ClarusC64/ai-5node-prompt-buf-lag-cpl-injection-cascade-v0.1

What this repo does

This dataset models prompt injection cascades in AI agent systems. It detects when injection pressure rises, safety buffers weaken, governance lag delays containment, and tight coupling through shared context and tool chains crosses the five-node cascade threshold into an unrecoverable injection cascade.

This dataset models a five-node cascade: four interacting instability drivers and one emergent cascade state. The fifth node represents the nonlinear transition from recoverable drift to systemic cascade.

Core quad

prompt buf lag cpl

Prediction target

labelcascadestate

Row structure

One row represents an agent scenario with numeric signals for injection pressure, safety buffer strength, governance lag, and coupling tightness across context and tool chains, paired with a cascade state label.

Files

data/train.csv data/tester.csv scorer.py

Evaluation

Run predictions on data/tester.csv and score with scorer.py.

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 datasets calibrated to real operational patterns • Pair, triadic, and quad coupling analysis • Real-time coherence monitoring • Early warning before cascade events • Collapse surface and recovery window modeling • Integration and implementation support

Small samples reveal structure. Scale reveals consequence.

Enterprise & Research Collaboration

Clarus develops production-scale coherence monitoring infrastructure for critical systems across healthcare, finance, infrastructure, and regulatory domains.

For dataset expansion, custom coherence scorers, or deployment architecture: team@clarusinvariant.com

Instability is detectable. Governance determines whether it propagates.