Recent text-to-image (T2I) models have had great success, and many benchmarks have been proposed to evaluate their performance and safety. However, they only consider explicit prompts while neglecting implicit prompts (hint at a target without explicitly mentioning it). These prompts may get rid of safety constraints and pose potential threats to the applications of these models. This position paper highlights the current state of T2I models toward implicit prompts. We present a benchmark named ImplicitBench and conduct an investigation on the performance and impacts of implicit prompts with popular T2I models. Specifically, we design and collect more than 2,000 implicit prompts of three aspects: General Symbols, Celebrity Privacy, and Not-Safe-For-Work (NSFW) Issues, and evaluate six well-known T2I models' capabilities under these implicit prompts. Experiment results show that (1) T2I models are able to accurately create various target symbols indicated by implicit prompts; (2) Implicit prompts bring potential risks of privacy leakage for T2I models. (3) Constraints of NSFW in most of the evaluated T2I models can be bypassed with implicit prompts. We call for increased attention to the potential and risks of implicit prompts in the T2I community and further investigation into the capabilities and impacts of implicit prompts, advocating for a balanced approach that harnesses their benefits while mitigating their risks.

翻译：近年来，文本到图像（T2I）模型取得了巨大成功，并已提出许多基准来评估其性能与安全性。然而，现有评估仅考虑显式提示，而忽视了隐式提示（即不明确提及目标但暗示其存在）。这些提示可能规避安全约束，并对模型应用构成潜在威胁。本立场文件重点探讨了当前T2I模型处理隐式提示的现状。我们提出了名为ImplicitBench的基准，并针对流行T2I模型在隐式提示下的性能与影响进行了调查研究。具体而言，我们设计并收集了涵盖三个方面的2000余条隐式提示：通用符号、名人隐私以及非安全内容（NSFW）问题，并评估了六个知名T2I模型在这些隐式提示下的生成能力。实验结果表明：（1）T2I模型能够准确生成隐式提示所指示的各种目标符号；（2）隐式提示给T2I模型带来了潜在的隐私泄露风险；（3）大多数被评估T2I模型的NSFW约束可通过隐式提示绕过。我们呼吁T2I领域更多关注隐式提示的潜力与风险，并进一步研究其能力与影响，倡导在利用其优势的同时降低风险，采取平衡的发展路径。