Impeding LLM-assisted Cheating in Introductory Programming Assignments via Adversarial Perturbation

• URL: http://arxiv.org/abs/2410.09318v2
• Date: Tue, 15 Oct 2024 05:48:40 GMT
• Title: Impeding LLM-assisted Cheating in Introductory Programming Assignments via Adversarial Perturbation
• Authors: Saiful Islam Salim, Rubin Yuchan Yang, Alexander Cooper, Suryashree Ray, Saumya Debray, Sazzadur Rahaman,
• Abstract summary: Large language model (LLM)-based programming assistants can help improve the productivity of professional software developers, but can also facilitate cheating in introductory computer programming courses. This paper investigates the baseline performance of 5 widely used LLMs on a collection of introductory programming problems, examines adversarial perturbations to degrade their performance, and describes the results of a user study aimed at understanding the efficacy of such perturbations in hindering actual code generation for introductory programming assignments.
• Score: 42.49889252988544
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: While Large language model (LLM)-based programming assistants such as CoPilot and ChatGPT can help improve the productivity of professional software developers, they can also facilitate cheating in introductory computer programming courses. Assuming instructors have limited control over the industrial-strength models, this paper investigates the baseline performance of 5 widely used LLMs on a collection of introductory programming problems, examines adversarial perturbations to degrade their performance, and describes the results of a user study aimed at understanding the efficacy of such perturbations in hindering actual code generation for introductory programming assignments. The user study suggests that i) perturbations combinedly reduced the average correctness score by 77%, ii) the drop in correctness caused by these perturbations was affected based on their detectability.

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