如何強化正式環境中的 AI 智能體，防範提示詞注入及工具劫持
How to Harden Production AI Agents Against Prompt Injection & Tool Hijacking
文章提出將 AI 智能體部署至正式環境時，間接提示詞注入可能帶來遠端程式碼執行（RCE）及資料外洩風險。指南列出三項經生產環境驗證的防禦措施，協助智能體防範 OWASP LLM Top 10 漏洞。
If you are moving AI agents from a toy script into production with enterprise clients, you quickly encounter a harsh reality: Traditional cybersecurity firewalls know nothing about probabilistic LLMs.
When your agent has access to tools (like SQL databases, web scrapers, shell commands, or Model Context Protocol servers), indirect prompt injection is not just an annoying glitch — it is a severe Remote Code Execution (RCE) and data exfiltration threat surface.
In this guide, we break down three production-proven defenses you can implement today to harden your AI agents against OWASP LLM Top 10 vulnerabilities.
Threat 1: Indirect Prompt Injection via Untrusted Context
Most agent developers concatenate user input or web crawl data directly into their prompts like this:
python
# VULNERABLE: Direct string interpolation
prompt = f"Summarize this customer ticket: {untrusted_customer_input}"
來源：DEV Community · dev.to