Multimodal large language models (MLLMs) have demonstrated significant utility across diverse real-world applications. But MLLMs remain vulnerable to jailbreaks, where adversarial inputs can collapse their safety constraints and trigger unethical responses. In this work, we investigate jailbreaks in the text-vision multimodal setting and pioneer the observation that visual alignment imposes uneven safety constraints across modalities in MLLMs, thereby giving rise to multimodal safety asymmetry. We then develop PolyJailbreak, a black-box jailbreak method grounded in reinforcement learning. Initially, we probe the model's attention dynamics and latent representation space, assessing how visual inputs reshape cross-modal information flow and diminish the model's ability to separate harmful from benign inputs, thereby exposing exploitable vulnerabilities. On this basis, we systematize them into generalizable and reusable operational rules that constitute a structured library of Atomic Strategy Primitives, which translate harmful intents into jailbreak inputs through step-wise transformations. Guided by the primitives, PolyJailbreak employs a multi-agent optimization process that automatically adapts inputs against the target models. We conduct comprehensive evaluations on a variety of open-source and closed-source MLLMs, demonstrating that PolyJailbreak outperforms state-of-the-art baselines.