Existing evaluations of harmful content detection rely predominantly on static benchmarks, which struggle to reflect the interactive adversarial ecosystem of real-world content platforms where users continuously revise their expressions in response to moderation feedback. This mismatch creates a significant performance gap between offline benchmark scores and online deployment effectiveness. To the best of our knowledge, we present EvoHarmBench, the first dynamic adversarial evaluation framework for content moderation systems. The framework employs an iterative optimization loop that evolves evasion strategies at the semantic-cluster level, while simultaneously optimizing for evasion success and human readability. We systematically evaluate LLM-based defense models which are widely used in real world moderation systems. The evaluation covers 229 semantic sub-clusters across five violation categories, derived from 5,002 real-world adversarial samples collected from content platforms. Our experiments reveal substantial vulnerabilities even in leading commercial systems: after twelve optimization iterations, Upon acceptance, we will release the full benchmark data, evaluation framework, and code to encourage a shift from static benchmarking toward dynamic adversarial evaluation in content safety research.

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.