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Malicious shell commands are linchpins to many cyber-attacks, but may not be easy to understand by security analysts due to complicated and often disguised code structures.
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S. Min, X. Lyu, A. Holtzman, M. Artetxe, M. Lewis, H. Hajishirzi, and L. Zettlemoyer, “Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?” in Conference on Empirical Methods in Natural Language Processing . Association for Computational Linguistics, 2022
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2021
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D. Guo, S. Ren, S. Lu, Z. Feng, D. Tang, S. Liu, L. Zhou, N. Duan, A. Svyatkovskiy, S. Fu, M. Tufano, S. K. Deng, C. B. Clement, D. Drain, N. Sundaresan, J. Yin, D. Jiang, and M. Zhou, “GraphCodeBERT: Pre-training Code Representations with Data Flow,” in International Conference on Learning Representations . OpenReview.net, 2021
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L. A. Johnson Kinyua, “AI/ML in Security Orchestration, Automation and Response: Future Research Directions,” Intelligent Automation & Soft Computing , vol. 28, no. 2, pp. 527–545, 2021
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S. Lu, D. Guo, S. Ren, J. Huang, A. Svyatkovskiy, A. Blanco, C. B. Clement, D. Drain, D. Jiang, D. Tang, G. Li, L. Zhou, L. Shou, L. Zhou, M. Tufano, M. Gong, M. Zhou, N. Duan, N. Sundaresan, S. K. Deng, S. Fu, and S. Liu, “CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation,” in Neural Information Processing Systems Track on Datasets and Benchmarks . PMLR, 2021
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2021
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2021
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2021
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2022
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2022
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J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. H. Chi, Q. V. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” in Conference on Neural Information Processing Systems . PMLR, 2022
2022
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2023
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J. Leinonen, P. Denny, S. MacNeil, S. Sarsa, S. Bernstein, J. Kim, A. Tran, and A. Hellas, “Comparing Code Explanations Created by Students and Large Language Models,” in Conference on Innovation and Technology in Computer Science Education V . ACM, 2023
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X. Liu, H. Lai, H. Yu, Y. Xu, A. Zeng, Z. Du, P. Zhang, Y. Dong, and J. Tang, “WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences,” in ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2023
2023
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S. MacNeil, A. Tran, A. Hellas, J. Kim, S. Sarsa, P. Denny, S. Bernstein, and J. Leinonen, “Experiences from Using Code Explanations Generated by Large Language Models in a Web Software Development E-Book,” in Technical Symposium on Computer Science Education , 2023
2023
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N. Muennighoff, N. Tazi, L. Magne, and N. Reimers, “MTEB: Massive Text Embedding Benchmark,” in Conference of the European Chapter of the Association for Computational Linguistics , 2023
2023
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——, “GPT-4 technical report,” arXiv preprint arXiv:2303.08774 , 2023
2023
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S. Ray, “Samsung Bans ChatGPT Among Employees After Sensitive Code Leak,” https://www.forbes.com/sites/siladityaray/2023/05/02/samsung-bans-chatgpt-and-other-chatbots-for-employees-after-sensitive-code-leak
2023
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2023
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A. Zeng, X. Liu, Z. Du, Z. Wang, H. Lai, M. Ding, Z. Yang, Y. Xu, W. Zheng, X. Xia, W. L. Tam, Z. Ma, Y. Xue, J. Zhai, W. Chen, Z. Liu, P. Zhang, Y. Dong, and J. Tang, “GLM-130B: An Open Bilingual Pre-trained Model,” in International Conference on Learning Representations . OpenReview.net, 2023
2023
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A. Rao, S. Vashistha, A. Naik, S. Aditya, and M. Choudhury, “Tricking llms into disobedience: Formalizing, analyzing, and detecting jailbreaks,” arXiv preprint arXiv: 2305.14965 , 2024
2024
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