The deployment of large-scale generative models is often restricted by their
potential risk of causing harm to users in unpredictable ways. We focus on the
problem of black-box red teaming, where a red team generates test cases and
interacts with the victim model to discover a diverse set of failures with
limited query access. Existing red teaming methods construct test cases based
on human supervision or language model (LM) and query all test cases in a
brute-force manner without incorporating any information from past evaluations,
resulting in a prohibitively large number of queries. To this end, we propose
Bayesian red teaming (BRT), novel query-efficient black-box red teaming methods
based on Bayesian optimization, which iteratively identify diverse positive
test cases leading to model failures by utilizing the pre-defined user input
pool and the past evaluations. Experimental results on various user input pools
demonstrate that our method consistently finds a significantly larger number of
diverse positive test cases under the limited query budget than the baseline
methods. The source code is available at
this https URL

提出了一种基于贝叶斯优化的黑盒反向渗透方法，利用预定义用户输入池和过去的评估迭代地识别多样的正向测试用例，以在有限的查询预算下找到更多的多样的正向测试用例。