"You still have to study" -- On the Security of LLM generated code
Project Overview
This document examines the role of generative AI, particularly large language models (LLMs), in education and software development, emphasizing their use in generating code. It specifically evaluates four prominent LLMs—GitHub Copilot, ChatGPT, CodeWhisperer, and CodeLlama—while investigating the impact of prompt quality on the security of the generated code. The analysis reveals that the effectiveness of LLMs in producing secure code is highly influenced by the prompting techniques employed. Although the initial outputs often contain vulnerabilities, the findings indicate that with optimal prompting strategies, these models can indeed generate secure and reliable code. The document underscores the potential of generative AI in educational contexts, particularly in teaching coding and software development, while also highlighting the need for careful management of prompts to mitigate risks associated with insecure code generation. Overall, it presents a balanced view of the capabilities and limitations of LLMs in enhancing educational outcomes in programming and software security.
Key Applications
Code generation and security analysis using LLMs
Context: Educational context for software development students
Implementation: Case study with 117 prompts applied to different LLMs (ChatGPT, Copilot, CodeWhisperer, CodeLlama) focusing on security vulnerabilities
Outcomes: Improved security of code generation by utilizing effective prompting techniques, with some LLMs achieving close to 100% secure code with manual guidance
Challenges: Initial generation of insecure code; reliance on the quality of prompts; need for user expertise in security practices
Implementation Barriers
Technical Barrier
Initial code generated by LLMs frequently contains security vulnerabilities due to inadequate training data and prompt quality.
Proposed Solutions: Implementing prompt engineering techniques and providing specific guidance to improve code security.
Educational Barrier
Students may overly rely on AI-generated code without understanding security practices, leading to insecure code.
Proposed Solutions: Educating students on secure coding practices and the importance of prompt quality when using AI tools.
Project Team
Stefan Goetz
Researcher
Andreas Schaad
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Stefan Goetz, Andreas Schaad
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai