The safety of large vision-language models is increasingly stress-tested by multimodal jailbreaks, yet existing attacks remain largely static at the meta level: template-based attacks freeze the image--text layout, while iterative attacks adapt only the image--text content with fixed attack strategies and frozen attacker parameters. We propose Meta-Adaptive Multimodal Jailbreaking (MAMJ), which instead optimizes the attacker itself along two axes: an attack strategy prompt (ASP) $θ$ governing attack iteration and attacker weights $φ$ determining attack effectiveness. Across groups of multimodal attack trajectories, an LLM-based critique first refines $θ$, after which group-aggregated attack-success-rate (ASR) rewards update $φ$. On MM-SafetyBench, MAMJ achieves $81.0\%$, $78.9\%$, and $82.3\%$ ASR against GPT-4o, Gemini-3-Pro-Preview, and Seed 2.0, respectively, outperforming the strongest sample-level baseline by up to $24.1$ percentage points. The learned attacker $(θ^\star,φ^\star)$ also transfers without retraining to unseen victims and remains effective under representative defenses. These results reveal a systemic vulnerability of frontier VLMs to meta-adaptive jailbreaks and motivate defenses against meta-level adversaries. Code is available at https://github.com/Alibaba-VELLDEPTH/MetaJailbreak-VLM.

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