Of all the insurance vectors in DeFi, technical exploits are by far the hardest to underwrite. At @Firelightfi , we’ve spent an absurd amount of time wrestling with this problem and attacking it from multiple angles. Think about what it means to insure protocols like Aave, Uniswap, or Lido that have never suffered a major security incident. There is no rich history of “similar” failures to anchor a model to. And unlike more traditional insurance domains, technical risk is extremely protocol-specific: past exploits in other lending markets don’t meaningfully quantify technical risk in Aave, just like a Uniswap bug tells you almost nothing about Lido’s staking code. There is no clean empirical solution to this. But you can get reasonably close with the right structure. At @Firelightfi , we break the problem of technical exploits into three main stages: Risk Decomposition → Risk Modeling → Model Simulation 1) Risk Decomposition First, we decompose each protocol into a very granular set of technical vectors (on the order of 70–80 dimensions) that let us quantify risk beyond “has this been hacked before?”. From there, we extrapolate risk from classes of past exploits that target the same underlying vectors, not just the same protocol category. This only works if you go very deep into the codebase and engineering practices—well beyond reading audit PDFs. Some of the dimensions we look at: Code Quality & Complexity Size/complexity metrics, unsafe patterns, upgrade/proxy architectures, dependency graph hygiene. Audit & Verification Evidence Depth and recency of audits, diversity of auditors, formal methods coverage, outstanding findings and how they were handled. Change Management Release cadence, freeze windows, CI/CD controls, emergency upgrade levers, canary/partial rollouts. Privilege & Key Management Role granularity, timelocks, HSM / MPC custody, operational playbooks, blast radius of key or role compromise. External Dependencies Oracles, bridges, L2 settlement guarantees, third-party libraries, upstream protocol invariants. Runtime Monitoring & Incentives On-chain/invariant monitoring, anomaly detection, bug bounty structure and payouts, response SLAs. Incident & Lineage Record Prior incidents (class, root cause, remediation quality), forked or legacy code lineage, inherited design flaws. This stage is all about turning “vibes” about protocol safety into structured, machine-readable risk vectors. 2) Risk Modeling Once we have the risk decomposition, we build a series of candidate risk models aligned with those vectors. Instead of a single monolithic score, we work with families of models (think: different priors about exploit frequency, severity distributions, dependency failure modes) and calibrate them against: Known exploit histories in structurally similar components Simulated attack paths given the specific architecture Stress scenarios in which multiple vectors degrade at once The idea is not to pretend we can perfectly predict a black-swannish exploit, but to bound the risk in a way that is transparent, composable, and improvable over time. 3) Risk Simulation With model candidates in place, we run thousands of simulations across different market and technical conditions to test how these models behave: How does risk evolve under upgrade churn? What happens if an upstream oracle or bridge degrades? How sensitive is expected loss to a single privileged role being compromised? We’re not trying to produce a magic number. We’re trying to understand where the model breaks, how often, and in which directions—so we can design cover terms, limits, and pricing that reflect reality instead of marketing. How AI Fits In Firelight is AI-first by design, and technical exploit analysis is one of the areas where that actually matters: We use more traditional ML techniques to learn patterns across our 70–80+ risk vectors and how they correlate with historical incidents. We leverage frontier-scale models to read and reason over complex codebases, spotting patterns and anti-patterns that are hard to catch with static rules alone. We rely on simulation methods like Monte Carlo to explore edge conditions and tail scenarios in our candidate models. We apply reinforcement learning–style approaches to iteratively refine model policies and decision thresholds based on simulated outcomes and new data. And that’s just the beginning. There’s a lot more detail behind each of these layers that we’ll share in future posts. For now, the key point is this: technical exploits in DeFi are not “uninsurable”—but they are only insurable if you’re willing to decompose the problem ruthlessly, admit uncertainty, and use every tool (including AI) to narrow the gap between what we don’t know and what we can responsibly underwrite.