PSR2: A Phase-based Semantic Reasoning Framework for Atomicity Violation Detection via Contract Refinement

TL;DR

PSR2 is a new static analysis framework for detecting atomicity violations in smart contracts, combining structural path searching with semantic reasoning to improve accuracy and reduce false positives.

Contribution

It introduces a collaborative analysis approach with modules for structural path search and semantic reasoning, validated on 1,600 contracts with superior performance.

Findings

Achieves 94.69% F1-score on complex ERC-721 contracts.

Reduces false positives by nearly 50% compared to single-module methods.

Outperforms pattern-matching baselines significantly.

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.