Current - Issue
Year 2026 · Volume 5 · Issue 2
Original Article
An AI-Driven Secure Interpreter for Adaptive and Security- Aware Program Execution
Zeeshan Ahmed1
Shivangini Bihari2
Anu Priya3
1 Student, Department of CSE, ITER, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India. 2 Assistant Professor, Faculty of CSE & IT, Jharkhand Rai University, Ranchi, Jharkhand, India. 3 Assistant Professor, Amity Institute of Information Technology, Amity University, Patna, Bihar, India.
Published Online: May-August 2026
Pages: 1087-1096
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20260502116
References
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23. Wang, Y., Le, H., Gotmare, A. D., Bui, N. D. Q., Li, J., & Hoi, S. C. H. (2023). CodeT5+: Open code large language models for code
understanding and generation. arXiv. https://arxiv.org/abs/2305.07922
24. Alam, I., & Singh, S. N. (2015). Mitigating cloud security challenges with intrusion detection technique. Splint International Journal of
Professionals, 2(6), 7–15.
25. Wang, Y., Wang, W., Joty, S., & Hoi, S. C. H. (2021). CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code
understanding and generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 8696–
8708). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.685
26. Xia, C. S., Deng, Y., Dunn, S., & Zhang, L. (2024). Agentless: Demystifying LLM-based software engineering agents. arXiv.
https://arxiv.org/abs/2407.01489
27. Bihari, S., & Alam, M. I. (2025). Leveraging recommender systems for course selection in higher education: A pathway to informed decision-
making. In Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025) (Advances in
Intelligent Systems Research). Atlantis Press. https://doi.org/10.2991/978-94-6463-787-8_27
28. Yang, J., Jimenez, C. E., Wettig, A., Lieret, K., Yao, S., Narasimhan, K., & Press, O. (2024). SWE-agent: Agent-computer interfaces enable
automated software engineering. arXiv. https://arxiv.org/abs/2405.15793
29. Zhang, F., Chen, B., Zhang, Y., Keung, J., Liu, J., Zan, D., Mao, Y., Lou, J.-G., & Chen, W. (2023). RepoCoder: Repository-level code
completion through iterative retrieval and generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language
Processing (pp. 2471–2484). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.151
30. European Union. (2024). Regulation (EU) 2024/1689: Artificial Intelligence Act. Official Journal of the European Union.
large language models. IEEE Transactions on Emerging Topics in Computational Intelligence. https://doi.org/10.1109/TETCI.2024.3446695.
2. Bi, Y., Li, Y., Feng, X., & Mi, X. (2024). Enabling privacy-preserving cyber threat detection with federated learning. arXiv.
https://arxiv.org/abs/2404.05130
3. Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. de O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021).
Evaluating large language models trained on code. arXiv. https://arxiv.org/abs/2107.03374
4. Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., & Zhang, J. M. (2023). Large language models for software
engineering: Survey and open problems. In 2023 IEEE/ACM International Conference on Software Engineering: Future of Software
Engineering (pp. 31–53). IEEE.
5. Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, T., Liu, T., Jiang, D., & Zhou, M. (2020). CodeBERT: A pre-
trained model for programming and natural languages. In Findings of the Association for Computational Linguistics: EMNLP 2020 (pp.
1536–1547). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.findings-emnlp.139
6. Guo, D., Ren, S., Lu, S., Feng, Z., Tang, D., Liu, S., Zhou, L., Duan, N., Fu, S., Tufano, M., et al. (2021). GraphCodeBERT: Pre-training
code representations with data flow. In International Conference on Learning Representations.
7. Alam, M. I., & Singh, S. N. (2021). Designing and implementing cloud security using multi-layer DNA cryptography in Python. In M.
Chakraborty, R. K. Jha, V. E. Balas, S. N. Sur, & D. Kandar (Eds.), Trends in wireless communication and information security (Lecture
Notes in Electrical Engineering, Vol. 740, pp. 375–385). Springer. https://doi.org/10.1007/978-981-33-6393-9_38
8. Alam, M. I. (2020). Enhancing cloud security using multi-level DNA cryptography. Splint International Journal of Professionals, 7(1), 75–
82.
9. Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J., & Wang, H. (2024). Large language models for software
engineering: A systematic literature review. ACM Transactions on Software Engineering and Methodology, 33(8), 1–79.
https://doi.org/10.1145/3695988
10. Islam, N. T., Khoury, J., Seong, A., Bou-Harb, E., & Najafirad, P. (2024). Enhancing source code security with LLMs: Demystifying the
challenges and generating reliable repairs. arXiv. https://arxiv.org/abs/2409.00571
11. Jiang, J., Wang, F., Shen, J., Kim, S., & Kim, S. (2024). A survey on large language models for code generation. arXiv.
https://arxiv.org/abs/2406.00515
12. Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., & Narasimhan, K. (2024). SWE-bench: Can language models resolve real-
world GitHub issues? In International Conference on Learning Representations.
13. Li, J., Rabbi, F., Cheng, C., Sangalay, A., Tian, Y., & Yang, J. (2024). An exploratory study on fine-tuning large language models for secure
code generation. arXiv. https://arxiv.org/abs/2408.09078
14. Li, R., Allal, L. B., Zi, Y., Muennighoff, N., Kocetkov, D., Mou, C., Marone, M., et al. (2023). StarCoder: May the source be with you!
arXiv. https://arxiv.org/abs/2305.06161
15. Li, Y., et al. (2022). Competition-level code generation with AlphaCode. Science, 378, 1092–1097.
16. Li, Y., Zou, D., Xu, S., Ou, X., Jin, H., Wang, S., Deng, Z., & Zhong, Y. (2018). VulDeePecker: A deep learning-based system for
vulnerability detection. In Proceedings of the 25th Annual Network and Distributed System Security Symposium (NDSS).17. MITRE. (2025). MITRE ATLAS: Adversarial threat landscape for artificial-intelligence systems. MITRE.
18. National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0). U.S. Department
of Commerce.
19. National Institute of Standards and Technology. (2024). Artificial intelligence risk management framework: Generative artificial intelligence
profile (NIST AI 600-1). U.S. Department of Commerce.
20. Open Worldwide Application Security Project. (2025). OWASP Top 10 for large language model applications. OWASP Foundation.
21. Rozière, B., Gehring, J., Gloeckle, F., Sootla, S., Gat, I., Tan, X. E., Adi, Y., Liu, J., Sauvestre, R., Remez, T., et al. (2023). Code Llama:
Open foundation models for code. arXiv. https://arxiv.org/abs/2308.12950
22. UNESCO. (2021). Recommendation on the ethics of artificial intelligence. UNESCO.
23. Wang, Y., Le, H., Gotmare, A. D., Bui, N. D. Q., Li, J., & Hoi, S. C. H. (2023). CodeT5+: Open code large language models for code
understanding and generation. arXiv. https://arxiv.org/abs/2305.07922
24. Alam, I., & Singh, S. N. (2015). Mitigating cloud security challenges with intrusion detection technique. Splint International Journal of
Professionals, 2(6), 7–15.
25. Wang, Y., Wang, W., Joty, S., & Hoi, S. C. H. (2021). CodeT5: Identifier-aware unified pre-trained encoder-decoder models for code
understanding and generation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (pp. 8696–
8708). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.emnlp-main.685
26. Xia, C. S., Deng, Y., Dunn, S., & Zhang, L. (2024). Agentless: Demystifying LLM-based software engineering agents. arXiv.
https://arxiv.org/abs/2407.01489
27. Bihari, S., & Alam, M. I. (2025). Leveraging recommender systems for course selection in higher education: A pathway to informed decision-
making. In Proceedings of the Recent Advances in Artificial Intelligence for Sustainable Development (RAISD 2025) (Advances in
Intelligent Systems Research). Atlantis Press. https://doi.org/10.2991/978-94-6463-787-8_27
28. Yang, J., Jimenez, C. E., Wettig, A., Lieret, K., Yao, S., Narasimhan, K., & Press, O. (2024). SWE-agent: Agent-computer interfaces enable
automated software engineering. arXiv. https://arxiv.org/abs/2405.15793
29. Zhang, F., Chen, B., Zhang, Y., Keung, J., Liu, J., Zan, D., Mao, Y., Lou, J.-G., & Chen, W. (2023). RepoCoder: Repository-level code
completion through iterative retrieval and generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language
Processing (pp. 2471–2484). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.151
30. European Union. (2024). Regulation (EU) 2024/1689: Artificial Intelligence Act. Official Journal of the European Union.
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