Xingshuang Lin

dblp:402/5929 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · unresolved

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Software testing · 67% Program verification · 33%
Network and information security
1 paper
Blockchain and cryptocurrency security · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Blockchain and cryptocurrency security
smart contract security
0.912025
PromFuzz: Leveraging LLM-Driven and Bug-Oriented Composite Analysis for Detecting Functional Bugs in Smart Contracts · ASE 2025
Software testing
fuzzing
0.912025
PromFuzz: Leveraging LLM-Driven and Bug-Oriented Composite Analysis for Detecting Functional Bugs in Smart Contracts · ASE 2025
Program verification
invariant generation
0.912025
PromFuzz: Leveraging LLM-Driven and Bug-Oriented Composite Analysis for Detecting Functional Bugs in Smart Contracts · ASE 2025
Software testing › fuzzing
smart contract fuzzing
0.912025
PromFuzz: Leveraging LLM-Driven and Bug-Oriented Composite Analysis for Detecting Functional Bugs in Smart Contracts · ASE 2025

Methods — techniques the papers use, named apart from their topics

prompt engineering · 1.7large language model · 1.7dual-agent analysis · 1.7
YearPublicationVenuePosition
2025 PromFuzz: Leveraging LLM-Driven and Bug-Oriented Composite Analysis for Detecting Functional Bugs in Smart Contracts
abstract
Smart contracts are fundamental pillars of the blockchain, playing a crucial role in facilitating various business transactions. However, these smart contracts are vulnerable to exploitable bugs that can lead to substantial monetary losses. A recent study reveals that over 80% of these exploitable bugs, which are primarily functional bugs, can evade the detection of current tools. Automatically identifying functional bugs in smart contracts presents challenges from multiple perspectives. The primary issue is the significant gap between understanding the high-level logic of the business model and checking the low-level implementations in smart contracts. Furthermore, identifying deeply rooted functional bugs in smart contracts requires the automated generation of effective detection oracles based on various bug features.To address these challenges, we design and implement PromFuzz, an automated and scalable system to detect functional bugs in smart contracts. In PromFuzz, we first propose a novel Large Language Model (LLM)-driven analysis framework, which leverages a dual-agent prompt engineering strategy to pinpoint potentially vulnerable functions for further scrutiny. We then implement a dual-stage coupling approach, which focuses on generating invariant checkers that leverage logic information extracted from potentially vulnerable functions. Finally, we design a bug-oriented fuzzing engine, which maps the logical information from the high-level business model to the low-level smart contract implementations, and performs the bug-oriented fuzzing on targeted functions. We evaluate PromFuzz from 4 perspectives on 5 ground-truth datasets and compare it with multiple state-of-the-art methods. The results show that PromFuzz achieves 86.96% recall and 93.02% F1-score in detecting functional bugs, marking at least a 50% improvement in both metrics over state-of-the-art methods. Moreover, we perform an in-depth analysis on 10 real-world DeFi projects and detect 30 zero-day bugs. Our further case studies, the risky first deposit bug and the AMM price oracle manipulation bug on real-world DeFi projects, demonstrate the serious risks of the exploitable functional bugs in smart contracts. Up to now, 24 zero-day bugs have been assigned CVE IDs. Our discoveries have safeguarded assets totaling $18.2 billion from potential monetary losses.
Xingshuang Lin, Qinge Xie, Yuan Tian 0001, Saman A. Zonouz, Na Ruan, Raheem A. Beyah, Shouling Ji
ASE1