Stress Test Scenario

The scenario specifies the extreme parameters or shocks applied to the system. These are the "what-if" events designed to push the network to its limits. Common examples include:

• Extreme Volatility: A 90% price crash in a core collateral asset.
• Network Congestion: Transaction volume spiking to 10x normal capacity.
• Validator Churn: A sudden, coordinated exit of 40% of network validators.
• Oracle Failure: Critical price feeds freezing or reporting stale data.

The scenario establishes clear, measurable Key Performance Indicators (KPIs) to assess system behavior. Success or failure is not subjective but based on hitting predefined thresholds. Critical metrics include:

• Finality Time: Does block finality exceed acceptable limits (e.g., > 60 seconds)?
• Transaction Throughput: Does the network's TPS drop below a service-level agreement?
• Liquidation Efficiency: In DeFi, are underwater positions liquidated promptly, or do bad debts accumulate?
• State Growth: Does the size of the blockchain state balloon unsustainably?

A robust scenario isolates and tests a specific failure mode or cascade pathway to understand its isolated impact. This is distinct from a general load test. Examples of isolated risks:

• Liquidity Crunch in a Single AMM Pool: Simulating a bank run on a major stablecoin pool.
• Cross-Chain Bridge Delay: Testing the effects of a 12-hour delay in message finality from an L2.
• Smart Contract Gas Spike: Evaluating dApp functionality when the gas cost of a core function increases 100x.

While extreme, the initiating events must be plausible based on historical market events or credible attack vectors. This ensures the test has practical relevance rather than being a purely theoretical exercise. Plausible triggers are drawn from:

• Historical Precedents: The May 2022 Terra/LUNA collapse, the March 2020 Black Thursday flash crash.
• Known Attack Vectors: Eclipse attacks, time-bandit attacks, or governance exploits.
• Macroeconomic Shocks: Central bank policy shifts leading to rapid, correlated asset devaluation.

The scenario defines unambiguous success conditions and failure modes before execution. This removes bias from the analysis. Criteria are binary and protocol-specific:

• Pass: Network maintains liveness; all valid transactions are included within 5 blocks; no double-spends occur.
• Fail: Chain halts (no new blocks for > 100 slots); total value locked in DeFi drops by >50% due to broken mechanisms; a critical smart contract becomes permanently stuck.

Tests the system's capacity limits by simulating peak transaction volumes or maximum block sizes. This reveals bottlenecks in mempool management, block propagation, and state growth. Key metrics include:

• Transactions Per Second (TPS) degradation
• Block finality latency
• Node memory/CPU consumption spikes

Example: Spamming the network with millions of low-fee transactions to test mempool eviction policies.

Simulates adversarial economic conditions to test incentive alignment and validator behavior. Scenarios include:

• Maximum Extractable Value (MEV) auctions causing reorgs
• Slashing condition triggers from conflicting attestations
• Validator churn during rapid stake withdrawal
• Flash loan attacks on protocol economics

This validates the cryptoeconomic security model under profit-driven attacks.

Assumes a split-brain scenario where the network fragments into isolated partitions. This tests consensus safety and liveness guarantees during:

• Finality halts in Proof-of-Stake chains
• Chain reorganizations upon partition healing
• Governance deadlocks for coordinated recovery

The goal is to ensure the protocol can recover consensus without forking under non-malicious network failures.

Models the impact of a significant portion of the network's validators or full nodes going offline simultaneously. This evaluates:

• Finality resilience below the one-third or two-thirds Byzantine fault thresholds
• Block production continuity with reduced participation
• Peer-to-peer (P2P) network's ability to re-route gossip

Example: Simulating a correlated cloud provider outage taking down 40% of network nodes.

Targets the integrity of the world state and historical data. Scenarios include:

• State root mismatches between execution and consensus clients
• RPC endpoint failures under heavy historical data queries
• Pruning or archive node synchronization under corruption
• Worst-case contract storage growth patterns

This ensures data availability and consistency guarantees hold under corruption events.

Tests the process of protocol upgrades and on-chain governance under adversarial conditions. This includes:

• Contentious hard fork simulations with competing client implementations
• Governance proposal spam to test voting mechanisms
• Emergency upgrade procedures under active attack
• Backward/forward compatibility failures

Validates the social and technical coordination layer's resilience.

A stress test scenario is a controlled simulation that subjects a system to extreme but plausible conditions—such as massive price volatility, liquidity crunches, or coordinated attacks—to identify failure points before they occur in production. Its primary purpose is to quantify risk exposure and validate the economic security assumptions of a protocol, ensuring it can withstand black swan events without catastrophic failure.

Effective stress tests measure specific, quantifiable outcomes under duress. Critical metrics include:

• Maximum Drawdown (MDD): The peak-to-trough decline in Total Value Locked (TVL) or protocol equity.
• Liquidity Depth: The ability of Automated Market Makers (AMMs) or lending pools to handle large, imbalanced trades without excessive slippage.
• Solvency Ratios: For lending protocols, the ratio of collateral value to borrowed value across all users during a crash.
• Oracle Resilience: The latency and accuracy of price feeds during volatile periods and potential oracle manipulation attacks.

Standardized scenarios model specific failure modes:

• Flash Crash: A rapid, multi-sigma drop in a major asset's price (e.g., ETH drops 50% in one block), testing liquidation engines and collateral health.
• Liquidity Run: Simultaneous, mass withdrawals from a lending pool or bridge, testing withdrawal queue mechanics and reserve adequacy.
• Governance Attack: A malicious actor acquires a majority of governance tokens to pass a harmful proposal.
• Cross-Protocol Contagion: The failure of a major integrated protocol (like a stablecoin depeg) cascades through the system.

While crucial, stress testing has inherent limitations. It is a model-dependent exercise; the quality of the output depends on the accuracy of the input assumptions and agent behaviors. Scenarios can never perfectly predict novel, emergent systemic risks (unknown unknowns). Furthermore, passing a test may create a false sense of security if the simulated conditions do not match the complexity of real-world adversarial incentives and MEV (Maximal Extractable Value) strategies.

Stress testing intersects with several other security disciplines:

• Economic Security: The capital required to profitably attack a protocol, often assessed via stress tests.
• Scenario Analysis: A broader qualitative assessment of potential future states, of which stress testing is a quantitative subset.
• Sensitivity Analysis: Measuring how changes in a single input variable (e.g., interest rate) affect the system's output.
• Circuit Breakers: Emergency shutdown mechanisms that are often triggered by thresholds identified during stress testing.

A performance test that evaluates a system's behavior under expected peak load conditions. It measures metrics like transactions per second (TPS), latency, and throughput to ensure the system can handle normal operational stress.

• Purpose: Validate capacity and performance under high, but realistic, demand.
• Contrast with Stress Testing: Load testing pushes to expected limits; stress testing goes beyond to find the breaking point.

A specific stress test scenario that models the most severe plausible combination of adverse events to assess a system's resilience. In DeFi, this often involves simulating:

• Liquidity black holes (massive, rapid withdrawals)
• Oracle failure or manipulation
• Cascading liquidations across interconnected protocols
• Extreme market volatility (e.g., 90% price drop in minutes)

The goal is to identify single points of failure and quantify maximum potential loss.

A computational technique used in risk analysis that runs thousands of probabilistic simulations to model the outcome of uncertain variables. In stress testing, it's used to:

• Generate a distribution of potential losses under random market conditions.
• Estimate the Value at Risk (VaR) and Expected Shortfall for a protocol.
• Test smart contract logic against a vast array of randomized input states and transaction sequences to uncover edge cases.

The discipline of experimenting on a system in production to build confidence in its capability to withstand turbulent conditions. While stress testing is often a planned, offline simulation, chaos engineering involves introducing real, controlled failures.

• Examples: Randomly killing nodes, inducing network latency, or simulating validator downtime.
• Goal: To proactively discover systemic weaknesses before they cause an unplanned outage or exploit.

A specialized form of stress testing that models the actions of a malicious actor (black-hat hacker) attempting to exploit the system. This goes beyond economic stress to test security assumptions.

• Common Simulations: Front-running attacks, flash loan arbitrage, governance takeovers, and reentrancy exploits.
• Tools: Often performed by smart contract auditors using fuzzing tools (like Echidna) or formal verification to automatically search for attack vectors.

A key resilience metric identified during stress testing. It defines the maximum acceptable length of time a system can be offline or impaired after a failure before causing unacceptable consequences.

• In Blockchain Context: The target time to restore transaction finality, block production, or bridge operations after a network halt or consensus failure.
• Stress Test Link: Scenarios are designed to measure and validate the RTO under various failure conditions, testing the effectiveness of contingency plans and emergency multi-sigs.