MW Framework — Browser Domain

The Browser Is a Battlefield.
Test Your Defences.

MW Framework for Browser Behavioural Red Teaming. Automatically generates and executes statistically authentic malicious sessions — then measures how well your fraud detection systems actually catch them.

See Attack Chains Request Demo

https://your-app.com/checkout

MW Live Session Analysis — 21 Signals

mouse_movement_entropy0.847

keystroke_timing_variance182ms

paste_event_count7

scroll_depth_ratio0.73

session_entropy0.41

canvas_fingerprintpresent

timezone_consistencyMISMATCH

manifold_distanceHIGH RISK

⚠ Behavioural Clone Detected — Carding Pattern

The Uncomfortable Question

How Do You Know Your Behavioural Defences Actually Work?

Behavioural analytics — mouse movements, keystroke timing, paste events, scroll depth, session entropy — are now the frontline against account takeover, credential stuffing, carding, and automated abuse. But most organisations cannot answer this question.

Process Attacks vs Semantic Attacks — Thieves vs Lawyers analogy

Traditional Red Teams Test Vulnerabilities.
We Test Your Behavioural Defences.

Most red teams focus on network perimeters, APIs, or mobile app binaries. They test for XSS, SQL injection, insecure deserialisation. Important — but they do not test the one defence that modern fraud systems rely on most: behavioural detection.

Generating behaviourally authentic attack sessions at scale has, until now, required a team of human fraudsters. The MW Framework changes that.

Quantify your current behavioural detection rate against AI-generated attack chains
Identify which behavioural features are under-protected
Compare different fraud vendors using the same attack library
Continuously validate fraud models — integrate into CI/CD

Behavioural Attack Chains

The Attacks That Actually Cost You Money

Each attack chain is generated by the MW VAE — jointly realistic across all 21 browser signals — then executed in a real Chrome browser. To your fraud engine, it looks like a legitimate human.

Credential Stuffing

Behaviourally Authentic Login Attack

Our clone moves the mouse naturally, types with human variance, pastes credentials from clipboard, scrolls before submitting. To any behavioural model: a legitimate user who forgot their password.

Leads directly to account takeover & data theft

Account Takeover

Behavioural Fingerprint Imitation

Matches victim's expected timezone, carries a plausible referrer, distributes actions with entropy indistinguishable from human. The fraud engine sees a "normal" login — bypassing velocity rules entirely.

Root cause of payment fraud & data exfiltration

Carding

Payment Fraud Simulation

High paste counts (pasting card numbers), multiple form submits, moderate scrolling, occasional back navigation — as if correcting an error. To your fraud engine: a cautious but legitimate shopper.

Chargebacks, processor fines, merchant termination

Web Scraping

Behaviourally Evasive Bot

Scrolls, hovers, lingers on product pages, occasionally clicks irrelevant links — exactly like a human browsing. Still extracts all protected data: pricing, inventory, customer lists.

Inventory hoarding, competitive intelligence theft

Session Hijacking

Historical Behaviour Imitation

Injects stolen session token into new browser. MW clone mimics original user's historical patterns — mouse curves, typing rhythm, scroll depth — learned from the VAE's latent space. Bypasses MFA entirely.

MFA bypass, full account compromise

The MW Difference

Statistical Authenticity at Scale

Why can we generate these attack chains while traditional red teams cannot? The key insight: browser signals are correlated. The VAE learns the full correlation structure.

Bayesian VAE — 21 Jointly Modelled Signals

Mouse movements, keystroke timing, paste events, scroll depth, session entropy, timezone consistency, canvas fingerprint, referrer patterns — all correlated in the generative model. A naive bot might have high paste counts but low entropy. Our VAE learns the full joint distribution.

Real Chrome Browser Execution

Not a simulated browser. The execution engine controls a real Chrome instance: Bezier curve mouse movements, 100-300ms keystroke delays with occasional typos, clipboard API paste, chunked scroll with back-scrolls, timezone offset overrides. Your fraud engine sees real browser telemetry.

# MW Browser Signal Correlation — VAE Latent Space
# A human who pastes a lot also tends to:
# - submit more forms
# - have moderate session entropy

naive_bot = {
  paste_count:      HIGH,    # detected
  session_entropy:  LOW,     # detected
  mouse_movement:   NONE,    # detected
}

mw_clone = {
  paste_count:      HIGH,    ✓ realistic
  session_entropy:  0.73,    ✓ human range
  mouse_movement:   bezier,  ✓ natural curve
  keystroke_delay:  187ms,   ✓ human variance
  referrer:         plausible,✓ consistent
}

→ Fraud engine detection rate: FAILED
→ All 21 signals jointly realistic
→ Indistinguishable from legitimate user
        

How We Engage

Simulation Before Incident

We work with security, fraud, and product teams at SaaS and e-commerce organisations. Three engagement tiers.

Discovery Briefing

90 minutes — CISOs, Fraud Directors, Heads of Product Security

Live demonstration of the framework against a sandbox of your application. Show live behavioural attack chains. Discuss which current controls would be evaded. No commitment required.

Behavioural Gap Analysis

2-week engagement — Security & Fraud Teams

Custom attack library based on your risk profile. Execute against your UAT fraud stack. Deliver quantified report: detection rates, evasion vectors, and recommended improvements per feature category.

Continuous Validation

Ongoing integration — Engineering Teams

Connect the MW Framework to your fraud testing pipeline. Automated behavioural attack simulations with every major release of your fraud rules or ML models. Continuous assurance.

The Browser Is a Battlefield.Test Your Defences.