Risk Engine
The Raphael Sentinel Risk Engine is the core decision-making system that converts raw analytical signals into a transparent, deterministic risk score for both SPL and bonding-curve tokens. Unlike traditional scanners that rely on generic heuristics, the Risk Engine uses a structured, behavior-aware model tailored specifically to Solana.
Its purpose is simple:
Measure risk in a way that accurately reflects how real attacks and failures happen on Solana.
The engine operates on top of the Analysis Pipeline and transforms its outputs into a single, intelligible score with full reasoning.
1. Deterministic, Transparent Scoring
Raphael Sentinel does not use black-box machine learning. Every value in the score is:
explainable
reproducible
based on on-chain evidence
derived from modular “risk categories”
This ensures that users can trust the score and understand why a token is flagged as risky.
2. Dual Scoring Models (SPL + Bonding Curve)
Because Solana has two fundamentally different token ecosystems, Sentinel maintains two independent models:
SPL Risk Model — for SPL tokens.
Bonding Curve Risk Model — for Pump.fun, LetsBonk, and other similar launches
Each model uses different weightings because the threat surfaces do not overlap.
3. SPL Risk Model
SPL tokens are primarily vulnerable to technical and market-structure risks.
The SPL model evaluates six core dimensions:
1. LP Stability
LP size
lock duration
provider concentration
liquidity manipulation patterns
Weak LP = higher rug risk.
2. Authority Safety
mint authority
freeze authority
update authority
metadata control
Retained authorities = high manipulation potential.
3. Holder Integrity
whale concentration
Gini coefficient
distribution fairness
early holder dominance
Centralization usually predicts dump events.
4. Market Integrity
wash trading
spoofed volume
bot-driven liquidity inflation
Fake activity hides instability.
5. Bot/Wash Trading Detection
MEV bots
snipe bots
wash loops
circulation patterns
Inorganic behavior = unreliable market.
6. Wallet Quality
historical rug participation
dev involvement
prior manipulative behavior
Bad actors → bad outcomes.
All SPL dimensions produce category scores that are aggregated into a 0–100 SPL Risk Score.
4. Bonding Curve Risk Model
Bonding-curve tokens eliminate LP rug risk — but introduce behavioral manipulation as the dominant threat.
Sentinel evaluates four core categories:
1. Dev Cluster Control
How much supply + influence dev wallets hold, including hidden swarm wallets.
2. Pre-Funded Swarm Behavior
Detection of artificial demand created using:
multiple wallets
identical timing
identical sizes
shared funding origin
This is the strongest sign of manipulation.
3. Exit Patterns
dump intensity
dump sequencing
exit velocity
asymmetry between dev exits and retail entries
This identifies coordinated exits.
4. Dev Historical Behavior
Dev reputation matters. If the same cluster launched multiple farm tokens → risk skyrockets.
All categories combine into a 0–100 Bonding Curve Risk Score.
5. Confidence Scoring
Each score includes a confidence level, based on:
data completeness
transaction density
cluster clarity
market age
statistical signal strength
Low confidence does not mean low risk — it means insufficient evidence.
6. Red Flags & Explanation Layer
For every token, Sentinel generates:
explicit red flags
detailed reasoning
evidence
affected categories
severity rating
This removes ambiguity and turns the risk score into an actionable insight.
7. Real-Time Risk Updates
Risk is not static. The engine recalculates in real time when:
dev clusters begin dumping
LP changes (SPL)
swarm wallets join/leave
exit momentum increases
new behavior emerges
This offers a huge edge over static scanners.
8. Final Output
Each token receives:
a final numeric score (0–100)
a risk level (Low, Medium, High, Extreme)
confidence level
categorized breakdown
timeline of risk evolution
red flags with explanations
This output flows into the UI, API, alerts, and subscriptions.
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