Are Vision-Language Models Safe in the Wild? A Meme-Based Benchmark
  Study
Authors: name: DongGeon Lee, name: Joonwon Jang, name: Jihae Jeong, name: Hwanjo Yu

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 LLM-based 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 show greater vulnerability to
meme-based harmful prompts than to synthetic or typographic images. Memes
significantly increase harmful responses and decrease refusals compared to
text-only inputs. Though multi-turn interactions provide partial mitigation,
elevated vulnerability persists. These results highlight the need for
ecologically valid evaluations and stronger safety mechanisms.