Diversifying Toxicity Search in Large Language Models Through Speciation

Evolutionary prompt search is a practical black-box approach for red teaming large language models (LLMs), but existing methods often collapse onto a small family of high-performing prompts, limiting coverage of distinct failure modes. We present a speciated quality-diversity (QD) extension of ToxSearch that maintains multiple high-toxicity prompt niches in parallel rather than optimizing a single best prompt. ToxSearch-S introduces unsupervised prompt speciation via a search methodology that maintains capacity-limited species with exemplar leaders, a reserve pool for outliers and emerging niches, and species-aware parent selection that trades off within-niche exploitation and cross-niche exploration. ToxSearch-S is found to reach higher peak toxicity ($\approx 0.73$ vs.\ $\approx 0.47$) and a extreme heavier tail (top-10 median $0.66$ vs.\ $0.45$) than the baseline, while maintaining comparable performance on moderately toxic prompts. Speciation also yields broader semantic coverage under a topic-as-species analysis (higher effective topic diversity $N_1$ and larger unique topic coverage $K$). Finally, species formed are well-separated in embedding space (mean separation ratio $\approx 1.93$) and exhibit distinct toxicity distributions, indicating that speciation partitions the adversarial space into behaviorally differentiated niches rather than superficial lexical variants. This suggests our approach uncovers a wider range of attack strategies.

Evolving Prompts for Toxicity Search in Large Language Models

Large Language Models remain vulnerable to adversarial prompts that elicit toxic content even after safety alignment. We present ToxSearch, a black-box evolutionary framework that tests model safety by evolving prompts in a synchronous steady-state loop. The system employs a diverse set of operators, including lexical substitutions, negation, back-translation, paraphrasing, and two semantic crossover operators, while a moderation oracle provides fitness guidance. Operator-level analysis shows heterogeneous behavior: lexical substitutions offer the best yield-variance trade-off, semantic-similarity crossover acts as a precise low-throughput inserter, and global rewrites exhibit high variance with elevated refusal costs. Using elite prompts evolved on LLaMA 3.1 8B, we observe practically meaningful but attenuated cross-model transfer, with toxicity roughly halving on most targets, smaller LLaMA 3.2 variants showing the strongest resistance, and some cross-architecture models retaining higher toxicity. These results suggest that small, controllable perturbations are effective vehicles for systematic red-teaming and that defenses should anticipate cross-model reuse of adversarial prompts rather than focusing only on single-model hardening.