Large multimodal language models have proven transformative in numerous applications. However, these models have been shown to memorize and leak pre-training data, raising serious user privacy and information security concerns. While data leaks should be prevented, it is also crucial to examine the trade-off between the privacy protection and model utility of proposed approaches. In this paper, we introduce PrivQA -- a multimodal benchmark to assess this privacy/utility trade-off when a model is instructed to protect specific categories of personal information in a simulated scenario. We also propose a technique to iteratively self-moderate responses, which significantly improves privacy. However, through a series of red-teaming experiments, we find that adversaries can also easily circumvent these protections with simple jailbreaking methods through textual and/or image inputs. We believe PrivQA has the potential to support the development of new models with improved privacy protections, as well as the adversarial robustness of these protections. We release the entire PrivQA dataset at https://llm-access-control.github.io/.

翻译：大型多模态语言模型已在众多应用中展现出变革性作用。然而，这些模型已被证实会记忆并泄露预训练数据，引发严重的用户隐私与信息安全问题。虽然应防止数据泄露，但同样重要的是，需审视所提方法在隐私保护与模型效用之间的权衡。本文提出PrivQA——一个多模态基准测试，用于在模拟场景下评估模型受指令保护特定类别个人信息时的隐私/效用权衡。我们还提出一种迭代式自我审核响应技术，可显著提升隐私保护效果。然而，通过一系列红队攻防实验，我们发现攻击者仍可利用简单的越狱方法，通过文本和/或图像输入轻松绕过这些防护措施。我们相信PrivQA有望支持开发具有更强隐私保护能力的新模型，并提升这些防护措施对抗鲁棒性。我们在https://llm-access-control.github.io/公开发布了完整的PrivQA数据集。