With the rapid advancement of large language models (LLMs) in code synthesis, their generated code is increasingly being applied in intelligent foundational software supply chains. These supply chains integrate a large number of third-party modules and components developed using LLM-generated code. However, since LLMs are primarily trained on open-source code, defects and security vulnerabilities within the training data may cause potential errors in the generated code and security risks within the software supply chain. To address these issues, academic research has proposed targeted testing techniques such as EvalPlus. However, these approaches face challenges in covering critical paths in the supply chain due to their reliance on probability-based case generation, which makes it difficult to uncover deep-seated logical software defects. To overcome these limitations, we propose a defect detection method for LLM-generated code in software supply chains that integrates symbolic execution. The method employs a symbolic execution mounting mechanism to automatically identify input parameters in LLM-generated code and perform adaptation and symbolic binding. It then guides the symbolic execution engine to conduct precise constraint analysis on the program’s critical execution paths and generate efficient boundary test cases. These test cases can expose deep logical software defects that traditional methods often fail to detect. We evaluated our approach on 11 high-ranking LLMs from the LMSYS Chatbot Arena, using benchmarks from prominent defect detection frameworks and engines. The results show that our method is more effective at detecting logical defects in LLM-generated code, achieving an average pass rate reduction of 3.99% to 18.98% and coverage enhancement of 3.31% to 8.19% over existing research. These findings highlight the method’s effectiveness in improving the correctness of LLM-generated code and enhancing the security of intelligent foundational software supply chains.