AI's ability to understand both images and text has opened exciting new possibilities, but it also raises crucial safety concerns. Imagine an AI that misinterprets a harmless image, leading to harmful actions or the spread of misinformation. This isn't science fiction; it's a real risk with today's advanced Vision-Language Models (VLMs). Researchers are grappling with how to measure and mitigate these “jailbreak” vulnerabilities, where malicious inputs can trick a VLM into generating toxic or harmful outputs. A new research paper introduces "Retention Score," a clever way to quantify these risks. Unlike traditional methods that rely on resource-intensive adversarial attacks, Retention Score leverages existing diffusion models to assess how easily a VLM can be manipulated. This score considers both the visual and textual components, providing a more comprehensive security evaluation. The research reveals some startling findings: adding a visual component to an AI model can actually *decrease* its robustness against attacks, making it more vulnerable to manipulation. This underscores the importance of developing robust security measures as VLMs become increasingly integrated into our lives. While the Retention Score offers a promising path forward, more research is needed to address the evolving landscape of AI safety and security. As VLMs become more powerful, safeguarding them against manipulation is paramount to ensuring responsible and beneficial AI development.