The safety of large vision-language models is increasingly stress-tested by multimodal jailbreaks, yet existing attacks share one fundamental limitation: the attack itself is static. Template-based attacks freeze the image–text layout, while iterative sample-level attacks make only the image–text content dynamic, keeping the attack-iteration strategy fixed and the attacker’s parameters frozen. At the meta level they remain static, and we argue this partial dynamism cannot expose the full safety risks of multimodal models.We propose Meta-Adaptive Multimodal Jailbreaking (MAMJ), which lifts adversarial dynamism from the sample level to the meta level by making the attacker’s policy itself the optimisation target along two axes: an attack strategy prompt (ASP) 𝜃 in language space, governing how the attack is iterated against a victim VLM, and the attacker weights 𝜙, determining the success rate attainable under a given 𝜃.Evolving over a group of multimodal attack trajectories, an LLM-based critique of failed trajectories first refines 𝜃, after which group-aggregated attack-success-rate (ASR) rewards update 𝜙. On MM-SafetyBench, MAMJ attains 81.0%, 78.9%, and 82.3% ASR against GPT-4o, Gemini-3-Pro-Preview, and Seed 2.0, exceeding the strongest sample-level baseline by up to 24.1 percentage points, and the learned attacker (𝜃⋆, 𝜙⋆) transfers without retraining to unseen victims while remaining effective under representative defences.These results expose a systemic vulnerability of frontier VLMs to meta-adaptive jailbreaks and underscore the need for defences that anticipate meta-level adversaries. Code and checkpoints will be released.

In the process of learning Chinese, second language learners may have various grammatical errors due to the negative transfer of native language. This paper describes our submission to the NLPTEA 2020 shared task on CGED. We present a hybrid system that utilizes both detection and correction stages. The detection stage is a sequential labelling model based on BiLSTM-CRF and BERT contextual word representation. The correction stage is a hybrid model based on the n-gram and Seq2Seq. Without adding additional features and external data, the BERT contextual word representation can effectively improve the performance metrics of Chinese grammatical error detection and correction.