PSR2: A Phase-based Semantic Reasoning Framework for Atomicity Violation Detection via Contract Refinement
Xiaoqi Li, Xin Wang, Wenkai Li, Zongwei Li
TLDR
PSR² is a new framework that uses collaborative static analysis to detect atomicity violations in smart contracts with high accuracy.
Key contributions
- Introduces PSR², a collaborative static analysis framework for smart contract atomicity violation detection.
- Combines Graph Structure Analysis (GSAM) and Semantic Context Analysis (SCAM) for deep contextual awareness.
- Utilizes a Fusion Decision Module (FDM) for formal cross-validation of potential vulnerabilities.
- Achieves 94.69% F1-score in ERC-721 contracts, significantly outperforming existing pattern-matching tools.
Why it matters
Smart contract security is vital for decentralized applications. PSR² significantly improves atomicity violation detection, a major vulnerability, by reducing false positives and increasing accuracy. This makes smart contract auditing more reliable and efficient, enhancing overall blockchain security.
Original Abstract
With the rapid advancement of decentralized applications, smart contract security faces severe challenges, particularly regarding atomicity violations in complex logic such as Oracle and NFT contracts. Rigid rule sets often limit traditional static analyzers and lack deep contextual awareness, leading to high false-positive and false-negative rates when identifying vulnerabilities that depend on intermediate state inconsistencies. To address these limitations, this paper proposes PSR\textsuperscript{2}, a novel collaborative static analysis framework that integrates structural path searching with deterministic semantic reasoning. PSR\textsuperscript{2} utilizes a Graph Structure Analysis Module (GSAM) to identify suspicious execution sequences in control flow graphs and a Semantic Context Analysis Module (SCAM) to extract data dependencies and state facts from abstract syntax trees. A Fusion Decision Module (FDM) then performs formal cross validation to confirm vulnerabilities based on a unified atomicity inconsistency model. Experimental results on 1,600 contract samples demonstrate that PSR\textsuperscript{2} significantly outperforms pattern-matching baselines, achieving an F1-score of 94.69\% in complex ERC-721 scenarios compared to 51.86\% for existing tools. Ablation studies further confirm that our fusion logic effectively reduces the false-positive rate by nearly half compared to single module analysis.
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