RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary
  Quality-Diversity Search
Large Language Models (LLMs) exhibit remarkable capabilities but are
susceptible to adversarial prompts that exploit vulnerabilities to produce
unsafe or biased outputs. Existing red-teaming methods often face scalability
challenges, resource-intensive requirements, or limited diversity in attack
strategies. We propose RainbowPlus, a novel red-teaming framework rooted in
evolutionary computation, enhancing adversarial prompt generation through an
adaptive quality-diversity (QD) search that extends classical evolutionary
algorithms like MAP-Elites with innovations tailored for language models. By
employing a multi-element archive to store diverse high-quality prompts and a
comprehensive fitness function to evaluate multiple prompts concurrently,
RainbowPlus overcomes the constraints of single-prompt archives and pairwise
comparisons in prior QD methods like Rainbow Teaming. Experiments comparing
RainbowPlus to QD methods across six benchmark datasets and four open-source
LLMs demonstrate superior attack success rate (ASR) and diversity
(Diverse-Score approx 0.84), generating up to 100 times more unique prompts
(e.g., 10,418 vs. 100 for Ministral-8B-Instruct-2410). Against nine
state-of-the-art methods on the HarmBench dataset with twelve LLMs (ten
open-source, two closed-source), RainbowPlus achieves an average ASR of 81.1%,
surpassing AutoDAN-Turbo by 3.9%, and is 9 times faster (1.45 vs. 13.50 hours).
Our open-source implementation fosters further advancements in LLM safety,
offering a scalable tool for vulnerability assessment. Code and resources are
publicly available at https://github.com/knoveleng/rainbowplus, supporting
reproducibility and future research in LLM red-teaming.
6806fb8e27dabde0a109776d
Quy-Anh Dang
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2025-04-22T09:51:09.578Z
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Quy-Anh Dang
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quyanh
6806fb8e27dabde0a109776e
Chris Ngo
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2025-04-22T09:51:07.511Z
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Chris Ngo
user
tnngo2
6806fb8e27dabde0a109776f
Truong-Son Hy
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2025-05-12T18:35:01.304Z
68221c9b9f55842b8f6e0c31
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Truong-Son Hy
user
hytruongson
RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary
  Quality-Diversity Search
Large Language Models (LLMs) exhibit remarkable capabilities but are
susceptible to adversarial prompts that exploit vulnerabilities to produce
unsafe or biased outputs. Existing red-teaming methods often face scalability
challenges, resource-intensive requirements, or limited diversity in attack
strategies. We propose RainbowPlus, a novel red-teaming framework rooted in
evolutionary computation, enhancing adversarial prompt generation through an
adaptive quality-diversity (QD) search that extends classical evolutionary
algorithms like MAP-Elites with innovations tailored for language models. By
employing a multi-element archive to store diverse high-quality prompts and a
comprehensive fitness function to evaluate multiple prompts concurrently,
RainbowPlus overcomes the constraints of single-prompt archives and pairwise
comparisons in prior QD methods like Rainbow Teaming. Experiments comparing
RainbowPlus to QD methods across six benchmark datasets and four open-source
LLMs demonstrate superior attack success rate (ASR) and diversity
(Diverse-Score approx 0.84), generating up to 100 times more unique prompts
(e.g., 10,418 vs. 100 for Ministral-8B-Instruct-2410). Against nine
state-of-the-art methods on the HarmBench dataset with twelve LLMs (ten
open-source, two closed-source), RainbowPlus achieves an average ASR of 81.1%,
surpassing AutoDAN-Turbo by 3.9%, and is 9 times faster (1.45 vs. 13.50 hours).
Our open-source implementation fosters further advancements in LLM safety,
offering a scalable tool for vulnerability assessment. Code and resources are
publicly available at https://github.com/knoveleng/rainbowplus, supporting
reproducibility and future research in LLM red-teaming.
RainbowPlus, an evolutionary computation-based red-teaming framework, enhances adversarial prompt generation for LLMs, improving attack success rate and diversity compared to existing methods.
Large Language Models (LLMs) adversarial prompts red-teaming methods evolutionary computation adaptive quality-diversity (QD) search MAP-Elites multi-element archive fitness function attack success rate (ASR) Diverse-Score HarmBench dataset AutoDAN-Turbo vulnerability assessment