Quality-Diversity Evolution for Discovering Diverse Vulnerabilities in LLM Safety

Abstract

Current approaches to LLM adversarial testing suffer from coverage gaps: manual red-teaming does not scale, LLM-as-attacker methods exhibit mode collapse, and gradient-based approaches produce uninterpretable gibberish. We introduce a quality-diversity evolutionary framework that operates at the semantic level, evolving interpretable attack strategies rather than token sequences. Using MAP-Elites, we maintain a diverse archive of attacks across behavioral dimensions (strategy type, encoding meth

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@misc{mitra2026qualitydiversity,
  title = {{Quality-Diversity Evolution for Discovering Diverse Vulnerabilities in LLM Safety}},
  author = {Subhadip Mitra},
  year = {2026},
  month = may,
  eprint = {2606.00801},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.00801}
}