Known Failure Modes
RAX Protocol is designed to improve risk visibility and decision-making, but no system can anticipate or prevent all failure scenarios.
This page outlines known categories of failure modes to help users understand where limitations may arise and how to interpret system outputs under stress.
Extreme Market Events
Certain market events may exceed modeled expectations, including:
Sudden market crashes or spikes
Rapid deleveraging cascades
Correlated liquidations across protocols
Abrupt regime shifts
Under these conditions, historical patterns may lose relevance and risk assessments may degrade in accuracy.
Liquidity Collapse
Liquidity conditions can deteriorate rapidly.
Failure modes include:
Liquidity disappearing faster than models anticipate
Incentive-driven liquidity exiting simultaneously
Slippage exceeding expected bounds
Risk scores and simulations may lag during extreme liquidity contractions.
Smart Contract Exploits
RAX does not control external smart contracts.
Failure modes include:
Undiscovered vulnerabilities
Exploits with no historical precedent
Governance attacks or malicious upgrades
These events may occur without warning and may not be predictable through risk analytics.
Oracle and Data Failures
Risk assessments rely on external data sources.
Failure modes include:
Incorrect or delayed oracle data
Inconsistent pricing across sources
Data outages or partial visibility
Such issues may reduce model confidence or produce distorted signals.
Cross-Chain and Bridge Failures
Cross-chain dependencies introduce additional risk.
Failure modes include:
Bridge exploits
Delayed or halted transfers
Chain-specific outages
Diversification across chains does not fully eliminate shared infrastructure risk.
Model and Assumption Drift
Models are based on assumptions that may become invalid over time.
Failure modes include:
Structural changes in protocols
New financial primitives with limited history
Behavioral shifts not reflected in past data
Model confidence is designed to reflect some of this uncertainty, but drift cannot be eliminated entirely.
AI and Automation Risks
AI-driven systems may exhibit failure modes such as:
Overfitting to recent conditions
Misinterpretation of novel patterns
Reduced performance during regime changes
Human oversight remains essential.
User Configuration Errors
Incorrect configuration may introduce risk.
Examples include:
Excessively permissive risk constraints
Overreliance on automated suggestions
Insufficient alert coverage
RAX provides tools, but configuration discipline is required.
Summary
Known failure modes exist at the market, protocol, infrastructure, model, and user levels.
RAX is designed to surface risk and uncertainty, not to eliminate them. Awareness of these limitations is essential for responsible use.
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