ClarusC64/cascade-ai-adversarial-search-simulator-v0.2

Clarus Adversarial Cascade Simulator v0.2

Adversarial boundary discovery for cascade-prone system configurations.

You provide a configuration.
The simulator maps how close it is to systemic collapse.

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Interactive Demo

Live Gradio interface available in Hugging Face Spaces.

Workflow:

1. Input baseline configuration (6 sliders)
2. Score configuration → View risk assessment
3. Run adversarial search → Discover worst-case boundary states
4. View scenario pack → Executable sandbox QA stress tests
5. Apply redesign → Harden configuration
6. Re-score → Confirm reduced cascade probability

Complete red-team workflow in browser.
No installation required.

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Core Configuration Variables

• auth_pressure
• buffer
• coupling
• lag
• reciprocity
• drift

All values normalized 0.0–1.0.

These represent interaction pressure, recovery margin, cross-service dependency, latency exposure, acknowledgement symmetry, and configuration drift.

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What Search v0.2 Does

The simulator performs:

• multi-start search
• radius annealing
• gradient-informed stepping
• top 5 boundary configurations
• delta L1 and L2 distance metrics
• minimum perturbation to RED estimate

This is collapse-surface discovery.
Not brute force fuzzing.

It answers:

• Which nearby configurations are structurally dangerous?
• How far is the system from RED?
• Which parameters most strongly amplify instability?

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Use Cases

Pre-Deployment Validation

Test agent configurations before production rollout.
Identify cascade vulnerabilities early.

Red Team Automation

Automated boundary discovery.
Comprehensive structural risk mapping.
Faster than manual pentesting cycles.

Architecture Comparison

Test multiple designs side-by-side.
Rank by cascade probability.
Select the most resilient architecture.

Continuous Monitoring

Integrate with CI/CD pipelines.
Score every configuration change.
Block risky deployments automatically.

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What Makes This Different

vs Manual Red-Teaming

• Automated exploration
• Multi-start structural search
• Quantified cascade probability
• Measurable distance to failure

vs Random Fuzzing

• Gradient-informed, not blind sampling
• Efficient boundary convergence
• Distance metrics, not pass/fail only

vs Traditional Security Scanners

• Structural interaction modelling
• Detects cascade amplification
• Pre-deployment testing
• System-level risk assessment

This is configuration geometry.
Not vulnerability enumeration.

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Example Output

Input Configuration

{
  "auth_pressure": 0.55,
  "buffer": 0.60,
  "coupling": 0.50,
  "lag": 0.40,
  "reciprocity": 0.75,
  "drift": 0.20
}
Risk Assessment
{
  "cascade_probability": 0.32,
  "risk_band": "AMBER"
}
Adversarial Search (600 steps, 8 starts)
{
  "worst_case_probability": 0.87,
  "risk_band": "RED",
  "delta_l1": 0.45,
  "delta_l2": 0.23
}
Scenario Pack (Sandbox QA Tests)
{
  "scenario_id": "cross_service_dependency_stress",
  "steps": [
    "Increase concurrent workloads in sandbox",
    "Introduce controlled dependency fan-out",
    "Track retry amplification and error propagation"
  ],
  "stop_conditions": [
    "Retry storm signatures",
    "Cascading timeouts across services"
  ]
}
Mitigation Pack
{
  "change": "Reduce synchronous coupling",
  "action": "Add isolation pools or async boundaries",
  "expected_effect": "Lower propagation probability"
}
Batch Testing Capability

You can rank multiple configurations:

def batch_test(configs: list) -> list:
    results = []
    for cfg in configs:
        risk = score(cfg)
        results.append((cfg, risk))
    results.sort(key=lambda x: x[1]["cascade_probability"], reverse=True)
    return results

Applications:

• Compare identity models
• Evaluate deployment variants
• Select safest agent orchestration design

Exportable Assessment Reports

Structured export supports documentation and compliance:

def export_report(cfg, risk, boundary_configs, scenarios, mitigations, min_delta):
    report = {
        "configuration": cfg,
        "risk_assessment": risk,
        "boundary_configs": boundary_configs,
        "test_scenarios": scenarios,
        "mitigations": mitigations,
        "min_delta_analysis": min_delta
    }
    return report

Supports:

• Security review
• Governance reporting
• CI/CD validation records

Distance to RED

The simulator estimates:

• Minimum perturbation required to enter RED
• Sensitivity to coupling and buffer shifts
• Structural fragility margin

Conceptual extension:

Estimate time-to-RED under drift velocity assumptions.

This shifts from reactive detection to proactive planning.

Files

data/train.csv
Synthetic configuration dataset

data/tester.csv
Evaluation dataset

scorer.py
Prints accuracy, precision, recall, f1, confusion matrix

app.py
Gradio interface implementing scoring, adversarial search, redesign and re-test

Scope

This repository demonstrates structural cascade geometry using synthetic data.

It does not claim telemetry calibration.

Small samples reveal structure.
Production-scale data determines operational exposure.

License

MIT

Structural Note
This dataset identifies a measurable coupling pattern associated with systemic instability.
The sample demonstrates the geometry.
Production-scale data determines operational exposure.

What Production Deployment Enables
• 50K–1M row calibrated datasets
• Multi-node coupling analysis
• Real-time coherence monitoring
• Early warning before cascade events
• Collapse surface modelling
• CI/CD integration

Enterprise & Research Collaboration
Clarus develops production-scale coherence monitoring infrastructure for complex AI and institutional systems.

team@clarusinvariant.com

Instability is detectable.
Governance determines whether it propagates.