Can We Alert Smart-Contract Fraud Early? UniCast for Cross-Phase Multi-Fraud Forecasting

Smart-contract-related fraud caused over US$14 billion in recorded financial losses in 2025 alone. Early warning is therefore crucial for mitigating such losses. However, such early warning remains significantly challenging since complex frauds typically span different phases of smart contracts, while behavioral evidence is inevitably sparse and inconclusive in the early warning stages. Prior methods either do not account for cross-phase inter-dependencies or suffer from limited prediction accuracy under sparse behavioral evidence. Meanwhile, they typically target specific fraud mechanisms, e.g. RugPull, limiting their applicability to diverse real-world fraud patterns. To address these challenges, we propose UniCast, a unified cross-phase framework for early fraud warning across multiple types of smart-contract frauds, including Rug Pull, Honeypot, Selling Restrictions, Fee Abuse, and Minting Abuse. Specifically, Cross-Phase Dependency Modeling first effectively captures and represents static-to-dynamic and dynamic-to-static inter-dependencies through bidirectional contrastive prediction. Then, the Early-Warning Classifier novelly addresses decision ambiguity under limited transaction observation by moving beyond single ambiguous decision space to separately parameterized benign and fraudulent representation spaces, each equipped with multiple subspace-specific anchors to characterize diverse behavioral patterns. Extensive experiments on the Multi-Fraud and RPHunter datasets demonstrate that UniCast achieves 98.1% early-warning accuracy, compared with 78.3% for the strongest baseline, RPHunter, while being 6.47 X faster in inference. We will release our code upon acceptance.