activity
20232026
most citedPropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation

55 citations · 121 across the 10 of their papers we have counts for

collaborators

11 papers

cs.SE2026

SmartMemory: Detecting On-chain-off-chain Communication Inconsistency for Smart Contract via Memory-based Agent

Zeqin Liao, Yuhong Nan, Henglong Liang +7

Smart contracts underpin decentralized finance, where growing demand for on-chain/off-chain communication(OFC) has driven diverse applications such as cross-chain bridges, real-wor…

cs.CR2026

Knowledge Over Parameters: Evolving Smart Contract Vulnerability Detection

Yuqiang Sun, Han Liu, Ying Li +4

Smart contract vulnerabilities are predominantly logic bugs whose detection requires structured, step-by-step procedural knowledge of attack patterns and contract semantics. Existi…

cs.CR2026

EvoPoC: Automated Exploit Synthesis for DeFi Smart Contracts via Hierarchical Knowledge Graphs

Ruichao Liang, Jing Chen, Xianglong Li +5

Smart contract vulnerabilities in Decentralized Finance caused over billions of dollars losses every year, yet the security community faces a critical bottleneck: identifying a vul…

cs.CR2025

A Large Scale Study of AI-based Binary Function Similarity Detection Techniques for Security Researchers and Practitioners

Jingyi Shi, Yufeng Chen, Yang Xiao +10

Binary Function Similarity Detection (BFSD) is a foundational technique in software security, underpinning a wide range of applications including vulnerability detection, malware a…

cs.CR2024★ 1 cited

SelfDefend: LLMs Can Defend Themselves against Jailbreaking in a Practical Manner

Xunguang Wang, Daoyuan Wu, Zhenlan Ji +7

Jailbreaking is an emerging adversarial attack that bypasses the safety alignment deployed in off-the-shelf large language models (LLMs) and has evolved into multiple categories: h…

cs.SE2024★ 55 cited

PropertyGPT: LLM-driven Formal Verification of Smart Contracts through Retrieval-Augmented Property Generation

Ye Liu, Yue Xue, Daoyuan Wu +4

With recent advances in large language models (LLMs), this paper explores the potential of leveraging state-of-the-art LLMs,such as GPT-4, to transfer existing human-written proper…

cs.SE2024★ 37 cited

Static Application Security Testing (SAST) Tools for Smart Contracts: How Far Are We?

Kaixuan Li, Yue Xue, Sen Chen +6

In recent years, the importance of smart contract security has been heightened by the increasing number of attacks against them. To address this issue, a multitude of static applic…

cs.CR2024★ 1 cited

LLMs Can Defend Themselves Against Jailbreaking in a Practical Manner: A Vision Paper

Daoyuan Wu, Shuai Wang, Yang Liu +1

Jailbreaking is an emerging adversarial attack that bypasses the safety alignment deployed in off-the-shelf large language models (LLMs). A considerable amount of research exists p…

cs.SE2024★ 6 cited

Combining Fine-Tuning and LLM-based Agents for Intuitive Smart Contract Auditing with Justifications

Wei Ma, Daoyuan Wu, Yuqiang Sun +5

Smart contracts are decentralized applications built atop blockchains like Ethereum. Recent research has shown that large language models (LLMs) have potential in auditing smart co…

cs.CR2024★ 19 cited

LLM4Vuln: A Unified Evaluation Framework for Decoupling and Enhancing LLMs' Vulnerability Reasoning

Yuqiang Sun, Daoyuan Wu, Yue Xue +5

Large language models (LLMs) have demonstrated significant potential in various tasks, including those requiring human-level intelligence, such as vulnerability detection. However,…

cs.CL2023★ 2 cited

Split and Merge: Aligning Position Biases in LLM-based Evaluators

Zongjie Li, Chaozheng Wang, Pingchuan Ma +4

Large language models (LLMs) have shown promise as automated evaluators for assessing the quality of answers generated by AI systems. However, these LLM-based evaluators exhibit po…