Foundation Models for Code: Accuracy vs. Security Trade-offs

Abstract

Foundation models, such as large-scale pretrained transformers, have demonstrated remarkable capabilities in generating, understanding, and completing source code. These models power a new generation of AIassisted programming tools that boost developer productivity by automating code synthesis, bug fixing, and documentation. However, alongside impressive accuracy in code generation, security concerns arise due to the possibility of generating vulnerable or insecure code snippets inadvertently. This paper investigates the trade-offs between model accuracy and security in foundation models applied to code generation tasks. We first analyze how foundation models like OpenAI Codex, GPT-based models, and other transformer architectures balance code correctness and security. We review datasets used for training and evaluate common vulnerabilities that can be introduced through model outputs, including buffer overflows, injection flaws, and improper authentication. A comprehensive literature review highlights approaches to improving both accuracy and security, such as integrating static analysis tools into the generation pipeline, applying adversarial training with vulnerability datasets, and leveraging domain-specific fine-tuning. Our research methodology involves benchmarking selected foundation models on standard programming tasks while measuring accuracy through functional correctness and security through vulnerability assessments using automated scanning tools. We examine the impact of training data curation and prompt engineering on mitigating security risks without significant loss of code quality. The results reveal a fundamental tension: optimizing models for accuracy often leads to more complex code that may unintentionally introduce security flaws, while emphasizing security constraints can reduce model flexibility and accuracy. The study discusses architectural and training strategies to balance these competing objectives. Concluding, we suggest future directions including hybrid human-AI workflows, improved dataset curation for secure coding, and robust evaluation metrics that consider both functional and security aspects. This work aims to guide practitioners and researchers toward developing foundation models that produce both high-quality and secure code.