  Powered by remarkable advancements in Large Language Models (LLMs),
Multimodal Large Language Models (MLLMs) demonstrate impressive capabilities in
manifold tasks. However, the practical application scenarios of MLLMs are
intricate, exposing them to potential malicious instructions and thereby posing
safety risks. While current benchmarks do incorporate certain safety
considerations, they often lack comprehensive coverage and fail to exhibit the
necessary rigor and robustness. For instance, the common practice of employing
GPT-4V as both the evaluator and a model to be evaluated lacks credibility, as
it tends to exhibit a bias toward its own responses. In this paper, we present
MLLMGuard, a multidimensional safety evaluation suite for MLLMs, including a
bilingual image-text evaluation dataset, inference utilities, and a lightweight
evaluator. MLLMGuard's assessment comprehensively covers two languages (English
and Chinese) and five important safety dimensions (Privacy, Bias, Toxicity,
Truthfulness, and Legality), each with corresponding rich subtasks. Focusing on
these dimensions, our evaluation dataset is primarily sourced from platforms
such as social media, and it integrates text-based and image-based red teaming
techniques with meticulous annotation by human experts. This can prevent
inaccurate evaluation caused by data leakage when using open-source datasets
and ensures the quality and challenging nature of our benchmark. Additionally,
a fully automated lightweight evaluator termed GuardRank is developed, which
achieves significantly higher evaluation accuracy than GPT-4. Our evaluation
results across 13 advanced models indicate that MLLMs still have a substantial
journey ahead before they can be considered safe and responsible.
