{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:15:06Z","timestamp":1758672906235,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Embodied agents exhibit immense potential across a multitude of domains, making the assurance of their behavioral safety a fundamental prerequisite for their widespread deployment. However, existing research predominantly concentrates on the security of general large language models, lacking specialized methodologies for establishing safety benchmarks and input moderation tailored to embodied agents. To bridge this gap, this paper introduces a novel input moderation framework, meticulously designed to safeguard embodied agents. This framework encompasses the entire pipeline, including taxonomy definition, dataset curation, moderator architecture, model training, and rigorous evaluation. Notably, we introduce EAsafetyBench, a meticulously crafted safety benchmark engineered to facilitate both the training and stringent assessment of moderators specifically designed for embodied agents. Furthermore, we propose Pinpoint, an innovative prompt-decoupled input moderation scheme that harnesses a masked attention mechanism to effectively isolate and mitigate the influence of functional prompts on moderation tasks. Extensive experiments conducted on diverse benchmark datasets and models validate the feasibility and efficacy of the proposed approach. The results demonstrate that our methodologies achieve an impressive average detection accuracy of 94.58%, surpassing the performance of existing state-of-the-art techniques, alongside an exceptional moderation processing time of merely 0.002 seconds per instance. The source code and datasets can be found at https:\/\/github.com\/ZihanYan-CQU\/EAsafetyBench.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/867","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"7795-7803","source":"Crossref","is-referenced-by-count":0,"title":["Advancing Embodied Agent Security: From Safety Benchmarks to Input Moderation"],"prefix":"10.24963","author":[{"given":"Ning","family":"Wang","sequence":"first","affiliation":[{"name":"College of computer science, Chongqing University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihan","family":"Yan","sequence":"additional","affiliation":[{"name":"College of computer science, Chongqing University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiyang","family":"Li","sequence":"additional","affiliation":[{"name":"College of computer science, Chongqing University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuan","family":"Ma","sequence":"additional","affiliation":[{"name":"College of computer science, Chongqing University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"He","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, The Chinese University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Xiang","sequence":"additional","affiliation":[{"name":"College of computer science, Chongqing University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:35:20Z","timestamp":1758627320000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/867"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/867","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}