Core Concepts
Improving code security through prompt engineering methods for AI-based code synthesis.
Abstract
The study focuses on enhancing the security of AI-generated code, specifically using GitHub Copilot. It reviews current approaches, proposes prompt-altering methods, and evaluates their effectiveness. Three main methods are discussed: scenario-specific, iterative, and general clause. The study aims to reduce insecure code samples and increase secure code by altering prompts systematically.
Abstract:
AI assistants for coding are gaining popularity.
Concerns about the security of generated code hinder full utilization.
Proposed systematic approach based on prompt-altering methods.
Evaluation on GitHub Copilot using OpenVPN project shows promising results.
Introduction:
Shift towards AI coding assistants observed.
AlphaCode 2 outperformed human competitors in a recent model.
Survey results show high usage of GitHub Copilot among developers.
Background and Design Space:
Three main areas for improving LLM code-generating abilities: output optimizing, model fine-tuning, prompt optimizing.
Design space considerations for each method's pros and cons.
Proposed Approach:
Scenario-Specific Method:
Provides specific information about local context to the AI assistant.
Requires expert knowledge but can automate prompt alterations based on context.
Iterative Method:
Applies repeated process to prompt alteration by modifying commentary iteratively.
Agnostic to task and context, requires proper selection of commentaries sequence.
General Alignment Shifting Method:
Inspired by inception prompt concept but differs in conversation pattern.
Simple implementation with potential performance issues.
Experiments:
Experiment design includes selecting tasks from OpenVPN project for evaluation.
Methods applied differently to alter prompts: scenario-specific, iterative, general clause.
Manual assessment of synthesized code security into secure, partially secure, insecure categories.
Related Work:
Studies evaluating security implications of large language models' code assistants reviewed.
Focus on empirical evaluation of average security in synthesized code observed in recent research studies.
Discussion:
Limitations include trade-offs in prompt additions and dataset limitations.
Future research directions include exploring potential improvements in model fine-tuning methods.
Conclusion:
Systematic approach proposed to enhance security of AI-generated code through prompt engineering methods.
Results indicate improved performance in terms of code security with proposed methods.
Stats
According to Liang et al., 70% of respondents use GitHub Copilot monthly while 46% use it daily.
The proposed methods reduced insecure generated code samples by up to 16% and increased secure code by up to 8%.
Quotes
"AI assistants for coding are proficient in many areas."
"The proposed systematic approach aims at bettering the security of generated code."
"Our results indicate that the proposed methods can enhance the security of generated code."