REVEAL: Multi-turn Evaluation of Image-Input Harms for Vision LLM

TL;DR

The paper introduces REVEAL, a comprehensive framework for evaluating multi-turn image-input harms in vision LLMs, revealing vulnerabilities and performance differences across models in safety-critical scenarios.

Contribution

It presents a scalable, automated evaluation pipeline specifically designed for multi-turn image-input safety assessment in vision LLMs, addressing limitations of prior single-turn, text-only frameworks.

Findings

Multi-turn interactions increase defect rates in VLLMs.

GPT-4o has the best safety-usability balance among evaluated models.

Misinformation detection remains a critical challenge.

Abstract

Vision Large Language Models (VLLMs) represent a significant advancement in artificial intelligence by integrating image-processing capabilities with textual understanding, thereby enhancing user interactions and expanding application domains. However, their increased complexity introduces novel safety and ethical challenges, particularly in multi-modal and multi-turn conversations. Traditional safety evaluation frameworks, designed for text-based, single-turn interactions, are inadequate for addressing these complexities. To bridge this gap, we introduce the REVEAL (Responsible Evaluation of Vision-Enabled AI LLMs) Framework, a scalable and automated pipeline for evaluating image-input harms in VLLMs. REVEAL includes automated image mining, synthetic adversarial data generation, multi-turn conversational expansion using crescendo attack strategies, and comprehensive harm assessment through evaluators like GPT-4o. We extensively evaluated five state-of-the-art VLLMs, GPT-4o, Llama-3.2, Qwen2-VL, Phi3.5V, and Pixtral, across three important harm categories: sexual harm, violence, and misinformation. Our findings reveal that multi-turn interactions result in significantly higher defect rates compared to single-turn evaluations, highlighting deeper vulnerabilities in VLLMs. Notably, GPT-4o demonstrated the most balanced performance as measured by our Safety-Usability Index (SUI) followed closely by Pixtral. Additionally, misinformation emerged as a critical area requiring enhanced contextual defenses. Llama-3.2 exhibited the highest MT defect rate ($16.55 \%$) while Qwen2-VL showed the highest MT refusal rate ($19.1 \%$).