Hao Wu 0100

dblp:72/4250-100 · DBLP profile ↗
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4ranked-venue papers
1as first author
4since 2021 · last 2025
0009-0008-2080-6497ORCID · verified

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

Software engineering, systems software and programming languages · 4 · 1 first-author · 4 since 2021
YearPublicationVenuePosition
2025 ETrace : Event-Driven Vulnerability Detection in Smart Contracts via LLM-Based Trace Analysis
abstract
With the advance application of blockchain technology in various fields, ensuring the security and stability of smart contracts has emerged as a critical challenge.Current security analysis methodologies in vulnerability detection can be categorized into static analysis and dynamic analysis methods.However, these existing traditional vulnerability detection methods predominantly rely on analyzing original contract code, not all smart contracts provide accessible code.We present ETrace, a novel event-driven vulnerability detection framework for smart contracts, which uniquely identifies potential vulnerabilities through LLM-powered trace analysis without requiring source code access.By extracting fine-grained event sequences from transaction logs, the framework leverages Large Language Models (LLMs) as adaptive semantic interpreters to reconstruct event analysis through chain-of-thought reasoning.ETrace implements patternmatching to establish causal links between transaction behavior patterns and known attack behaviors.Furthermore, we validate the effectiveness of ETrace through preliminary experimental results.
Chenyang Peng, Haijun Wang 0002, Hao Wu 0100, Ming Fan 0002, Ting Liu 0002
Internetware4
2025 Detecting State Manipulation Vulnerabilities in Smart Contracts Using LLM and Static Analysis
abstract
An increasing number of DeFi protocols are gaining popularity, facilitating transactions among multiple anonymous users.State Manipulation is one of the notorious attacks in DeFi smart contracts, with price variable being the most commonly exploited state variable-attackers manipulate token prices to gain illicit profits.In this paper, we propose PriceSleuth, a novel method that leverages the Large Language Model (LLM) and static analysis to detect Price Manipulation (PM) attacks proactively.PriceSleuth firstly identifies core logic function related to price calculation in DeFi contracts.Then it guides LLM to locate the price calculation code statements.Secondly, PriceSleuth performs backward dependency analysis of price variables, instructing LLM in detecting potential price manipulation.Finally, PriceSleuth utilizes propagation analysis of price variables to assist LLM in detecting whether these variables are maliciously exploited.We presented preliminary experimental results to substantiate the effectiveness of PriceSleuth.And we outline future research directions for PriceSleuth.
Hao Wu 0100, Haijun Wang 0002, Shangwang Li, Ming Fan 0002, Ting Liu 0002
Internetware1
2024 Skyeye: Detecting Imminent Attacks via Analyzing Adversarial Smart Contracts
abstract
Smart contracts are susceptible to various vulnerabilities that can be exploited by hackers via developing adversarial contracts. Existing vulnerability detection techniques often concentrate solely on vulnerable contracts, neglecting adversarial contracts, which may weaken the effectiveness of vulnerability detection and fail to meet practical needs.
Haijun Wang 0002, Yurui Hu, Hao Wu 0100, Dijun Liu, Chenyang Peng, Ming Fan 0002, Ting Liu 0002
ASE3
2024 AdvSCanner: Generating Adversarial Smart Contracts to Exploit Reentrancy Vulnerabilities Using LLM and Static Analysis
abstract
Smart contracts are prone to vulnerabilities, with reentrancy attacks posing significant risks due to their destructive potential. While various methods exist for detecting reentrancy vulnerabilities in smart contracts, such as static analysis, these approaches often suffer from high false positive rates and lack the ability to directly illustrate how vulnerabilities can be exploited in attacks.
Xiaofei Xie, Chenyang Peng, Dijun Liu, Hao Wu 0100, Ming Fan 0002, Ting Liu 0002, Haijun Wang 0002
ASE5