Robust Multimodal Safety via Conditional Decoding

TL;DR

This paper introduces CASA, a simple safety mechanism for multimodal large-language models that significantly reduces attack success rates while maintaining performance on benign inputs.

Contribution

CASA is a novel conditional decoding strategy that enhances safety detection in MLLMs without external classifiers or modality-specific fine-tuning.

Findings

CASA reduces attack success rate by over 97% across multiple modalities.

CASA maintains strong utility on benign inputs, validated by human and automated evaluations.

CASA is effective against diverse attack types and benchmarks.

Abstract

Multimodal large-language models (MLLMs) often experience degraded safety alignment when harmful queries exploit cross-modal interactions. Models aligned on text alone show a higher rate of successful attacks when extended to two or more modalities. In this work, we propose a simple conditional decoding strategy, CASA (Classification Augmented with Safety Attention) that utilizes internal representations of MLLMs to predict a binary safety token before response generation. We introduce a novel safety attention module designed to enhance the model's ability to detect malicious queries. Our design ensures robust safety alignment without relying on any external classifier or auxiliary head, and without the need for modality-specific safety fine-tuning. On diverse benchmarks such as MM-SafetyBench, JailbreakV-28k, and adversarial audio tests, CASA lowers the average attack success rate by more than 97% across modalities and across attack types. Our empirical evaluations also show that CASA maintains strong utility in benign inputs, a result validated through both automated and human evaluations (via 13 trained annotators). Together, these results highlight CASA as a simple and generalizable framework to improve multimodal LLM safety.