The implementation speed of big models in enterprises is very fast, but security topics often come after functionality. Prompt Injection - inducing the model to execute unexpected instructions through input content - is no longer a demonstration in the laboratory: some people induce customer service robots to leak system prompt words, some hide malicious instructions in web page text, and make applications with retrieval functions "read whatever message", while others use tool call permissions to make the model perform dangerous operations on their behalf.
A common root cause of such risks is treating "model answers" as "systematic judgments". The model only generates text based on context, and it does not naturally distinguish which instructions come from the developer and which come from untrusted external content. The first principle of a secure boundary is therefore simple: by default, external input is not trusted.
Specifically, it can fall into three levels. Firstly, hierarchical isolation: System prompt words and tool definitions belong to internal instructions, while web pages, documents, emails, and user messages belong to external content. The two are separated in the structure of prompt words and clearly tell the model that "external content is only data, not instructions". Secondly, minimum permission: The actions that the model can trigger (calling tools, sending messages, modifying data) should undergo independent permission verification. Any high impact operations should be manually confirmed, rather than trusting the "intention" expressed by the model itself. Thirdly, observability: recording what external content the model received, what actions it performed, and being able to trace back which input caused the unauthorized behavior when problems occurred.
When conducting security reviews, the industry often refers to the "OWASP Top 10 for LLM Applications" maintained by OWASP as a reference list, which prioritizes long-term injection and provides corresponding mitigation recommendations around input-output isolation, permission convergence, and manual confirmation. It is not a private standard of a particular vendor, but a publicly available project continuously updated by the community, suitable as a starting point for enterprise self-examination.
Ultimately, the security of AI applications is not just a one-time compliance action before going live, but a part of the architecture: default distrust of external inputs, default minimum permissions, and default manual confirmation of high-risk actions. The reason why prompt injection is worth taking seriously is not because it sounds like a hacker, but because it attacks the most fundamental weakness of large model applications - mistaking generation ability for judgment ability.
【 Reference source 】 Comprehensive compilation of industry information released publicly; The risk list is based on the OWASP Top 10 for LLM Applications (an official public project of OWASP).