The rapid advancement of artificial intelligence has brought safety concerns to the forefront of research. Recent studies indicate that Large Language Models (LLMs) may exhibit emergent misalignment during training progress: when exposed to narrow-domain harmful data, they can develop unsafe behaviors across broad, unrelated tasks. This phenomenon raises a critical question: do current alignment methods genuinely eliminate harmful capabilities, or do they merely condition models to “simulate” safety? To investigate this, we conduct a comprehensive study on cross-modal emergent misalignment involving diverse Multimodal Large Language Models (MLLMs), including vision-language models (VLMs) such as Gemma3 and Qwen2.5-VL and unified any-to-any models including Janus and Bagel. We utilize a multimodal training setup encompassing text-to-text (T2T), text-image-to-text (TI2T), and text-to-image (T2I) tasks to probe safety boundaries. Our experiments yield four critical findings: (1) Cross-modal penetration: Current alignment methods primarily constrain model behavior rather than unlearning harmful knowledge, as evidenced by safety breaches transferring across modalities. (2) Backbone vulnerability: Mechanistic analysis reveals that the LLM backbone, rather than the visual encoder/decoder, is the primary source of cross-modal vulnerabilities. (3) Evaluation gaps: We identify significant deficiencies in current safety evaluation practices, demonstrating that single-modality testing is inadequate for detecting cross-modal risks. These findings underscore the urgent need for comprehensive cross-modal evaluation frameworks to mitigate safety concerns in next-generation multimodal models.