構建安全的 AI Agent：Vercel 的安全防護指南與最佳實踐
Original: Building secure AI agents
As large language models (LLMs) have evolved, AI applications have moved beyond simple "question-and-answer conversations" toward "AI…
隨著 AI Agent 從單純對話走向自主執行任務，安全挑戰日益嚴峻。Vercel 釋出指南，探討如何透過安全沙盒（如 E2B）隔離程式碼執行、利用 IAM 限制 Agent 權限、防範提示詞注入，以及在關鍵步驟引入「人工確認（Human-in-the-Loop）」機制，幫助開發者在 Vercel 平台上構建兼具功能與安全性的 AI 應用。
As large language models (LLMs) have evolved, AI applications have moved beyond simple "question-and-answer conversations" toward "AI Agents" capable of autonomously executing tasks. These agents can call external APIs, read and write databases, and even generate and execute code on their own. However, this powerful autonomy also introduces unprecedented security challenges. Vercel recently published a guide that delves into the security threats developers must confront when building AI agents, and proposes practical defensive architectures.
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