Co-Evolutionary Multi-Modal Alignment via Structured Adversarial Evolution
Adversarial behavior plays a central role in aligning large language models with human values. However, existing alignment methods largely rely on static adversarial settings, which fundamentally limit robustness, particularly in multimodal settings with a larger attack surface. In this work, we move beyond static adversarial supervision and introduce co-evolutionary alignment with evolving attacks, instantiated by CEMMA (Co-Evolutionary Multi-Modal Alignment), an automated and adaptive framework for multimodal safety alignment. We introduce an Evolutionary Attacker that decomposes adversarial prompts into method templates and harmful intents. By employing genetic operators, including mut...
| Authors | Guoxin Shi, Haoyu Wang, Zaihui Yang et al. |
|---|---|
| Primary category | cs.CR |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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