Validating Solidity Code Defects using Symbolic and Concrete Execution powered by Large Language Models
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915497315926016 |
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| author | Susan, Ştefan-Claudiu Arusoaie, Andrei Lucanu, Dorel |
| author_facet | Susan, Ştefan-Claudiu Arusoaie, Andrei Lucanu, Dorel |
| contents | The high rate of false alarms from static analysis tools and Large Language Models (LLMs) complicates vulnerability detection in Solidity Smart Contracts, demanding methods that can formally or empirically prove the presence of defects. This paper introduces a novel detection pipeline that integrates custom Slither-based detectors, LLMs, Kontrol, and Forge. Our approach is designed to reliably detect defects and generate proofs. We currently perform experiments with promising results for seven types of critical defects. We demonstrate the pipeline's efficacy by presenting our findings for three vulnerabilities -- Reentrancy, Complex Fallback, and Faulty Access Control Policies -- that are challenging for current verification solutions, which often generate false alarms or fail to detect them entirely. We highlight the potential of either symbolic or concrete execution in correctly classifying such code faults. By chaining these instruments, our method effectively validates true positives, significantly reducing the manual verification burden. Although we identify potential limitations, such as the inconsistency and the cost of LLMs, our findings establish a robust framework for combining heuristic analysis with formal verification to achieve more reliable and automated smart contract auditing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13023 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Validating Solidity Code Defects using Symbolic and Concrete Execution powered by Large Language Models Susan, Ştefan-Claudiu Arusoaie, Andrei Lucanu, Dorel Software Engineering Artificial Intelligence I.2.2;D.2.5;D.2.4;D.4.6 The high rate of false alarms from static analysis tools and Large Language Models (LLMs) complicates vulnerability detection in Solidity Smart Contracts, demanding methods that can formally or empirically prove the presence of defects. This paper introduces a novel detection pipeline that integrates custom Slither-based detectors, LLMs, Kontrol, and Forge. Our approach is designed to reliably detect defects and generate proofs. We currently perform experiments with promising results for seven types of critical defects. We demonstrate the pipeline's efficacy by presenting our findings for three vulnerabilities -- Reentrancy, Complex Fallback, and Faulty Access Control Policies -- that are challenging for current verification solutions, which often generate false alarms or fail to detect them entirely. We highlight the potential of either symbolic or concrete execution in correctly classifying such code faults. By chaining these instruments, our method effectively validates true positives, significantly reducing the manual verification burden. Although we identify potential limitations, such as the inconsistency and the cost of LLMs, our findings establish a robust framework for combining heuristic analysis with formal verification to achieve more reliable and automated smart contract auditing. |
| title | Validating Solidity Code Defects using Symbolic and Concrete Execution powered by Large Language Models |
| topic | Software Engineering Artificial Intelligence I.2.2;D.2.5;D.2.4;D.4.6 |
| url | https://arxiv.org/abs/2509.13023 |