Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarization
This addresses the problem of recurring vulnerabilities in decentralized finance smart contracts for auditors and developers, offering a systematic approach that is not purely incremental but builds on shared abstractions.
The paper tackles automated vulnerability detection in smart contracts by proposing Knowdit, a knowledge-driven agentic framework that leverages auditing knowledge graphs and multi-agent collaboration, achieving detection of all high-severity and 77% of medium-severity vulnerabilities with only 2 false positives in evaluations.
Smart contracts govern billions of dollars in decentralized finance (DeFi), yet automated vulnerability detection remains challenging because many vulnerabilities are tightly coupled with project-specific business logic. We observe that recurring vulnerabilities across diverse DeFi business models often share the same underlying economic mechanisms, which we term DeFi semantics, and that capturing these shared abstractions can enable more systematic auditing. Building on this insight, we propose Knowdit, a knowledge-driven, agentic framework for smart contract vulnerability detection. Knowdit first constructs an auditing knowledge graph from historical human audit reports, linking fine-grained DeFi semantics with recurring vulnerability patterns. Given a new project, a multi-agent framework leverages this knowledge through an iterative loop of specification generation, harness synthesis, fuzz execution, and finding reflection, driven by a shared working memory for continuous refinement. We evaluate Knowdit on 12 recent Code4rena projects with 75 ground-truth vulnerabilities. Knowdit detects all 14 high-severity and 77\% of medium-severity vulnerabilities with only 2 false positives, significantly outperforming all baselines. Applied to six real-world projects, Knowdit further discovers 12 high- and 10 medium-severity previously unknown vulnerabilities, proving its outstanding performance.