{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T23:14:22Z","timestamp":1789082062333,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031769337","type":"print"},{"value":"9783031769344","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T00:00:00Z","timestamp":1733961600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,12]],"date-time":"2024-12-12T00:00:00Z","timestamp":1733961600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-76934-4_7","type":"book-chapter","created":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T13:16:15Z","timestamp":1734441375000},"page":"100-118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["LLMSecCode: Evaluating Large Language Models for\u00a0Secure Coding"],"prefix":"10.1007","author":[{"given":"Anton","family":"Ryd\u00e9n","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erik","family":"N\u00e4slund","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3258-3696","authenticated-orcid":false,"given":"Elad Michael","family":"Schiller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3383-9617","authenticated-orcid":false,"given":"Magnus","family":"Almgren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,12]]},"reference":[{"key":"7_CR1","unstructured":"Alhanahnah, M., Boshmaf, Y., Baudry, B.: Depsrag: towards managing software dependencies using large language models. CoRR abs\/2405.20455 (2024)"},{"key":"7_CR2","unstructured":"Alhanahnah, M., Hasan, M.R., Bagheri, H.: An empirical evaluation of pre-trained large language models for repairing declarative formal specifications. CoRR abs\/2404.11050 (2024)"},{"key":"7_CR3","unstructured":"Bavarian, M., et al.: Efficient training of language models to fill in the middle. CoRR abs\/2207.14255 (2022)"},{"key":"7_CR4","unstructured":"Ben\u00a0Allal, L., Muennighoff, N., Kumar\u00a0Umapathi, L., Lipkin, B., von Werra, L.: A framework for the evaluation of code generation models (2022). https:\/\/github.com\/bigcode-project\/bigcode-evaluation-harness"},{"key":"7_CR5","unstructured":"Bhatt, M., et al.: CyberSecEval 2: a wide-ranging cybersecurity evaluation suite for large language models (2024). arXiv:2404.13161"},{"key":"7_CR6","unstructured":"Bhatt, M., et al.: Purple llama cyberseceval: a secure coding benchmark for language models. CoRR abs\/2312.04724 (2023)"},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Botes, M., Lenzini, G.: When cryptographic ransomware poses cyber threats: ethical challenges and proposed safeguards for cybersecurity researchers. In: IEEE European Symposium on Security and Privacy, EuroS &P, pp. 562\u2013568. IEEE (2022)","DOI":"10.1109\/EuroSPW55150.2022.00067"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Bui, Q.C., Scandariato, R., Ferreyra, N.E.D.: Vul4J: a dataset of reproducible Java vulnerabilities geared towards the study of program repair techniques. In: 2022 IEEE\/ACM 19th International Conference on Mining Software Repositories (MSR), pp. 464\u2013468 (2022)","DOI":"10.1145\/3524842.3528482"},{"key":"7_CR9","unstructured":"Chen, M., et al.: Evaluating large language models trained on code. CoRR abs\/2107.03374 (2021)"},{"key":"7_CR10","unstructured":"Fang, C., et al.: Large language models for code analysis: do LLMs really do their job? CoRR abs\/2310.12357 (2023)"},{"key":"7_CR11","unstructured":"Frantar, E., Ashkboos, S., Hoefler, T., Alistarh, D.: GPTQ: accurate post-training quantization for generative pre-trained transformers. CoRR abs\/2210.17323 (2022)"},{"key":"7_CR12","unstructured":"Gao, L., et al.: A framework for few-shot language model evaluation (2023)"},{"key":"7_CR13","unstructured":"GitHub: CodeQL (2024). https:\/\/codeql.github.com\/"},{"key":"7_CR14","unstructured":"Guo, D., et al.: Deepseek-coder: when the large language model meets programming the rise of code intelligence. CoRR abs\/2401.14196 (2024)"},{"key":"7_CR15","unstructured":"Hui, B.: An awesome and curated list of best code-LLM for research (2023). https:\/\/github.com\/huybery\/Awesome-Code-LLM"},{"issue":"4","key":"7_CR16","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MS.2021.3072577","volume":"38","author":"C Le Goues","year":"2021","unstructured":"Le Goues, C., Pradel, M., Roychoudhury, A., Chandra, S.: Automatic program repair. IEEE Softw. 38(4), 22\u201327 (2021)","journal-title":"IEEE Softw."},{"key":"7_CR17","unstructured":"Li, Y., et al.: Competition-level code generation with alphacode. CoRR abs\/2203.07814 (2022)"},{"key":"7_CR18","doi-asserted-by":"crossref","unstructured":"Lin, D., Koppel, J., Chen, A., Solar-Lezama, A.: Quixbugs: a multi-lingual program repair benchmark set based on the quixey challenge. In: Murphy, G.C. (ed.) Proceedings Companion of the 2017 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity, SPLASH, pp. 55\u201356. ACM (2017)","DOI":"10.1145\/3135932.3135941"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Marginean, A., et al.: Sapfix: automated end-to-end repair at scale. In: Sharp, H., Whalen, M. (eds.) Proceedings of the 41st International Conference on Software Engineering: Software Engineering in Practice, ICSE (SEIP), pp. 269\u2013278. IEEE\/ACM (2019)","DOI":"10.1109\/ICSE-SEIP.2019.00039"},{"issue":"4","key":"7_CR20","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1145\/299157.299164","volume":"42","author":"T O\u2019Reilly","year":"1999","unstructured":"O\u2019Reilly, T.: Lessons from open-source software development - introduction. Commun. ACM 42(4), 32\u201337 (1999)","journal-title":"Commun. ACM"},{"key":"7_CR21","unstructured":"PyCQA: Bandit (2024). https:\/\/bandit.readthedocs.io\/en\/latest\/"},{"key":"7_CR22","unstructured":"Rozi\u00e8re, B., et al.: Code Llama: Open Foundation Models for Code (2024). http:\/\/arxiv.org\/abs\/2308.12950"},{"key":"7_CR23","unstructured":"Ryd\u00e9n, A., N\u00e4slund, E., Schiller, E.M., Almgren, M.: LLMSecCode: evaluating large language models for secure coding. CoRR abs\/2408.16100 (2024)"},{"key":"7_CR24","doi-asserted-by":"crossref","unstructured":"Shi, C., et al.: A thorough examination of decoding methods in the era of LLMs. CoRR abs\/2402.06925 (2024)","DOI":"10.18653\/v1\/2024.emnlp-main.489"},{"key":"7_CR25","doi-asserted-by":"crossref","unstructured":"Siddiq, M.L., Santos, J.C.S.: SecurityEval dataset: mining vulnerability examples to evaluate machine learning-based code generation techniques. In: Mining Software Repositories Applications for Privacy and Security (MSR4P &S22) (2022)","DOI":"10.1145\/3549035.3561184"},{"key":"7_CR26","unstructured":"Team, M.L.: Introducing meta llama 3: the most capable openly available LLM to date (2024). https:\/\/ai.meta.com\/blog\/meta-llama-3\/. Accessed 08 July 2024"},{"key":"7_CR27","unstructured":"Touvron, H., et al.: Llama 2: open foundation and fine-tuned chat models. CoRR abs\/2307.09288 (2023)"},{"key":"7_CR28","unstructured":"Wolf, T., et al.: Transformers: state-of-the-art natural language processing. In: Liu, Q., Schlangen, D. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP, pp. 38\u201345. Association for Computational Linguistics (2020)"},{"key":"7_CR29","unstructured":"Wolsey, A.: The state-of-the-art in AI-based malware detection techniques: a review. CoRR abs\/2210.11239 (2022)"},{"key":"7_CR30","doi-asserted-by":"crossref","unstructured":"Wu, Y., et al.: How effective are neural networks for fixing security vulnerabilities. In: Just, R., Fraser, G. (eds.) Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis, ISSTA, pp. 1282\u20131294. ACM (2023)","DOI":"10.1145\/3597926.3598135"},{"key":"7_CR31","doi-asserted-by":"crossref","unstructured":"Xia, C.S., Wei, Y., Zhang, L.: Automated program repair in the era of large pre-trained language models. In: 45th IEEE\/ACM International Conference on Software Engineering, ICSE, pp. 1482\u20131494. IEEE (2023)","DOI":"10.1109\/ICSE48619.2023.00129"},{"key":"7_CR32","unstructured":"Zhao, P.: An empirical study on using codex for automated program repair. Digitala Vetenskapliga Arkivet (2023)"},{"key":"7_CR33","unstructured":"Zhao, W.X., et al.: A survey of large language models. CoRR abs\/2303.18223 (2023)"}],"container-title":["Lecture Notes in Computer Science","Cyber Security, Cryptology, and Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-76934-4_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,17]],"date-time":"2024-12-17T14:02:55Z","timestamp":1734444175000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-76934-4_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,12]]},"ISBN":["9783031769337","9783031769344"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-76934-4_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,12]]},"assertion":[{"value":"12 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CSCML","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Cyber Security, Cryptology, and Machine Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Be'er Sheva","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","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":"20 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cscml2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.cscml.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}