{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T21:36:35Z","timestamp":1757626595947,"version":"3.44.0"},"publisher-location":"Cham","reference-count":73,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031944574"},{"type":"electronic","value":"9783031944581"}],"license":[{"start":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T00:00:00Z","timestamp":1756598400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T00:00:00Z","timestamp":1756598400000},"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\/978-3-031-94458-1_7","type":"book-chapter","created":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T15:54:41Z","timestamp":1756569281000},"page":"139-168","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Can\u2019t Say Cant? Measuring and\u00a0Reasoning of\u00a0Dark Jargons in\u00a0Large Language Models"],"prefix":"10.1007","author":[{"given":"Xu","family":"Ji","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhangchi","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianqian","family":"Qiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiying","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Md Imran","family":"Hossen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiali","family":"Hei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,31]]},"reference":[{"key":"7_CR1","unstructured":"4chan community. https:\/\/www.4chan.org\/. Accessed 3 Oct 2023"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Bang, Y., Lee, N., Ishii, E., Madotto, A., Fung, P.: Assessing political prudence of open-domain chatbots. arXiv preprint arXiv:2106.06157 (2021)","DOI":"10.18653\/v1\/2021.sigdial-1.57"},{"key":"7_CR3","unstructured":"Bard-Google (2023). https:\/\/bard.google.com\/"},{"issue":"1","key":"7_CR4","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/s40979-023-00142-3","volume":"19","author":"D Birks","year":"2023","unstructured":"Birks, D., Clare, J.: Linking artificial intelligence facilitated academic misconduct to existing prevention frameworks. Int. J. Educ. Integr. 19(1), 20 (2023)","journal-title":"Int. J. Educ. Integr."},{"key":"7_CR5","unstructured":"Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., Toutanova, K.: BooLQ: exploring the surprising difficulty of natural yes\/no questions. arXiv preprint arXiv:1905.10044 (2019)"},{"key":"7_CR6","unstructured":"X\u00a0Corp. https:\/\/drugabuse.com\/addiction\/list-street-names-drugs\/. Accessed 2 Oct 2023"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Dasigi, P., Lo, K., Beltagy, I., Cohan, A., Smith, N.A., Gardner, M.: A dataset of information-seeking questions and answers anchored in research papers. arXiv preprint arXiv:2105.03011 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.365"},{"key":"7_CR8","unstructured":"The Racial\u00a0Slur Database. http:\/\/www.rsdb.org\/races. Accessed 19 May 2023"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Dhamala, J., et al.: Bold: dataset and metrics for measuring biases in open-ended language generation. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 862\u2013872 (2021)","DOI":"10.1145\/3442188.3445924"},{"key":"7_CR10","unstructured":"DrugAbuse. https:\/\/drugabuse.com\/addiction\/list-street-names-drugs\/. Accessed 19 May 2023"},{"key":"7_CR11","unstructured":"ERNIE (2023). https:\/\/yiyan.baidu.com\/welcome"},{"key":"7_CR12","unstructured":"Hugging Face. https:\/\/huggingface.co\/. Accessed 6 May 2024"},{"key":"7_CR13","first-page":"107","volume":"38","author":"L Zhang","year":"2021","unstructured":"Zhang, L., Feng, X.: Development trend and identification path of drug-related cryptic language under the background of internet plus. J. Polit. Sci. Law 38, 107\u2013118 (2021)","journal-title":"J. Polit. Sci. Law"},{"key":"7_CR14","unstructured":"A\u00a0Gun Lingo\u00a0glossary for those unfamiliar with\u00a0firearms. https:\/\/lifehacker.com\/a-gun-lingo-glossary-for-those-unfamiliar-with-firearms-1825427596. Accessed 19 May 2023"},{"key":"7_CR15","unstructured":"Google (2023). https:\/\/claude.ai\/"},{"key":"7_CR16","unstructured":"GPT-4 (2023). https:\/\/openai.com\/research\/gpt-4"},{"key":"7_CR17","doi-asserted-by":"publisher","first-page":"80218","DOI":"10.1109\/ACCESS.2023.3300381","volume":"11","author":"M Gupta","year":"2023","unstructured":"Gupta, M., Akiri, C., Aryal, K., Parker, E., Praharaj, L.: Impact of generative AI in cybersecurity and privacy. IEEE Access 11, 80218\u201380245 (2023)","journal-title":"IEEE Access"},{"issue":"4","key":"7_CR18","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1017\/S1351324901002807","volume":"7","author":"L Hirschman","year":"2001","unstructured":"Hirschman, L., Gaizauskas, R.: Natural language question answering: the view from here. Nat. Lang. Eng. 7(4), 275\u2013300 (2001)","journal-title":"Nat. Lang. Eng."},{"key":"7_CR19","unstructured":"Jones, E., Dragan, A., Raghunathan, A., Steinhardt, J.: Automatically auditing large language models via discrete optimization. arXiv preprint arXiv:2303.04381 (2023)"},{"key":"7_CR20","unstructured":"Kaggle. https:\/\/www.kaggle.com. Accessed 6 May 2024"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Kang, D., Li, X., Stoica, I., Guestrin, C., Zaharia, M., Hashimoto, T.: Exploiting programmatic behavior of LLMs: dual-use through standard security attacks. arXiv preprint arXiv:2302.05733 (2023)","DOI":"10.1109\/SPW63631.2024.00018"},{"key":"7_CR22","unstructured":"Karanjai, R.: Targeted phishing campaigns using large scale language models. arXiv preprint arXiv:2301.00665 (2022)"},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"Li, H., Guo, D., Fan, W., Xu, M., Song, Y.: Multi-step jailbreaking privacy attacks on ChatGPT. arXiv preprint arXiv:2304.05197 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.272"},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"Li, S., et al.: Hidden backdoors in human-centric language models. In: Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security, pp. 3123\u20133140 (2021)","DOI":"10.1145\/3460120.3484576"},{"key":"7_CR25","unstructured":"Liang, P.P., Wu, C., Morency, L.-P., Salakhutdinov, R.: Towards understanding and mitigating social biases in language models. In: International Conference on Machine Learning, pp. 6565\u20136576. PMLR (2021)"},{"key":"7_CR26","unstructured":"Liang, P., et al.: Holistic evaluation of language models. arXiv preprint arXiv:2211.09110 (2022)"},{"key":"7_CR27","unstructured":"Lexicon Library: LGBT. https:\/\/lexicon.library.lgbt\/wordgrouping\/intersectional\/. Accessed 19 May 2023"},{"key":"7_CR28","unstructured":"McCurry, J.: https:\/\/www.theguardian.com\/world\/2021\/jan\/14\/time-to-properly-socialise-hate-speech-ai-chatbot-pulled-from-facebook. Accessed 19 May 2023"},{"key":"7_CR29","unstructured":"Meng, C.: Lingo of telecom fraud crime analysis. J. Beijing Police Acad., 102\u2013105 (2021)"},{"issue":"2","key":"7_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3605943","volume":"56","author":"B Min","year":"2023","unstructured":"Min, B., et al.: Recent advances in natural language processing via large pre-trained language models: a survey. ACM Comput. Surv. 56(2), 1\u201340 (2023)","journal-title":"ACM Comput. Surv."},{"key":"7_CR31","doi-asserted-by":"crossref","unstructured":"Motoki, F., Pinho\u00a0Neto, V., Rodrigues, V.: More human than human: measuring ChatGPT political bias. SSRN (2023)","DOI":"10.1007\/s11127-023-01097-2"},{"key":"7_CR32","unstructured":"NewBing (2023). https:\/\/www.bing.com\/new"},{"key":"7_CR33","unstructured":"Ohlheiser, A. https:\/\/wapo.st\/3mRIOww. Accessed 19 May 2023"},{"key":"7_CR34","unstructured":"OpenAI. https:\/\/openai.com\/chatgpt. Accessed 19 October 2023"},{"key":"7_CR35","first-page":"27730","volume":"35","author":"L Ouyang","year":"2022","unstructured":"Ouyang, L., et al.: Training language models to follow instructions with human feedback. Adv. Neural. Inf. Process. Syst. 35, 27730\u201327744 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"7_CR36","doi-asserted-by":"crossref","unstructured":"Perez, E., et al.: Red teaming language models with language models. arXiv preprint arXiv:2202.03286 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.225"},{"key":"7_CR37","unstructured":"OpenAI platform. https:\/\/platform.openai.com\/docs\/guides\/moderation\/overview. Accessed 19 Oct 2023"},{"key":"7_CR38","unstructured":"Pratim Ray, P.: ChatGPT: a comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope. Internet Things Cyber-Phys. Syst. (2023)"},{"key":"7_CR39","unstructured":"Reddit. https:\/\/www.reddit.com\/. Accessed 2 Oct 2024"},{"issue":"10","key":"7_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3560260","volume":"55","author":"A Rogers","year":"2023","unstructured":"Rogers, A., Gardner, M., Augenstein, I.: QA dataset explosion: a taxonomy of NLP resources for question answering and reading comprehension. ACM Comput. Surv. 55(10), 1\u201345 (2023)","journal-title":"ACM Comput. Surv."},{"key":"7_CR41","unstructured":"Roy, S.S., Naragam, K.V., Nilizadeh, S.: Generating phishing attacks using ChatGPT. arXiv preprint arXiv:2305.05133 (2023)"},{"issue":"3","key":"7_CR42","doi-asserted-by":"publisher","first-page":"148","DOI":"10.3390\/socsci12030148","volume":"12","author":"D Rozado","year":"2023","unstructured":"Rozado, D.: The political biases of ChatGPT. Soc. Sci. 12(3), 148 (2023)","journal-title":"Soc. Sci."},{"key":"7_CR43","unstructured":"ERNIE\u00a0Protection Rule. https:\/\/wanhua.baidu.com\/talk\/protectionrule. Accessed 19 Oct 2023"},{"key":"7_CR44","unstructured":"Sacco, L.N.: Drug Enforcement in the United States: History, Policy, and Trends, vol. 7. Congressional Research Service Washington, DC (2014)"},{"key":"7_CR45","unstructured":"Sahoo, P., Singh, A.K., Saha, S., Jain, V., Mondal, S., Chadha, A.: A systematic survey of prompt engineering in large language models: techniques and applications. arXiv preprint arXiv:2402.07927 (2024)"},{"key":"7_CR46","unstructured":"Shen, X., Chen, Z., Backes, M., Zhang, Y.: In ChatGPT we trust? Measuring and characterizing the reliability of ChatGPT. arXiv preprint arXiv:2304.08979 (2023)"},{"key":"7_CR47","doi-asserted-by":"crossref","unstructured":"Si, W.M., et al. :Why so toxic? Measuring and triggering toxic behavior in open-domain chatbots. In: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, pp. 2659\u20132673 (2022)","DOI":"10.1145\/3548606.3560599"},{"key":"7_CR48","doi-asserted-by":"crossref","unstructured":"Sohail, S.S., et al.: Decoding ChatGPT: a taxonomy of existing research, current challenges, and possible future directions. J. King Saud Univ. Comput. Inf. Sci., 101675 (2023)","DOI":"10.1016\/j.jksuci.2023.101675"},{"key":"7_CR49","doi-asserted-by":"publisher","first-page":"13","DOI":"10.3406\/lfr.1991.6192","volume":"90","author":"M Sourdot","year":"1991","unstructured":"Sourdot, M.: Argot, jargon, jargot. Lang. Fr. 90, 13\u201327 (1991)","journal-title":"Lang. Fr."},{"key":"7_CR50","doi-asserted-by":"crossref","unstructured":"Sun, Z., et\u00a0al.: Aligning large multimodal models with factually augmented RLHF. arXiv preprint arXiv:2309.14525 (2023)","DOI":"10.18653\/v1\/2024.findings-acl.775"},{"key":"7_CR51","doi-asserted-by":"crossref","unstructured":"Tan, Y., et al.: Can ChatGPT replace traditional KBQA models? An in-depth analysis of the question answering performance of the GPT LLM family. In: International Semantic Web Conference, pp. 348\u2013367. Springer (2023)","DOI":"10.1007\/978-3-031-47240-4_19"},{"key":"7_CR52","unstructured":"Urban Thesaurus. http:\/\/onlineslangdictionary.com\/thesaurus\/words+meaning+weapon.html. Accessed 19 May 2023"},{"key":"7_CR53","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"7_CR54","unstructured":"Wang, J., Liu, Z., Park, K.H., Chen, M., Xiao, C.: Adversarial demonstration attacks on large language models. arXiv preprint arXiv:2305.14950 (2023)"},{"key":"7_CR55","unstructured":"Wei, A., Haghtalab, N., Steinhardt, J.: Jailbroken: how does LLM safety training fail? arXiv preprint arXiv:2307.02483 (2023)"},{"key":"7_CR56","unstructured":"Weidinger, L., et\u00a0al.: Ethical and social risks of harm from language models. arXiv preprint arXiv:2112.04359 (2021)"},{"key":"7_CR57","doi-asserted-by":"crossref","unstructured":"Weidinger, L., et\u00a0al.: Taxonomy of risks posed by language models. In: Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pp. 214\u2013229 (2022)","DOI":"10.1145\/3531146.3533088"},{"key":"7_CR58","unstructured":"Defining Wellness. https:\/\/definingwellness.com\/resources\/drug-slang-word-glossary\/. Accessed 19 May 2023"},{"key":"7_CR59","unstructured":"EverybodyWiki Bios\u00a0 & Wiki. https:\/\/en.everybodywiki.com\/List_of_nicknames_of_Donald_Trump. Accessed 19 May 2023"},{"key":"7_CR60","doi-asserted-by":"crossref","unstructured":"Wikipedia. https:\/\/en.wikipedia.org\/wiki\/LGBT_slang. Accessed 19 May 2023","DOI":"10.1515\/9783839458624-005"},{"key":"7_CR61","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1007\/s10579-018-9416-0","volume":"52","author":"W Liang","year":"2018","unstructured":"Liang, W., Morstatter, F., Liu, H.: SlangSD: building, expanding and using a sentiment dictionary of slang words for short-text sentiment classification. Lang. Resour. Eval. 52, 839\u2013852 (2018)","journal-title":"Lang. Resour. Eval."},{"key":"7_CR62","unstructured":"X. https:\/\/twitter.com\/. Accessed 6 May 2024"},{"key":"7_CR63","unstructured":"SparkDesk Xunfei-Xinghuo (2023). https:\/\/xinghuo.xfyun.cn\/"},{"key":"7_CR64","unstructured":"Qu, Y.: Grammar summary of Chinese folk secret language (lingo) (part 1). Cult. J., 26\u201333 (2014)"},{"key":"7_CR65","doi-asserted-by":"crossref","unstructured":"Yih, W.-T., Richardson, M., Meek, C., Chang, M.-W., Suh, J.: The value of semantic parse labeling for knowledge base question answering. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 201\u2013206 (2016)","DOI":"10.18653\/v1\/P16-2033"},{"key":"7_CR66","unstructured":"Yu, W., Jiang, Z., Dong, Y., Feng, J.: ReClor: a reading comprehension dataset requiring logical reasoning. arXiv preprint arXiv:2002.04326 (2020)"},{"key":"7_CR67","unstructured":"Yuan, K., Lu, H., Liao, X., Wang X.: Reading thieves\u2019 cant: automatically identifying and understanding dark jargons from cybercrime marketplaces. In: 27th USENIX Security Symposium (USENIX Security 18), pp. 1027\u20131041 (2018)"},{"key":"7_CR68","unstructured":"Yuan, T., et al.: GPT-4 is too smart to be safe: stealthy chat with LLMs via cipher. arXiv preprint arXiv:2308.06463 (2023)"},{"key":"7_CR69","first-page":"1194","volume":"28","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Zeng, J., Di, M., et al.: Network drug-related lingo identification strategy and management countermeasures. Chin. J. Drug Abuse Prev. Control 28, 1194\u20131198 (2022)","journal-title":"Chin. J. Drug Abuse Prev. Control"},{"key":"7_CR70","unstructured":"Zhao, J., Yan, Q., Liu, X., Li, B., Zuo, G.: Cyber threat intelligence modeling based on heterogeneous graph convolutional network. In: 23rd International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2020), pp. 241\u2013256 (2020)"},{"key":"7_CR71","unstructured":"Zheng, R., et al.: Secrets of RLHF in large language models part i: PPO (2023)"},{"key":"7_CR72","unstructured":"Zhong, L., Wang, Z.: A study on robustness and reliability of large language model code generation. arXiv preprint arXiv:2308.10335 (2023)"},{"key":"7_CR73","unstructured":"Zou, A., Wang, Z., Kolter, J.Z., Fredrikson, M.: Universal and transferable adversarial attacks on aligned language models (2023)"}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Security and Privacy in Communication Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-94458-1_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T22:45:03Z","timestamp":1757457903000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-94458-1_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,31]]},"ISBN":["9783031944574","9783031944581"],"references-count":73,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-94458-1_7","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2025,8,31]]},"assertion":[{"value":"31 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SecureComm","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Security and Privacy in Communication Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dubai","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Arab Emirates","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"securecomm2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/securecomm.eai-conferences.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}