SmartPoC: Generating Executable and Validated PoCs for Smart Contract Bug Reports
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Abstract (arXiv)
Smart contracts are commonly audited through static analysis to explore vulnerabilities. However, static approaches typically produce heterogeneous findings rather than reproducible, executable proof-of-concept (PoC) test cases, leading to costly and ad hoc manual validation. Large language models (LLMs) offer a promising way to translate audit reports into PoC test cases, but face three major challenges: noisy inputs, lack of execution grounding, and missing runtime oracles. We present SmartPoC, an end-to-end approach for validating reported vulnerabilities in audit reports by generating and executing PoC test cases with automated exploitability verification. SmartPoC first extracts a focused function-level slice from each report to reduce noise, centering on the key functions referenced in a finding and augmenting them with execution-relevant neighbors. To improve executability, we wrap LLM-based PoC synthesis in a generate-repair-execute loop, combining deterministic pre-execution sanitization with feedback-driven post-execution debugging. We further use differential verification as an oracle to confirm the exploitability of generated test cases. On the SmartBugs-Vul and FORGE-Vul benchmarks, SmartPoC achieves confirmation precision of 98.32% and 98.65%, with recall of 84.17% and 85.28%, respectively. On a recent Etherscan verified-source corpus, SmartPoC confirms 64 bugs from 545 audit findings at an average cost of 0.03.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Nov 2025.
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Citing papers per year: 2026: 5.
5 papers.
- 2026AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detectioncited by 4
- 2026TxRay: Agentic Postmortem of Live Blockchain Attackscited by 2
- 2026Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarizationcited by 1
- 2026PoC-Adapt: Semantic-Aware Automated Vulnerability Reproduction with LLM Multi-Agents and Reinforcement Learning-Driven Adaptive Policycited by 1
- 2026CyberChainBench: Can AI Agents Secure Smart Contracts Against Real-World On-Chain Vulnerabilities?cited by 0
- 2026CyberChainBench: Can AI Agents Secure Smart Contracts Against Real-World On-Chain Vulnerabilities?24 Jun 2026
- 2026AnyPoC: Universal Proof-of-Concept Test Generation for Scalable LLM-Based Bug Detection13 Apr 2026
- 2026PoC-Adapt: Semantic-Aware Automated Vulnerability Reproduction with LLM Multi-Agents and Reinforcement Learning-Driven Adaptive Policy8 Apr 2026
- 2026Knowdit: Agentic Smart Contract Vulnerability Detection with Auditing Knowledge Summarization27 Mar 2026
- 2026TxRay: Agentic Postmortem of Live Blockchain Attacks1 Feb 2026