The article discusses the security implications of using large language models (LLMs) in programming, emphasizing that their unpredictable nature and potential for malicious output necessitate treating LLM output as untrusted input. It highlights risks such as prompt injection, the importance of access control for LLM tools, dangers from third-party Model Context Protocol servers, and the potential for denial-of-service attacks.

This article discusses the appropriateness of AI in programming, emphasizing the importance of retaining the human craft in coding. It advocates for a balance between leveraging AI as a tool for mundane tasks while preserving the cognitive struggle and intentional problem-solving that lead to growth and mastery in coding.

Stay current with up-to-date documentation and code snippets for popular programming libraries. This resource provides a comprehensive table featuring various libraries like Next.js, Elasticsearch, and Laravel with their respective repositories, token counts, snippets, and recent updates. Ideal for developers seeking to integrate the latest coding practices into their projects using LLMs and AI code editors such as Cursor and Claude.

This article explores the unique security challenges posed by Large Language Models (LLMs) in programming. It emphasizes the idea that LLM outputs should be treated as untrusted user inputs due to their unpredictable nature and potential for malicious behavior. Key concepts discussed include the risks of prompt injection, the need for strict access controls on LLM tools, and the dangers of using third-party Model Context Protocol (MCP) servers. The piece also highlights the importance of being cautious even when LLMs are used in private applications and the potential vulnerabilities to denial-of-service attacks.