37 citations · 56 across the 8 of their papers we have counts for
8 papers
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…
TrajAD: Trajectory Anomaly Detection for Trustworthy LLM Agents
Yibing Liu, Chong Zhang, Zhongyi Han +5
We address the problem of runtime trajectory anomaly detection, a critical capability for enabling trustworthy LLM agents. Current safety measures predominantly focus on static inp…
LogicScan: An LLM-driven Framework for Detecting Business Logic Vulnerabilities in Smart Contracts
Jiaqi Gao, Zijian Zhang, Yuqiang Sun +5
Business logic vulnerabilities have become one of the most damaging yet least understood classes of smart contract vulnerabilities. Unlike traditional bugs such as reentrancy or ar…
Do Fine-Tuned LLMs Understand Vulnerabilities? An Investigation into the Semantic Trap
Feiyang Huang, Yuqiang Sun, Fan Zhang +3
Large Language Models (LLMs) have shown promising performance in software vulnerability detection, particularly after domain-specific Supervised Fine-Tuning (SFT). However, it rema…
Doctor: Optimizing Container Rebuild Efficiency by Instruction Re-Orchestration
Zhiling Zhu, Tieming Chen, Chengwei Liu +4
Containerization has revolutionized software deployment, with Docker leading the way due to its ease of use and consistent runtime environment. As Docker usage grows, optimizing Do…
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…
DeepFWI: Identifying Bug-Sensitive Warnings with Multi-Modal Code-Warning Semantics
Han Liu, Jian Zhang, Cen Zhang +7
Static analysis tools have evolved over time to assist in detecting bugs. However, the excessive false warnings can impede developers' productivity and confidence in the tools. Pre…
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,…