BlackHartBlackHart
Scores/Methodology
MethodologyOpen & Auditable

How We Score

The BlackHart Risk Index (BRI) is a single number (300-1000) that quantifies how resistant a DeFi protocol is to adversarial exploitation. Higher is safer. Every score is evidence-backed and on-chain verifiable.

300-1000
BRI Range
12
Dimensions
7
Forge Tiers
6h
Refresh Cadence
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Open Methodology

11 of 12 BRI dimensions are fully open. The multiplicative formula, every dimension weight, and the data sources that feed each score are published here. Anyone can independently verify how a protocol's observable behavior maps to its BRI score.

Published
Formula, weights, dimension definitions, Z-Factor, Forge Scale thresholds, weight governance rules
Verifiable
On-chain data, GitHub commits, audit reports, governance transactions, TVL history — every input is observable
Reproducible
Given the published weights and a protocol's observable data, 11 of 12 dimension scores can be independently computed
Committed
Every score update includes an on-chain evidence hash. The provenance chain from behavior to score is auditable
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D7 Adversarial Resilience — methodology proprietary

The adversarial assessment methodology is not published. The same principle that governs responsible vulnerability disclosure — share the impact, not the exploit — applies here. Publishing our offensive tooling would arm the attackers we exist to stop. D7 scores are evidence-backed and on-chain committed, but the methodology that produces them stays behind closed doors. Why this matters →

BRI = 300 + 700 × Π(Dᵢ / 100)^wᵢ

Multiplicative — one catastrophic dimension cannot be hidden by high scores elsewhere

Explore the Methodology

Dimensions at a Glance

Full details →

Forge Scale

Full tiers →
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Reduce exploitable risk

BlackHart Monitoring provides continuous adversarial analysis, vulnerability detection, and remediation support. Scores update when verified risk posture changes.

View Monitoring Plans

View live scores

See the methodology in action — every scored protocol shows its full dimension breakdown.

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