剖析AI编程工具的幻觉与安全风险，助力开发者安全部署。
SOK: Exploring Hallucinations and Security Risks in AI-Assisted Software Development with Insights for LLM Deployment
- 对比GitHub Copilot等工具，分析其生成代码的漏洞与偏差。
- 发现工具易复制不安全编码模式并产生无意义代码。
- 适合关注AI开发安全的工程师与团队决策者参考。
大型语言模型（如GitHub Copilot、ChatGPT、Cursor AI和Codeium AI）在软件开发中的集成已显著提升生产力，实现代码生成、重构与实时调试支持。然而，其广泛应用也带来安全隐患、代码质量下降与伦理问题。本文通过用户反馈、安全分析与实际案例，系统评估了这些工具在复制不安全编码实践、引入偏见及生成错误或荒谬代码（幻觉）方面的风险。同时探讨数据泄露、知识产权侵犯等威胁，强调需建立强安全机制。通过对比各工具的功能与性能，旨在帮助开发者合理选择与部署，最大化AI辅助编程效益，降低潜在风险。
原文摘要 · Abstract (English)
The integration of Large Language Models (LLMs) such as GitHub Copilot, ChatGPT, Cursor AI, and Codeium AI into software development has revolutionized the coding landscape, offering significant productivity gains, automation, and enhanced debugging capabilities. These tools have proven invaluable for generating code snippets, refactoring existing code, and providing real-time support to developers. However, their widespread adoption also presents notable challenges, particularly in terms of security vulnerabilities, code quality, and ethical concerns. This paper provides a comprehensive analysis of the benefits and risks associated with AI-powered coding tools, drawing on user feedback, security analyses, and practical use cases. We explore the potential for these tools to replicate insecure coding practices, introduce biases, and generate incorrect or non-sensical code (hallucinations). In addition, we discuss the risks of data leaks, intellectual property violations and the need for robust security measures to mitigate these threats. By comparing the features and performance of these tools, we aim to guide developers in making informed decisions about their use, ensuring that the benefits of AI-assisted coding are maximized while minimizing associated risks.
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