FaultSeeker: LLM-Empowered Framework for Blockchain Transaction Fault Localization
This program is tentative and subject to change.
Web3 applications, particularly decentralized finance (DeFi) protocols, have grown rapidly with over $100 billion locked in smart contracts, attracting sophisticated attacks causing billions in losses. When attack occur, security analysts need to perform fault localization to identify vulnerable functions and understand attack vectors. This critical process currently requires an average of 16.7 analyst hours per incident due to complex blockchain execution models, rapidly evolving protocol interactions, and multi-contract attack patterns that exceed existing analytical capabilities. Despite its critical importance, blockchain fault localization has received limited attention due to fundamental challenges requiring semantic understanding of economic models and protocol-specific logic. Existing blockchain-specific tools target only single vulnerability types, while the only comprehensive solution, DAppFL, relies on machine learning model that may miss sophisticated exploits and lacks interpretability in results. Recent advances in large language models (LLMs) demonstrate remarkable code comprehension capabilities, but existing applications focus on proactive vulnerability detection with minimal exploration of post-incident fault localization.
We present FaultSeeker, an LLM-empowered framework for blockchain transaction fault localization. Inspired by cognitive science memory and attention mechanisms, our two-stage architecture combines transaction-level forensics for strategic scoping with coordinated specialist agents for sustained reasoning. This design provides long-term memory management via orchestrator agents and specialized attention allocation through coordinated workers, enabling comprehensive analysis across complex multi-contract transactions without context loss. We evaluate FaultSeeker on a compiled dataset of 115 real-world malicious transactions with expert-validated annotations spanning diverse attack patterns and complexity levels. Results demonstrate that FaultSeeker significantly outperforms existing approaches, including DAppFL and leading native LLMs (GPT-4o, Claude 3.7 Sonnet, DeepSeek R1), while maintaining practical efficiency (4.4-8.6 minutes) and cost-effectiveness ($1.55-$4.53 per transaction).
This program is tentative and subject to change.
Tue 18 NovDisplayed time zone: Seoul change
11:00 - 12:30 | |||
11:00 10mTalk | FaultSeeker: LLM-Empowered Framework for Blockchain Transaction Fault Localization Research Papers Kairan Sun Nanyang Technological University, Zhengzi Xu Imperial Global Singapore, Kaixuan Li Nanyang Technological University, Lyuye Zhang Nanyang Technological University, Yuqiang Sun Nanyang Technological University, Liwei Tan MetaTrust Labs, Yang Liu Nanyang Technological University | ||
11:10 10mTalk | FlexFL: Flexible and Effective Fault Localization With Open-Source Large Language Models Journal-First Track Chuyang Xu Zhejiang University, Zhongxin Liu Zhejiang University, Xiaoxue Ren Zhejiang University, Gehao Zhang Ant Group, Ming Liang Ant Group, David Lo Singapore Management University | ||
11:20 10mTalk | LLM-Based Identification of Null Pointer Exception Patches Research Papers Tahir Ullah Beijing Institute of Technology, Waseem Akram Beijing Institute of Technology, Fiza Khaliq Beijing Institute of Technology, Hui Liu Beijing Institute of Technology | ||
11:30 10mTalk | SpectAcle: Fault Localisation of AI-Enabled CPS by Exploiting Sequences of DNN Controller Inferences Journal-First Track Deyun Lyu National Institute of Informatics, Zhenya Zhang Kyushu University, Japan, Paolo Arcaini National Institute of Informatics
, Xiao-Yi Zhang University of Science and Technology Beijing, Fuyuki Ishikawa National Institute of Informatics, Jianjun Zhao Kyushu University | ||
11:40 10mTalk | Sifting Truth from Coincidences: A Two-Stage Positive and Unlabeled Learning Model for Coincidental Correctness Detection Research Papers Chunyan Liu Chongqing University, Huan Xie Chongqing University, Yan Lei Chongqing University, Zhenyu Wu School of Big Data & Software Engineering, Chongqing University, Jinping Wang Chonqing University | ||
11:50 10mTalk | Let the Code Speak: Incorporating Program Dynamic State for Better Method-Level Fault Localization Research Papers Yihao Qin , Shangwen Wang National University of Defense Technology, Bo Lin National University of Defense Technology, Xin Peng , Sheng Ouyang National University of Defense Technology, Liqian Chen National University of Defense Technology, Xiaoguang Mao National University of Defense Technology | ||
12:00 10mTalk | Issue Localization via LLM-Driven Iterative Code Graph Searching Research Papers Zhonghao Jiang Zhejiang University, Xiaoxue Ren Zhejiang University, Meng Yan Chongqing University, Wei Jiang Ant Group, Yong Li Ant Group, Zhongxin Liu Zhejiang University | ||
12:10 10mTalk | Hypergraph Neural Network-based Multi-Granular Root Cause Localization for Microservice Systems Research Papers Yaxiao Li Xidian University, Lu Wang Xidian University, Chenxi Zhang Xidian University, Qingshan Li Xidian University, Siming Rong Xidian University, Baiyang Wen Xidian University, Xuyang Li Purdue University, Kun Ma Xidian University, Quanwei Du Xidian University, KeYang Li Xidian University, Lingfeng Pan Xidian University, Xinyue Li Peking University, MingXuan Hui Xidian University | ||
12:20 10mTalk | Explainable Fault Localization for Programming Assignments via LLM-Guided Annotation Research Papers Fang Liu Beihang University, Tianze Wang Beihang University, Li Zhang Beihang University, Zheyu Yang Beihang University, Jing Jiang Beihang University, Zian Sun Beihang University Pre-print | ||