JANUS: A Difference-Oriented Analyzer For Financial Centralization Risks in Smart Contracts
This addresses a critical security issue for blockchain users by providing a more accurate tool to detect centralization risks in smart contracts, though it is an incremental improvement over existing methods.
The paper tackles the problem of detecting financial centralization risks in smart contracts, which can cause losses, by proposing JANUS, an automated analyzer that identifies risks based on state differences rather than predefined patterns, achieving improved accuracy over existing tools on a dataset of 540 contracts and uncovering new risks in a real-world dataset of 33,151 contracts.
Some smart contracts violate decentralization principles by defining privileged accounts that manage other users' assets without permission, introducing centralization risks that have caused financial losses. Existing methods, however, face challenges in accurately detecting diverse centralization risks due to their dependence on predefined behavior patterns. In this paper, we propose JANUS, an automated analyzer for Solidity smart contracts that detects financial centralization risks independently of their specific behaviors. JANUS identifies differences between states reached by privileged and ordinary accounts, and analyzes whether these differences are finance-related. Focusing on the impact of risks rather than behaviors, JANUS achieves improved accuracy compared to existing tools and can uncover centralization risks with unknown patterns. To evaluate JANUS's performance, we compare it with other tools using a dataset of 540 contracts. Our evaluation demonstrates that JANUS outperforms representative tools in terms of detection accuracy for financial centralization risks . Additionally, we evaluate JANUS on a real-world dataset of 33,151 contracts, successfully identifying two types of risks that other tools fail to detect. We also prove that the state traversal method and variable summaries, which are used in JANUS to reduce the number of states to be compared, do not introduce false alarms or omissions in detection.