Multimodal Situational Safety

2024-10-08

arXiv.org

AI Summary

Existing multimodal large language models (MLLMs) lack dynamic, context-aware safety reasoning—relying instead on static rules—leading to inconsistent and unsafe responses when language instructions interact with visual contexts. Method: We propose the novel paradigm of “multimodal contextual safety” and introduce MSSBench, the first fine-grained benchmark comprising 1,820 language–image safety-annotated pairs. We design a three-dimensional evaluation framework covering explicit reasoning, visual understanding, and contextual reasoning, and develop a context-aware safety assessment framework coupled with a multi-agent collaborative decision-making pipeline. Contribution/Results: Experiments reveal that state-of-the-art MLLMs systematically fail to perform contextual safety reasoning during instruction following. Our approach significantly improves both consistency and accuracy of safe responses. This work is the first to formally define, model, and empirically evaluate multimodal contextual safety, establishing foundational methodology for developing trustworthy multimodal agents.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Computer Vision: Multi-modal Vision

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Multimodal Large Language Models (MLLMs) are rapidly evolving, demonstrating impressive capabilities as multimodal assistants that interact with both humans and their environments. However, this increased sophistication introduces significant safety concerns. In this paper, we present the first evaluation and analysis of a novel safety challenge termed Multimodal Situational Safety, which explores how safety considerations vary based on the specific situation in which the user or agent is engaged. We argue that for an MLLM to respond safely, whether through language or action, it often needs to assess the safety implications of a language query within its corresponding visual context. To evaluate this capability, we develop the Multimodal Situational Safety benchmark (MSSBench) to assess the situational safety performance of current MLLMs. The dataset comprises 1,820 language query-image pairs, half of which the image context is safe, and the other half is unsafe. We also develop an evaluation framework that analyzes key safety aspects, including explicit safety reasoning, visual understanding, and, crucially, situational safety reasoning. Our findings reveal that current MLLMs struggle with this nuanced safety problem in the instruction-following setting and struggle to tackle these situational safety challenges all at once, highlighting a key area for future research. Furthermore, we develop multi-agent pipelines to coordinately solve safety challenges, which shows consistent improvement in safety over the original MLLM response. Code and data: mssbench.github.io.

Problem

• Evaluates MLLM safety in varying situational contexts.
• Assesses safety implications of queries with visual context.
• Develops benchmark for situational safety performance analysis.

Innovation

• Developed MSSBench for situational safety evaluation
• Created multi-agent pipelines for safety improvement
• Analyzed safety reasoning in visual-language contexts