VLMs (Vision-Language Models) extend the capabilities of LLMs (Large Language
Models) to accept multimodal inputs. Since it has been verified that LLMs can
be induced to generate harmful or inaccurate content through specific test
cases (termed as Red Teaming), how VLMs perform in similar scenarios,
especially with their combination of textual and visual inputs, remains a
question. To explore this problem, we present a novel red teaming dataset
RTVLM, which encompasses 10 subtasks (e.g., image misleading, multi-modal
jail-breaking, face fairness, etc) under 4 primary aspects (faithfulness,
privacy, safety, fairness). Our RTVLM is the first red-teaming dataset to
benchmark current VLMs in terms of these 4 different aspects. Detailed analysis
shows that 10 prominent open-sourced VLMs struggle with the red teaming in
different degrees and have up to 31% performance gap with GPT-4V. Additionally,
we simply apply red teaming alignment to LLaVA-v1.5 with Supervised Fine-tuning
(SFT) using RTVLM, and this bolsters the models' performance with 10% in RTVLM
test set, 13% in MM-Hal, and without noticeable decline in MM-Bench,
overpassing other LLaVA-based models with regular alignment data. This reveals
that current open-sourced VLMs still lack red teaming alignment. Our code and
datasets will be open-source.

研究通过 RTVLM 数据集评估当前开源 VLM 在红队评测中的性能差距，结果显示这些 VLM 在不同程度上面临红队挑战，并且与 GPT-4V 相比，性能差距高达 31%。将红队诱导方法应用于 LLaVA-v1.5 模型，性能提高了 10% 以上，并显示出其他基于 LLaVA 的模型无法达到的性能，同时产生的数据对性能并无明显下降。