@inproceedings{pala-etal-2025-ferret,
    title = "Ferret: Faster and Effective Automated Red Teaming with Reward-Based Scoring Technique",
    author = "Pala, Tej Deep  and
      Toh, Vernon  and
      Bhardwaj, Rishabh  and
      Poria, Soujanya",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.634/",
    doi = "10.18653/v1/2025.findings-emnlp.634",
    pages = "11845--11860",
    ISBN = "979-8-89176-335-7",
    abstract = "As large language models (LLMs) are increasingly integrated into real-world applications, ensuring their safety and robustness is critical. Automated red-teaming methods generate adversarial attacks to identify vulnerabilities, but existing approaches often face challenges like slow performance, limited categorical diversity, and high resource demands. We propose Ferret, a novel method that enhances the baseline, Rainbow Teaming by generating multiple adversarial prompt mutations per iteration and ranking them using scoring functions such as reward models, Llama Guard, and LLM-as-a-judge. Ferret achieves a 95{\%} attack success rate (ASR), a 46{\%} improvement over baseline, and reduces time to a 90{\%} ASR by 15.2{\%}. Additionally, it generates transferable adversarial prompts effective on larger LLMs. Our code is available at https://github.com/declare-lab/ferret"
}