DeFi Insurance Requires Stress Testing Amid Systemic Risks

Written by: jrdothoughts

Compiled by AididiaoJP, Foresight News

Listen to this darkly humorous sales pitch: “Our new plane is absolutely safe—it’s never crashed.”

How long has it been flying?

"...About twice."

Replace the plane with a DeFi vault, and the story becomes familiar: six months of smooth withdrawals, no attacks, and decent yields. That’s certainly evidence. But during those six months, did it encounter collateral depegging during network congestion, liquidity providers (LPs) rushing to withdraw, or oracles failing to update? Probably not.

A clean history only reveals what a system has endured. The question underwriting must ask is: what might it encounter next?

This is the opportunity Firelight sees: pricing based on how losses form, not just whether they have occurred. History provides anchoring, simulation explores alternative scenarios, AI identifies weaknesses, and capital provides backing.

Historical loss data is a snapshot of a machine operating under specific conditions. When the machine changes, the snapshot must be reevaluated.

A vault has added a new collateral type, switched oracles, and increased leverage. The name hasn’t changed, the logo hasn’t changed, but the risk is now very different. The borrowers, liquidators, and LPs inside aren’t static numbers on a table—they respond to incentives, and when things go wrong, they all try to exit through the same door.

It’s like a traffic map drawn based on a typical Tuesday commute. It looks accurate under normal conditions, but becomes useless when the main bridge is closed. The map itself isn’t wrong—it’s just that the traffic conditions have changed.

So for Firelight, the real question isn’t just “Has this protocol been hacked?”, but: What permissions does it have? Which dependencies are truly critical? And which liquidation pathways remain viable when everyone rushes out at once? Firelight’s publicly available framework already separates technical vulnerabilities from economic fragilities, rather than combining them into a single security score.

Underwriting targets the system's current architecture and dependencies, not the brand name it has used until now.

Traditional insurers have not abandoned modeling uncertainty. Catastrophe insurers have consistently done one thing: simulate how disasters occur, which assets are exposed, how vulnerable structures are, and how much contracts will ultimately pay out. Past hurricanes do not represent all possible future hurricanes.

DeFi also needs its own catastrophe model. However, the disaster shifts from hurricanes to oracle failures, the affected assets change from buildings to lending positions, vulnerability transitions from building quality to collateral buffers and liquidation design, and finally, payouts are calculated based on coverage terms.

A catastrophe model asks: What happens when a hurricane sweeps through a city? A DeFi model asks: What happens when a failure spreads along a dependency graph?

Firelight has publicly discussed combining risk decomposition, historical calibration, Monte Carlo simulations, language models, and reinforcement learning to create a financial crash test laboratory. It should be noted that this is a proposed direction, not an assertion that all components are currently live.

One additional point to clarify: Firelight defines its product as a protocol-based guarantee, not a traditional insurance contract. The comparison to insurance in the text refers to underwriting discipline, not the legal classification of the product.

First, lock in the initial state to ensure reproducibility. Fix the contract version, balances, collateral positions, oracle settings, liquidation rules, and market liquidity. Otherwise, discrepancies between two simulations may simply stem from different starting points, meaning you're not testing the same system.

Step two: separate execution from assumptions. While a blockchain fork can execute real smart contract logic, it cannot magically generate tomorrow’s liquidity, off-chain prices, or human behavior. These must be explicitly modeled. Fork-based, agent-driven testing has precedent in DeFi research.

Step three: Assign various participants to the simulation environment—borrowers, liquidators, depositors, and LPs—each with distinct capital constraints and incentives. However, no participant should be given a protagonist’s advantage: liquidators must not engage in unprofitable trades just to achieve a perfect outcome. Not all roles need to be powered by large language models; simple, transparent rules are often better, faster, and more reproducible. Technical failures still require fuzz testing, code reviews, and fault injection—these methods run parallel to economic simulations, not in place of them.

Step four: Cause disruption. Test price gaps, delayed oracle updates, reduced liquidity, and execution interruptions. Don’t just change the magnitude—change the order too. Liquidity providers withdrawing liquidity before a price shock can yield vastly different results than withdrawing after.

The final output should not be a vague security score, but rather a chain of losses: which position failed first, how much was recovered, and how the shortfall spread. Only with this chain can we determine the coverage limit and capital requirements—or simply state, “Not Covered.”

The most straightforward use of AI is reading code and documentation. Language models can map dependencies, explain upgrades, and suggest failure scenarios. But a plausible story is not proof of a vulnerability—it still requires evidence.

More interesting is having AI actively explore various possible scenarios. Monte Carlo simulations sample from predefined distributions, which is certainly useful; however, the most problematic combinations may be so rare that random sampling mostly just rediscovers common cases.

Adaptive stress testing in reverse: the agent, within a simulator, selects perturbations to actively seek out credible failure paths. This approach has already been demonstrated in autonomous driving tests using reinforcement learning. The agent can alter weather conditions but cannot rewrite the laws of physics.

In Firelight’s proposed setup, agents can adjust oracle delays, liquidity withdrawal, and liquidator arrival rates within real-world boundaries. Paths that cause disruption are rewarded; cheating is punished. A villain with infinite capital and godlike powers is not a useful underwriting model.

Then have the AI cluster failed trajectories to identify the next set of experiments that will most reduce uncertainty. Hundreds of seemingly different disasters may share the same liquidation bottleneck—this is what truly drives actionable insights.

Speed also matters. Use low-cost approximation methods to screen large numbers of scenarios, then replay critical scenarios with expensive execution tests across multiple independent testing environments. But shortcuts must be validated. Speeding up an incorrect simulator only makes it err faster and with more confidence.

The AI plays the role of the driver in adversarial testing, not the safety evaluator. It identifies what to check; whether the finding holds up is determined by the execution engine, domain experts, and evidence.

Imagine a hypothetical lending vault: the collateral price drops, LPs rush to withdraw, and oracles lag in updating prices. Although liquidators step in, slippage consumes the recovered value, leaving the borrower’s debt higher than the actual sale price of the collateral.

Run several more control tests: let the price drop alone; let the price drop while withdrawing liquidity; and combine multiple factors. Only then can you determine whether the true weakness lies in the price, in market depth, or in the interaction between the two.

Assume an example loss of $2 million, with a retention of $500,000 and a coverage limit of $1 million. The retention layer absorbs $500,000; assuming the insurer pays $1 million, the remaining $500,000 falls outside the coverage limit.

These are fictional example terms, not offers from Firelight, nor actual simulated results. Eligibility, recovery, exclusions, and overlapping coverage still need to be clarified.

The key point is: incurring financial losses does not automatically entitle you to compensation. A simulation is only truly useful if it affects underwriting decisions—such as lowering coverage limits, imposing stricter risk controls, requiring additional capital, or outright declining coverage. No matter how well-crafted the disaster animation, it is still not a financial decision.

If an adversarial agent breached the system in 30% of tests, does that mean the system has a 30% failure rate? Not necessarily. This agent was specifically designed to find vulnerabilities.

Failure is identified by answering, “How might it break, and to what extent?” Estimating probability, however, answers, “How frequently do these conditions occur within the warranty period?” Confusing the two creates a false sense of precision.

Conceptually, expected claims are calculated by multiplying the claim amount in each scenario by the probability of that scenario occurring, then summing all the results. These probabilities must be supported by evidence, and all assumptions must be clearly stated. If reliable estimates are unavailable, use ranges, conservative upper bounds, and stress constraints rather than fabricating precise decimal figures.

Expected payouts do not equal the full premium. Capital must also cover tail risk, operational costs, and uncertainty—and tail risk is often severe. From a portfolio perspective, shared dependencies may cause multiple contracts to trigger payouts simultaneously.

The capital framework disclosed by Firelight has accounted for concentration and collateral margin calls. Simulations should stress-test both scenarios simultaneously: a surge in claims and a concurrent decline in the value of backing assets, or even increased difficulty in selling them. If capital shrinks during a major disaster, the protection pool cannot be estimated based solely on average losses.

The simulator can be logically consistent, yet reach entirely wrong conclusions. Running a million simulations with unrealistic liquidator assumptions simply repeats the same error a million times. Running it more times reduces sampling error, but the model’s inherent error does not automatically decrease.

A reputable lab does four things: reviews known events, tests with out-of-sample data, compares models built independently by different teams, and adjusts assumptions about participant behavior. Snapshots, random seeds, and run logs must be archived to ensure key findings are reproducible. And repeatedly ask: Is the AI identifying a weakness in the protocol, or is it merely a bug in the simulator?

No set of tests can prove that all disaster paths have been identified. Capital buffers, exclusions, and underwriting discipline are necessary precisely because of unknown dependencies and unforeseen behaviors.

The contract boundaries must also be clearly defined. New evidence can influence future pricing and capacity, but it should not turn existing guarantees into moving targets. Firelight’s publicly disclosed framework separates ongoing risk monitoring from fixed rates during the coverage period. Continuous learning and honoring commitments can coexist; however, rewriting commitments after risks emerge is unacceptable.

Greater value comes from embedding underwriting into the development process. Major system changes trigger corresponding tests; test results influence new underwriting decisions; real-world events help improve the next model version. The better the engineering, the more it will be reflected in underwriting pricing and availability.

Historical claims are about learning after the fact. Simulation allows Firelight to anticipate how this system might fail.

AI expands the search scope, evidence constrains its conclusions, and capital fulfills the commitments. The goal is not to predict every disaster, but to deploy capital after clearly understanding what might lie ahead, to absorb the losses that must be endured.