Automated smart contract security analysis treats each detection tool as an independent finding generator and aggregates results by union or intersection. This framing has a fundamental epistemological flaw: individual tools observe partial projections of the contract ecosystem, and no aggregation of partial projections can reconstruct the attacker's full threat model. We propose Multi-Perspective Ensemble Auditing (MPEA), a framework that reframes the auditing problem from finding detection to attacker intent reconstruction. MPEA coordinates 28 specialized tools, each acting as an observer with a distinct analytical perspective (control flow, storage layout, economic behavior, bytecode patterns, flash loan vectors, governance concentration, and 22 additional dimensions). A dedicated LLM reasoning agent — Dragon — receives all 28 reports simultaneously and applies chain-of-thought reasoning over the full cross-contract dependency graph to identify attack paths structurally invisible to any individual tool. Dragon is not the decision-maker; it is a synthesis layer that surfaces corroborated multi-hop attack paths for human review. Evaluated on Damn Vulnerable DeFi (58 vulnerabilities, 13 contract families) and 234 findings from a production audit dataset, MPEA achieves 54% false positive reduction over tool-union aggregation and detects 8 of 8 multi-hop attack paths (k≥3) that no individual tool surfaces.
Alejandro Jaime (Sun,) studied this question.