title: RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary
    Quality-Diversity Search
summary: 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.
paper_authors._id: 6806fb8e27dabde0a109776d
paper_authors.name: Quy-Anh Dang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-22T09:51:09.578Z
paper_authors._id: 645b663eca5d8a297712f2e1
paper_authors.avatarUrl: /avatars/c61dcba43feb879088b15b525e441cb9.svg
paper_authors.fullname: Quy-Anh Dang
paper_authors.type: user
paper_authors.user: quyanh
paper_authors._id: 6806fb8e27dabde0a109776e
paper_authors.name: Chris Ngo
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-22T09:51:07.511Z
paper_authors._id: 63105f7463b70252b4775783
paper_authors.avatarUrl: /avatars/89cfe71302cde6c2834f58d44bbcdbd5.svg
paper_authors.fullname: Chris Ngo
paper_authors.type: user
paper_authors.user: tnngo2
paper_authors._id: 6806fb8e27dabde0a109776f
paper_authors.name: Truong-Son Hy
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-12T18:35:01.304Z
paper_authors._id: 68221c9b9f55842b8f6e0c31
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/ov4_FLLaOc5ifehP-5i0i.jpeg
paper_authors.fullname: Truong-Son Hy
paper_authors.type: user
paper_authors.user: hytruongson
paper_title: RainbowPlus: Enhancing Adversarial Prompt Generation via Evolutionary
    Quality-Diversity Search
paper_summary: 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.
paper_ai_summary: RainbowPlus, an evolutionary computation-based red-teaming framework, enhances adversarial prompt generation for LLMs, improving attack success rate and diversity compared to existing methods.
paper_ai_keywords: 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