This paper describes the FBK′s participation in the Simultaneous Translation Evaluation Campaign at IWSLT 2024. For this year′s submission in the speech-to-text translation (ST) sub-track, we propose SimulSeamless, which is realized by combining AlignAtt and SeamlessM4T in its medium configuration. The SeamlessM4T model is used `off-the-shelf′ and its simultaneous inference is enabled through the adoption of AlignAtt, a SimulST policy based on cross-attention that can be applied without any retraining or adaptation of the underlying model for the simultaneous task. We participated in all the Shared Task languages (English-textgreaterGerman, Japanese, Chinese, and Czech-textgreaterEnglish), achieving acceptable or even better results compared to last year′s submissions. SimulSeamless, covering more than 143 source languages and 200 target languages, is released at: https://github.com/hlt-mt/FBK-fairseq/.