SafeNexus: Discovering and Steering Modality-Universal Safety Neurons in MLLMs

• URL: http://arxiv.org/abs/2607.28969v1
• Date: Fri, 31 Jul 2026 02:45:56 GMT
• Title: SafeNexus: Discovering and Steering Modality-Universal Safety Neurons in MLLMs
• Abstract summary: We introduce SafeNexus, a cross-modal safety alignment framework that adopts a dedicated neuron-level intervention strategy.<n>We identify modality-bound safety neurons (BS-Neurons), and validate their role in regulating safety behavior within each modality.<n>We propose two safety alignment strategies: activation-level safety amplifier and safety neuron calibrator.
• Score: 69.51621286001262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although Large Language Models (LLMs) have demonstrated promising safety performance, extending them to Multimodal Large Language Models (MLLMs) exposes a significant gap between expanded multimodal capabilities and existing safety mechanisms. Current defenses remain predominantly confined to specific modal settings, thereby limiting their robustness against broader cross-modal threats. To bridge this gap, we introduce SafeNexus, a cross-modal safety alignment framework that adopts a dedicated neuron-level intervention strategy. First, we formulate a neuron localization paradigm that identifies functionally specialized neurons by characterizing intermediate-layer activation patterns and quantifying their functional salience through importance scoring. Building upon this paradigm, we exploit contrastive data to identify modality-bound safety neurons (BS-Neurons), and validate their role in regulating safety behavior within each modality via targeted suppression. Further cross-modal analysis defines modality-universal safety neurons (US-Neurons) as the shared subset of BS-Neurons identified across individual modalities, serving as the core for defending against harmful cross-modal attacks. We observe that suppressing these neurons substantially degrades safety performance across modalities, while leaving overall utility largely unaffected. Building on these insights, we propose two safety alignment strategies: activation-level safety amplifier and safety neuron calibrator. The proposed strategies enhance model safety through two distinct routes: the former amplifies the activation magnitudes of US-Neurons, while the latter selectively calibrates them via targeted fine-tuning. Extensive experiments demonstrate that our method outperforms prevailing state-of-the-art approaches on safety benchmarks spanning diverse modality combinations, while effectively preserving utility.

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