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，许多基准测试被提出以评估其性能和安全性。然而，这些基准仅考虑显式提示，而忽略了隐式提示（暗示目标但不明确提及的提示）。这些提示可能绕过安全限制，对模型的应用构成潜在威胁。本立场论文强调了T2I模型在隐式提示方面的当前状态。我们提出了一个名为ImplicitBench的基准测试，并针对流行T2I模型在隐式提示下的性能和影响进行了研究。具体而言，我们设计并收集了超过2000个隐式提示，涵盖三个方面：通用符号、名人隐私以及不安全内容（NSFW）问题，并评估了六个知名T2I模型在这些隐式提示下的能力。实验结果表明：（1）T2I模型能够根据隐式提示准确生成各种目标符号；（2）隐式提示为T2I模型带来了隐私泄露的潜在风险；（3）大多数被评估的T2I模型中的NSFW限制可以通过隐式提示绕过。我们呼吁T2I社区更加关注隐式提示的潜力与风险，并进一步研究其能力与影响，倡导一种平衡的方法，以在利用其优势的同时减轻风险。