FLIRT: Feedback Loop In-context Red Teaming

Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, Rahul Gupta


Abstract
Warning: this paper contains content that may be inappropriate or offensive.As generative models become available for public use in various applications, testing and analyzing vulnerabilities of these models has become a priority. In this work, we propose an automatic red teaming framework that evaluates a given black-box model and exposes its vulnerabilities against unsafe and inappropriate content generation. Our framework uses in-context learning in a feedback loop to red team models and trigger them into unsafe content generation. In particular, taking text-to-image models as target models, we explore different feedback mechanisms to automatically learn effective and diverse adversarial prompts. Our experiments demonstrate that even with enhanced safety features, Stable Diffusion (SD) models are vulnerable to our adversarial prompts, raising concerns on their robustness in practical uses. Furthermore, we demonstrate that the proposed framework is effective for red teaming text-to-text models.
Anthology ID:
2024.emnlp-main.41
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
703–718
Language:
URL:
https://aclanthology.org/2024.emnlp-main.41
DOI:
10.18653/v1/2024.emnlp-main.41
Bibkey:
Cite (ACL):
Ninareh Mehrabi, Palash Goyal, Christophe Dupuy, Qian Hu, Shalini Ghosh, Richard Zemel, Kai-Wei Chang, Aram Galstyan, and Rahul Gupta. 2024. FLIRT: Feedback Loop In-context Red Teaming. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 703–718, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
FLIRT: Feedback Loop In-context Red Teaming (Mehrabi et al., EMNLP 2024)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.emnlp-main.41.pdf