Imagine a world where a seemingly innocent image could trick an AI into giving harmful or misleading information. That's the potential threat of "jailbreaking" attacks on Multimodal Large Language Models (MLLMs). These advanced AIs, capable of understanding both text and images, are vulnerable to subtle manipulations in visual inputs. New research explores how malicious users can exploit these vulnerabilities, crafting images that disrupt the AI's safety mechanisms. The study introduces CIDER, a clever tool designed to spot these malicious images. CIDER acts like a security guard, checking incoming images for signs of tampering before they reach the AI. It leverages the connection between harmful text prompts and manipulated images, spotting discrepancies that suggest an attack. Testing CIDER on various MLLMs showed promising results. It successfully detected a significant percentage of adversarial images, proving its potential as a robust defense. CIDER also stands out for its efficiency, adding minimal processing time compared to other defense methods. While CIDER represents a significant step toward securing MLLMs, the research also highlights the delicate balance between safety and maintaining the AI's overall performance. The strict nature of CIDER, which rejects potentially harmful images, can somewhat reduce the AI’s effectiveness on normal tasks. Future research will focus on fine-tuning this balance to ensure robust security without hindering the AI's capabilities. The fight against AI manipulation is an ongoing challenge. CIDER, however, stands out as an innovative solution that offers both effective detection and efficient performance, a crucial step towards a more secure future for multimodal AI.