Architectural Vulnerability Mapping and Ethical Risk Taxonomy in Vision-Language Models

Abstract

Vision-language models (VLMs) have demonstrated remarkable capabilities in integrating visual and textual data. However, their increasing deployment in real-world applications highlights critical safety and trustworthiness concerns that necessitate a comprehensive evaluation. This study presents a systematic analysis of the ethical risks and technical vulnerabilities associated with VLM architectures, categorizing them into social biases, security threats, and privacy concerns. We propose a dual-layered framework: first, a comprehensive taxonomy to classify ethical risks, and second, an architectural vulnerability map designed to contextualize potential risk origins within the VLM pipeline (vision encoder, connector, and LLM). Our synthesis suggests that while social biases (e.g., gender and racial stereotypes) often manifest predominantly through the LLM and alignment layers, security threats such as adversarial attacks frequently exploit vulnerabilities within the vision encoder. Furthermore, we evaluate current mitigation strategies, including Reinforcement Learning from Human Feedback (RLHF) and adversarial training, highlighting their effectiveness and limitations. The analysis indicates that achieving ethical VLMs requires a holistic approach that combines data-centric debiasing with robust architectural defenses. This paper provides a roadmap for researchers and developers to build safer and more equitable multimodal artificial intelligence (AI) systems.