The Multi-Modal Linkage (MML) attack represents a sophisticated method to compromise Large Vision-Language Models (VLMs) by utilizing an "encryption-decryption" strategy across text and image modalities. This technique involves embedding malicious queries within images through methods such as word substitution and image transformation to circumvent preliminary safety protocols. Subsequently, a carefully crafted text prompt instructs the VLM to "decrypt" the embedded content, resulting in the generation of harmful outputs. The concept of "evil alignment," which situates the attack within a video game context, further amplifies the effectiveness of this approach. Mitigation steps: **For AI Developers:** * Implement advanced safety filters to detect and mitigate MML-style attacks. * Establish rigorous input validation and sanitization protocols. **For Model Trainers/Fine-tuners:** * Enhance model robustness against adversarial examples across text and image modalities. * Conduct research into resilient multimodal safety alignment techniques.