A Multimodal Large Language Model Approach for Automated Security Requirements Verification
Traditional security auditing and compliance are resource-intensive manual processes. Recent work has applied Large Language Models to automate compliance tasks, but these approaches typically rely on textual inputs, limiting their applicability to real-world audit artifacts. This study proposes a Multimodal Large Language Model (MLLM)-based system for security requirements verification using evidence-grounded analysis of multimodal audit artifacts. The model is constrained to only consider artifacts provided at inference time. Evaluation is conducted through an industrial case study using real audit data, including artifacts, requirements, and ground truth labels. Results show good accuracy and explainability (up to 77%), high consistency, and high recall but lower precision. These findings demonstrate the potential of MLLMs to support automated security requirements verification in practical audit settings.