提示词注入是通过精心构造的输入内容绕过 AI 系统的指令优先级或安全限制、诱导大模型执行非预期行为的攻击技术，核心是把恶意指令伪装成正常输入混入上下文，能力包括直接注入与间接注入的识别、输入清洗、上下文隔离与输出审核。它解决的是大模型无法可靠区分指令与数据这一结构性弱点带来的越权风险，并不解决用户输入本身缺乏可信来源验证的问题。
提示词注入的核心是识别伪装成数据的指令，它可以帮助企业实现以下目标：
让直接注入在入口被拦截。用户输入中的越权指令被识别与清洗，不进入模型的有效上下文。攻击特征库随真实攻击案例持续更新，新的绕过手法出现时规则可以当天补上，而不是等大规模事件后再修。
让间接注入在源头被识别。外部文档、网页与邮件中携带的恶意指令被单独标注与隔离，而非当作可信资料使用。注入来源被区分对待——外部文档里的指令与系统自身的指令在上下文中物理隔离，模型收到的结构本身就限制了越权可能。
让上下文按最小必要暴露。系统提示与工具权限不整体外泄，攻击者能借以构造指令的素材被削减。
让工具调用有独立校验。高风险动作在执行前经过独立规则或二次模型确认，不因模型被诱导就直接生效。
总之，提示词注入可以帮助企业实现大模型应用从能演示到可上线的转变，把第一号应用层威胁纳入工程防线，通过多层设防降低智能客服、Agent 与 RAG 场景被诱导执行未授权操作的风险。
翻译
Prompt injection is an attack technique that bypasses the instruction priority or safety limits of AI systems through carefully crafted input content, inducing large language models to perform unintended behavior. Its core is disguising malicious instructions as ordinary input mixed into context, with capabilities covering identification of direct and indirect injection, input sanitization, context isolation and output review. It solves the privilege-escalation risk arising from the structural weakness that large models cannot reliably distinguish instructions from data, but it does not solve the problem that user input itself lacks trusted-source verification.
At the heart of prompt injection defense is recognizing instructions disguised as data, which can help companies achieve the following:
Intercept direct injection at the entry point. Overreaching instructions in user input are identified and sanitized before entering the model’s effective context.
Identify indirect injection at the source. Malicious instructions carried in external documents, web pages and email are separately labeled and isolated rather than treated as trusted material.
Expose only the minimum necessary context. System prompts and tool permissions are not leaked wholesale, reducing the material attackers can use to construct instructions.
Independently validate tool calls. High-risk actions pass a separate rule check or a second-model confirmation before execution, so induced instructions do not take effect directly.
In short, prompt injection defense can help enterprises shift large model applications from demonstrable to production-ready, bring the number-one application-layer threat into the engineering perimeter, and reduce the risk of unauthorized actions being induced in agent, RAG and customer-service scenarios.
