TAPE-JB: Trait-Aligned Prompt Evolution for Jailbreaking Large Language Models

Abstract

Ensuring the safety of large language models (LLMs) requires rigorous evaluation against adversarial and jailbreaking attempts that can expose harmful behaviors. Malicious prompt generation, while inherently risky, is essential for systematically stress-testing model defenses and uncovering alignment vulnerabilities. Jailbreak attacks against large language models are often studied either through direct prompt rewriting or through search-based optimization over unconstrained prompt variants. We introduce TAPE-JB (Trait-Aligned Prompt Evolution for Jailbreaking LLMs), a genotype-guided evolutionary framework for adversarial prompt generation that searches over structured prompt traits while preserving the original harmful intention. Our method combines iterative prompt rewriting with an alignment-aware fitness function that jointly optimizes attack effectiveness, semantic faithfulness to the source intention, and realization of the intended prompt strategy. Across experiments, TAPE-JB consistently improves over simpler prompting baselines and outperforms CL-GSO on AdvBench, with a 14 percentage-point gain. Our results also show that the generated attacks retain cross-model transferability and that the proposed method remains effective in Polish (76% of attack success rate), demonstrating its applicability beyond the original English benchmark. These findings highlight the value of structured evolutionary search for systematic red-teaming of language models.