JANUS: A Difference-Oriented Analyzer For Financial Centralization Risks in Smart Contracts
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 61 references that are in the map. First posted on arXiv 5 Dec 2024.
Builds on
14 of 61 references.
References · 14
- 2017Graph Attention Networkscited by 8,923
- 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
- 2019Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networkscited by 272
- 2019Slither: A Static Analysis Framework For Smart Contractscited by 89
- 2023GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysiscited by 65
- 2018Securify: Practical Security Analysis of Smart Contractscited by 61
- 2023When ChatGPT Meets Smart Contract Vulnerability Detection: How Far Are We?cited by 42
- 2021Combining Graph Neural Networks with Expert Knowledge for Smart Contract Vulnerability Detectioncited by 34
- 2018ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detectioncited by 32
- 2018Finding The Greedy, Prodigal, and Suicidal Contracts at Scalecited by 19
- 2022Behavior-aware Account De-anonymization on Ethereum Interaction Graphcited by 17
- 2022Token Spammers, Rug Pulls, and SniperBots: An Analysis of the Ecosystem of Tokens in Ethereum and in the Binance Smart Chain (BNB)cited by 15
Built on by
Not cited yet.