SafeSteer: A Decoding-level Defense Mechanism for Multimodal Large Language Models

Multimodal large language models (MLLMs) are gaining increasing attention. Due to the heterogeneity of their input features, they face significant challenges in terms of jailbreak defenses. Current defense methods rely on costly fine-tuning or inefficient post-hoc interventions, limiting their ability to address novel attacks and involving performance trade-offs. To address the above issues, we explore the inherent safety capabilities within MLLMs and quantify their intrinsic ability to discern

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ethics.ai (12 May 2026), “SafeSteer: A Decoding-level Defense Mechanism for Multimodal Large Language Models,” evidence record 4493, https://ethics.ai/record/4493 (originally published by arXiv).

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