Rapid deployment of vision-language models (VLMs) magnifies safety risks, yet most evaluations rely on artificial images.This study asks: How safe are current VLMs when confronted with meme images that ordinary users share?To investigate this question, we introduce MEMESAFETYBENCH, a 50,430-instance benchmark pairing real meme images with both harmful and benign instructions.Using a comprehensive safety taxonomy and LLMbased instruction generation, we assess multiple VLMs across single and multi-turn interactions.We investigate how real-world memes influence harmful outputs, the mitigating effects of conversational context, and the relationship between model scale and safety metrics.Our findings demonstrate that VLMs are more vulnerable to meme-based harmful prompts than to synthetic or typographic images.Memes significantly increase harmful responses and decrease refusals compared to textonly inputs.Though multi-turn interactions provide partial mitigation, elevated vulnerability persists.These results highlight the need for ecologically valid evaluations and stronger safety mechanisms.MEMESAFETYBENCH is publicly available at https://github.com/ oneonlee/Meme-Safety-Bench.Warning: This paper includes examples of harmful language and images that may be sensitive or uncomfortable.Reader discretion is recommended.