SafeCap: Improving LVLM Safety with Image Captioning Reinforcement Learning

Large vision-language models (LVLMs) remain vulnerable to jailbreak attacks that exploit visual inputs to bypass safety alignment inherited from their language backbones. We propose SafeCap, a reinforcement-learning framework that aligns LVLMs through learned self-captioning. SafeCap trains a policy model to first generate a safety-relevant image caption and then produce a final answer; the caption is further optimized by whether it enables a frozen LLM to reach a safety-aligned decision. This c

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ethics.ai (11 August 2026), “SafeCap: Improving LVLM Safety with Image Captioning Reinforcement Learning,” evidence record 18423, https://ethics.ai/record/18423 (originally published by arXiv).

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