2023/11/01 by Mohammed Latif Siddiq, Joanna C. S. Santos, Sajith Devareddy +1 · 1 voice
Computer Science · #Advanced Malware Detection Techniques #Security and Verification in Computing #Software Engineering Research #cs.AI #cs.SE
paper · pdf · doi:10.1145/3691621.3694934
arxiv published 2023/11/01 · arxiv updated 2024/09/04 · openalex publication_date 2024/10/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
With the growing popularity of Large Language Models (LLMs) in software engineers' daily practices, it is important to ensure that the code generated by these tools is not only functionally correct but also free of vulnerabilities. Although LLMs can help developers to be more productive, prior empirical studies have shown that LLMs can generate insecure code. There are two contributing factors to the insecure code generation. First, existing datasets used to evaluate LLMs do not adequately represent genuine software engineering tasks sensitive to security. Instead, they are often based on competitive programming challenges or classroom-type coding tasks. In real-world applications, the code produced is integrated into larger codebases, introducing potential security risks. Second, existing evaluation metrics primarily focus on the functional correctness of the generated code while ignoring security considerations. Therefore, in this paper, we described Sallm, a framework to benchmark LLMs' abilities to generate secure code systematically. This framework has three major components: a novel dataset of security-centric Python prompts, configurable assessment techniques to evaluate the generated code, and novel metrics to evaluate the models' performance from the perspective of secure code generation.