Secure code generation with LLMs (Record no. 23296)
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| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250811122315.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250811b xxu||||| |||| 00| 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | AIKTC-KRRC |
| Transcribing agency | AIKTC-KRRC |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| 9 (RLIN) | 27025 |
| Author | Bar, Kaushik |
| 245 ## - TITLE STATEMENT | |
| Title | Secure code generation with LLMs |
| Remainder of title | : risk assessment and mitigation strategies |
| 250 ## - EDITION STATEMENT | |
| Volume, Issue number | Vol.17(1), Feb |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | Hyderabad |
| Name of publisher, distributor, etc. | IUP Publications |
| Year | 2024 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pagination | 75-95p. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | Artificial intelligence (AI)-powered code generation tools, such as GitHub Copilot and OpenAI Codex, have revolutionized software development by automating code synthesis. However, concerns remain about the security of AI-generated code and its susceptibility to vulnerabilities. This study investigates whether AI-generated code can match or surpass human-written code in security, using a systematic evaluation framework. It analyzes AIgenerated code samples from state-of-the-art large language models (LLMs) and compares them against human-written code using static and dynamic security analysis tools. Additionally, adversarial testing was done to assess the robustness of LLMs against insecure code suggestions. The findings reveal that while AI-generated code can achieve functional correctness, it frequently introduces security vulnerabilities, such as injection flaws, insecure cryptographic practices, and improper input validation. To mitigate these risks, securityaware training methods and reinforcement learning techniques were explored to enhance the security of AI-generated code. The results highlight the key challenges in AI-driven software development and propose guidelines for integrating AI-assisted programming safely in real-world applications. This paper provides critical insights into the intersection of AI and cybersecurity, paving the way for more secured AI-driven code synthesis models. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| 9 (RLIN) | 4619 |
| Topical term or geographic name entry element | EXTC Engineering |
| 773 0# - HOST ITEM ENTRY | |
| International Standard Serial Number | 0975-5551 |
| Title | IUP Journal of telecommunications |
| Place, publisher, and date of publication | Hyderabad IUP Publications |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| URL | https://iupindia.in/ViewArticleDetails.asp?ArticleID=7759 |
| Link text | Click here |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Articles Abstract Database |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Shelving location | Date acquired | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
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| Dewey Decimal Classification | School of Engineering & Technology | School of Engineering & Technology | Archieval Section | 11/08/2025 | 2025-1294 | 11/08/2025 | 11/08/2025 | Articles Abstract Database |