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This paper introduces a leakage-resistant framework, LROO Rug Pull Detector, for early detection of rug-pull schemes in smart contract-based ecosystems. It integrates on-chain behavioral metrics with temporally aligned Open Source Intelligence (OSINT) signals, ensuring features are extracted before liquidity withdrawal to avoid data leakage. The framework utilizes TabPFN for learning from multimodal tabular data and is evaluated on a hand-labeled dataset of 1,000 token projects, demonstrating improved discriminative performance and probability calibration compared to baselines.
Spot rug-pulls before they happen: a new framework combines blockchain data with social media buzz to predict crypto scams with improved accuracy.
Smart contract-based ecosystems enable decentralized applications without trusted intermediaries, but their immutability and permissionless design also facilitate large-scale fraud. One of the most prevalent attacks is the rug pull, where project operators abruptly withdraw liquidity after artificially inflating token value. Existing detection methods primarily rely on reactive on-chain signals and often suffer from temporal data leakage, limiting their real-world reliability. This paper proposes a leakage-aware framework for early rug-pull detection that integrates on-chain behavioral metrics with temporally aligned Open Source Intelligence (OSINT) signals. We construct a hand-labeled dataset of 1,000 token projects, spanning DeFi and non-DeFi settings, with all features extracted strictly prior to any liquidity withdrawal to preserve causal validity. The dataset combines structural on-chain indicators with external attention signals derived from social media activity and search trends. Within this framework, TabPFN is employed as a core modeling component for learning from multimodal tabular data under strict temporal constraints. Experimental results show that the proposed framework achieves strong discriminative performance and improved probability calibration compared to classical baselines, while maintaining low false-negative rates. By framing rug-pull detection as a causal, multimodal forecasting problem, this work emphasizes the necessity of leakage-resilient evaluation and calibrated risk estimation for deployment in blockchain security systems.
