the malware itself is getting more complex and more evasive. Not because AI magically decrypts files (it doesn’t), but because it’s one of the few practical ways to spot the behavioral patterns that show up before the encryption finishes.

What changed in RansomHouse—and why it matters

Answer first: RansomHouse’s “Mario” encryptor evolved from single-pass encryption to a two-stage, multi-key process with dynamic, chunked (and sometimes sparse) file processing, especially aimed at VMware ESXi environments.

RansomHouse operates as ransomware-as-a-service (RaaS). That split matters:

• Operators run the business: tooling, leak site, negotiation channels, payment infrastructure.
• Affiliates (attackers) break in, move laterally, steal data, then deploy the ransomware.
• Victims get hit twice: operational outage + data exposure.

Why this upgrade is a big deal for defenders:

1. Decryption becomes less feasible. Two keys and multi-stage transforms raise the bar for reverse engineering and key recovery.
2. Recovery becomes less reliable. The malware targets virtualization and backup-related file types (think Veeam artifacts and VM disk/snapshot formats), aiming to cripple the “just restore” plan.
3. Traditional static signals age faster. Hashes and simple signatures help—until attackers recompile, tweak junk code, or modify routines. Behavioral detection becomes the backbone.

The ESXi ransomware pattern: one hypervisor, hundreds of outages

Answer first: RansomHouse affiliates target VMware ESXi because compromising the hypervisor lets them encrypt dozens or hundreds of virtual machines at once, maximizing downtime and pressure.

In practical terms, ESXi-focused ransomware is the enterprise equivalent of cutting the power at the breaker instead of flipping individual light switches.

RansomHouse’s chain (simplified) looks like this:

1) Infiltrate: initial access + lateral movement

Affiliates commonly use spear phishing or exploitation of vulnerable internet-facing systems. Once in, they do the familiar work: recon, privilege escalation, lateral movement, and identifying “high pain” assets.

2) Exfiltrate + deploy: the “MrAgent” to “Mario” handoff

RansomHouse uses a modular approach:

• MrAgent: a deployment/management tool that maintains persistence, runs commands, collects host info, and can disable defenses.
• Mario: the encryptor that targets VM-related files.

A detail that should make any infrastructure team uneasy: MrAgent includes commands to disable the ESXi firewall and can stop services related to management and monitoring. That creates a window where defenders lose visibility right when they need it most.

3) Extort: double pressure, faster decisions

Once data is stolen and encryption has started, the negotiation phase begins. The threat isn’t just operational downtime. It’s regulatory exposure, customer trust, and brand damage.

If you’ve ever watched leadership teams react during a ransomware incident, you know the pattern: the decision window shrinks to hours, and the cost of uncertainty skyrockets.

Inside the “Mario” encryptor upgrade (and what it signals)

Answer first: The upgraded Mario encryptor adds two-stage encryption with a secondary key, more efficient buffer management, dynamic chunk sizing, and selective/sparse encryption behavior—all designed to frustrate analysis and speed up real-world impact.

From a defender’s point of view, the key lesson isn’t the specific algorithmic choices. It’s what the choices imply: ransomware developers are investing in resilience against reverse engineering and incident response.

Two-stage encryption: why “multi-layer” changes recovery odds

Earlier Mario samples used a simpler single-pass transformation. Newer samples introduce a two-stage scheme with:

• A 32-byte primary key generated from random values
• An 8-byte secondary key generated from random values

That secondary key matters. Even if defenders analyze part of the workflow, they still face a second transformation layer. This isn’t about being “unbreakable” in some academic sense; it’s about increasing the time and skill needed to recover—long enough that many organizations choose to pay.

Chunked + dynamic processing: faster, sneakier, harder to reason about

Older encryption logic followed a more linear loop with fixed-size segments and a threshold around 536,870,911 bytes. The upgraded version raises the ceiling to 8 GB and introduces more complex chunk calculations.

The more modern approach includes:

• Variable segment lengths based on file size
• Non-linear chunk ordering (harder to model)
• Sparse encryption (encrypting certain blocks at specific offsets)

Sparse encryption is a classic ransomware tradeoff: it can make a huge file unusable while encrypting less data, which often means encryption completes faster and defenders have less time to stop it.

Output and progress reporting: a clue about operator maturity

The upgraded version prints more detailed “processed chunk” status and richer per-file summaries. That sounds cosmetic, but it’s a sign of operational maturity. Better feedback helps affiliates monitor execution and troubleshoot in messy enterprise environments.

Where traditional ransomware defenses fall short

Answer first: Traditional controls fail when they depend on predictable encryption patterns, static indicators, or delayed human response—especially in hypervisor-wide attacks.

Here are the most common failure points I see in real programs:

Over-reliance on signatures and hashes

Hashes change. Junk code and recompiles are cheap. Static detection still has value, but it’s not a strategy by itself.

Backups that aren’t ransomware-resilient

RansomHouse targets virtualization and backup ecosystem files for a reason. If your backups are:

• reachable from production credentials,
• not immutable,
• not tested for restore speed,

then they’re more of a comforting story than a recovery plan.

Alert fatigue + slow containment

Even a well-instrumented SOC struggles when the environment generates thousands of routine alerts. Ransomware thrives in that noise. Once encryption begins across datastores, manual response becomes a race you often lose.

How AI-driven security spots the upgrade earlier than you think

Answer first: AI helps by detecting pre-encryption behaviors and cross-system anomalies (identity, network, endpoint, hypervisor telemetry) that appear before files are renamed and ransom notes drop.

AI doesn’t “solve encryption.” It solves the harder operational problem: finding the few signals that matter inside a flood of normal activity.

1) Detecting double extortion workflows as behavior, not a label

Double extortion creates a recognizable pattern:

• sudden discovery and staging of sensitive files
• compression activity at odd times
• unusual outbound transfers
• rapid privilege use and lateral movement

AI-based anomaly detection can correlate these into a single incident narrative instead of scattered alerts. That correlation is the difference between “we saw something weird” and “this is an active ransomware play.”

2) Spotting ESXi-specific command sequences

Tools like MrAgent execute telltale operational commands (host identifiers, firewall changes, service manipulation). AI-assisted detections can watch for:

• unexpected esxcli usage, especially firewall disablement
• changes to management services in tight time windows
• suspicious persistence patterns on hypervisors

The key is baselining: most environments have very predictable ESXi admin behavior. Deviations stand out—if you’re looking.

3) Recognizing multi-layer encryption patterns indirectly

Even when encryption becomes more complex (two-stage keys, sparse chunks), the side effects remain measurable:

• abnormal file I/O patterns across datastores
• bursts of write operations to VM disk/snapshot files
• rapid creation of ransom notes across directories
• extension appends (like .emario) at scale

Machine learning models don’t need to know “this is Mario.” They need to know “this looks like hypervisor-wide destructive I/O.”

4) Automating containment when humans can’t move fast enough

When encryption starts, speed wins. AI-guided automation can trigger actions like:

• isolating a hypervisor from the network
• disabling compromised credentials or sessions
• blocking outbound exfiltration paths
• escalating high-confidence incidents to IR with context

Automation is only as good as its guardrails, but a slow manual loop is worse.

A practical ransomware readiness checklist for ESXi-heavy shops

Answer first: If you run ESXi at scale, focus on reducing blast radius, hardening access, and using AI-assisted detections that correlate identity + network + hypervisor behavior.

Here’s what I’d prioritize going into 2026 budgeting and Q1 roadmaps:

1. Lock down hypervisor management paths

   • Separate management networks
   • Restrict admin access to hardened jump hosts
   • Enforce MFA and short-lived privileged sessions

2. Make backups meaningfully resilient

   • Immutable backups (and verify immutability)
   • Separate credentials and networks for backup infrastructure
   • Quarterly restore tests that measure time to recover, not just success

3. Instrument ESXi telemetry and admin behavior

   • Log esxcli usage and configuration changes
   • Alert on firewall disablement and management service stops
   • Baseline “normal” admin actions so anomalies are obvious

4. Use AI to correlate weak signals into strong incidents

   • Tie together identity anomalies + data movement + unusual VM datastore I/O
   • Prioritize high-confidence stories over isolated alerts

5. Pre-stage containment playbooks

   • Decide in advance what “isolate hypervisor” means operationally
   • Practice the decision path with IT + security + leadership

A simple rule: if your first containment discussion happens during encryption, you’re already late.

What to do if you suspect RansomHouse activity

Answer first: Treat it as an active intrusion first (data theft risk), not just a malware event, and prioritize containment + evidence preservation.

Immediate steps that tend to hold up in real incidents:

• isolate suspected systems (especially hypervisors) to stop propagation
• preserve logs and volatile data where possible
• hunt for lateral movement and credential theft
• assume exfiltration until proven otherwise
• engage incident response early, before negotiation pressure sets the pace

The teams that recover fastest aren’t the ones with the fanciest tools. They’re the ones that can make confident decisions quickly.

RansomHouse’s upgrade is a warning—AI is the response

RansomHouse’s encryption upgrade is a clean signal that ransomware operators are investing in complexity: multi-key transforms, dynamic chunking, and tactics built for ESXi-scale disruption. That pushes defenders toward a blunt reality: static controls and slow, manual triage won’t keep up.

AI in cybersecurity earns its place here by doing what humans can’t do fast enough—correlating early-stage anomalies, prioritizing the right incidents, and triggering containment while there’s still time to act.

If you’re responsible for ransomware risk in 2026 planning, here’s the question worth arguing about internally: do you have enough visibility and speed to stop encryption on the hypervisor before the business is negotiating under pressure?