Validating Solidity Code Defects using Symbolic and Concrete Execution powered by Large Language Models
This addresses the problem of unreliable vulnerability detection in smart contracts for developers and auditors, though it appears incremental as it combines existing tools with LLMs.
The paper tackles the problem of high false alarm rates in Solidity smart contract vulnerability detection by introducing a pipeline that integrates custom Slither-based detectors, LLMs, Kontral, and Forge to reliably detect defects and generate proofs, demonstrating promising results for seven types of critical defects including Reentrancy, Complex Fallback, and Faulty Access Control Policies.
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.