@inproceedings{kour-etal-2025-exploring,
    title = "Exploring Straightforward Methods for Automatic Conversational Red-Teaming",
    author = "Kour, George  and
      Zwerdling, Naama  and
      Zalmanovici, Marcel  and
      Anaby Tavor, Ateret  and
      Fandina, Ora Nova  and
      Farchi, Eitan",
    editor = "Chen, Weizhu  and
      Yang, Yi  and
      Kachuee, Mohammad  and
      Fu, Xue-Yong",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.naacl-industry.10/",
    pages = "112--128",
    ISBN = "979-8-89176-194-0",
    abstract = "Large language models (LLMs) are increasingly used in business dialogue systems but they also pose security and ethical risks. Multi-turn conversations, in which context influences the model{'}s behavior, can be exploited to generate undesired responses. In this paper, we investigate the use of off-the-shelf LLMs in conversational red-teaming settings, where an attacker LLM attempts to elicit undesired outputs from a target LLM. Our experiments address critical questions and offer valuable insights regarding the effectiveness of using LLMs as automated red-teamers, shedding light on key strategies and usage approaches that significantly impact their performance.Our findings demonstrate that off-the-shelf models can serve as effective red-teamers, capable of adapting their attack strategies based on prior attempts. Allowing these models to freely steer conversations and conceal their malicious intent further increases attack success. However, their effectiveness decreases as the alignment of the target model improves."
}