{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T20:31:34Z","timestamp":1768422694714,"version":"3.49.0"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032077141","type":"print"},{"value":"9783032077158","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-07715-8_23","type":"book-chapter","created":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T08:56:49Z","timestamp":1760000209000},"page":"233-243","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Can Typos Cause Harm? The Impact of\u00a0Imperfect Input on\u00a0LLM Safety"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-2514-0402","authenticated-orcid":false,"given":"Saurabh","family":"Zinjad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6117-6382","authenticated-orcid":false,"given":"Amrita","family":"Bhattacharjee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6637-0761","authenticated-orcid":false,"given":"Alimohammad","family":"Beigi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,8]]},"reference":[{"key":"23_CR1","unstructured":"Andriushchenko, M., Flammarion, N.: Does refusal training in llms generalize to the past tense? (2024), https:\/\/arxiv.org\/abs\/2407.11969"},{"key":"23_CR2","doi-asserted-by":"publisher","unstructured":"Bhardwaj, R., Do, D.A., Poria, S.: Language models are Homer simpson! safety re-alignment of fine-tuned language models through task arithmetic. In: Ku, L.W., Martins, A., Srikumar, V. (eds.) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 14138\u201314149. Bangkok, Thailand, August 2024. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.762","DOI":"10.18653\/v1\/2024.acl-long.762"},{"key":"23_CR3","doi-asserted-by":"publisher","unstructured":"Cao, B., Cao, Y., Lin, L., Chen, J.: Defending against alignment-breaking attacks via robustly aligned LLM. In: Ku, L.W., Martins, A., Srikumar, V. (eds.) Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 10542\u201310560. Bangkok, Thailand, August 2024. https:\/\/doi.org\/10.18653\/v1\/2024.acl-long.568","DOI":"10.18653\/v1\/2024.acl-long.568"},{"key":"23_CR4","doi-asserted-by":"crossref","unstructured":"Cao, B., Cai, D., Zhang, Z., Zou, Y., Lam, W.: On the worst prompt performance of large language models. In: Globerson, A., Mackey, L., Belgrave, D., Fan, A., Paquet, U., Tomczak, J., Zhang, C. (eds.) Advances in Neural Information Processing Systems, vol.\u00a037, pp. 69022\u201369042 (2024), https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2024\/file\/7fa5a377b7ffabcce43cd00231bb3f9c-Paper-Conference.pdf","DOI":"10.52202\/079017-2205"},{"key":"23_CR5","doi-asserted-by":"publisher","unstructured":"Dong, Z., Zhou, Z., Yang, C., Shao, J., Qiao, Y.: Attacks, defenses and evaluations for LLM conversation safety: a survey. In: Duh, K., Gomez, H., Bethard, S. (eds.) Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 6734\u20136747. Mexico City, Mexico, June 2024. https:\/\/doi.org\/10.18653\/v1\/2024.naacl-long.375","DOI":"10.18653\/v1\/2024.naacl-long.375"},{"key":"23_CR6","doi-asserted-by":"crossref","unstructured":"Errica, F., Siracusano, G., Sanvito, D., Bifulco, R.: What did i do wrong? quantifying llms\u2019 sensitivity and consistency to prompt engineering (2025), https:\/\/arxiv.org\/abs\/2406.12334","DOI":"10.18653\/v1\/2025.naacl-long.73"},{"key":"23_CR7","unstructured":"Jiang, A.Q., et al.: Mistral 7b (2023), https:\/\/arxiv.org\/abs\/2310.06825"},{"key":"23_CR8","unstructured":"Llama\u00a0Team, A..M.: The llama 3 herd of models (2024), https:\/\/arxiv.org\/abs\/2407.21783"},{"key":"23_CR9","unstructured":"Ma, E.: Nlp augmentation. https:\/\/github.com\/makcedward\/nlpaug (2019)"},{"key":"23_CR10","unstructured":"Sclar, M., Choi, Y., Tsvetkov, Y., Suhr, A.: Quantifying language models\u2019 sensitivity to spurious features in prompt design or: How i learned to start worrying about prompt formatting (2024), https:\/\/arxiv.org\/abs\/2310.11324"},{"key":"23_CR11","doi-asserted-by":"publisher","unstructured":"Tam, Z.R., Wu, C.K., Tsai, Y.L., Lin, C.Y., Lee, H.Y., Chen, Y.N.: Let me speak freely? a study on the impact of format restrictions on large language model performance. In: Dernoncourt, F., Preo\u0163iuc-Pietro, D., Shimorina, A. (eds.) Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track, pp. 1218\u20131236. Miami, Florida, US, November 2024. https:\/\/doi.org\/10.18653\/v1\/2024.emnlp-industry.91","DOI":"10.18653\/v1\/2024.emnlp-industry.91"},{"key":"23_CR12","unstructured":"Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A.: Llama 2: open foundation and fine-tuned chat models (2023), https:\/\/arxiv.org\/abs\/2307.09288"},{"key":"23_CR13","doi-asserted-by":"publisher","unstructured":"Wang, B., Wei, C., Liu, Z., Lin, G., Chen, N.F.: Resilience of large language models for noisy instructions. In: Al-Onaizan, Y., Bansal, M., Chen, Y.N. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2024, pp. 11939\u201311950. Miami, Florida, USA, November 2024. https:\/\/doi.org\/10.18653\/v1\/2024.findings-emnlp.697","DOI":"10.18653\/v1\/2024.findings-emnlp.697"},{"key":"23_CR14","doi-asserted-by":"crossref","unstructured":"Xu, Z., Huang, R., Chen, C., Wang, X.: Uncovering safety risks of large language models through concept activation vector. In: Globerson, A., Mackey, L., Belgrave, D., Fan, A., Paquet, U., Tomczak, J., Zhang, C. (eds.) Advances in Neural Information Processing Systems, vol.\u00a037, pp. 116743\u2013116782 (2024), https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2024\/file\/d3a230d716e65afab578a8eb31a8d25f-Paper-Conference.pdf","DOI":"10.52202\/079017-3706"},{"key":"23_CR15","unstructured":"Yu, L., Do, V., Hambardzumyan, K., Cancedda, N.: Robust llm safeguarding via refusal feature adversarial training (2025), https:\/\/arxiv.org\/abs\/2409.20089"}],"container-title":["Lecture Notes in Computer Science","Social, Cultural, and Behavioral Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-07715-8_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T11:52:40Z","timestamp":1768391560000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-07715-8_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,8]]},"ISBN":["9783032077141","9783032077158"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-07715-8_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,8]]},"assertion":[{"value":"8 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SBP-BRiMS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pittsburgh, PA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"sbp2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sbp-brims.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}