Prompt injection refers to an attacker secretly stuffing a command that affects the model's behavior into what the model might read, causing the model to deviate from the tasks or rules it should have followed. It doesn't necessarily look like "malicious code", but often just mixed into the body of a web page, PDF document, knowledge base content, table notes, or even a seemingly ordinary sentence.
Why is it not a loophole in the traditional sense
Traditional vulnerabilities often exploit flaws in the program execution layer; Prompt injection is more like the fact that "using a model to read natural language" itself. The model is good at understanding human language, which is an advantage, but it also means that it may mistake content that should not be an instruction for an instruction. So it's both a security issue and a contextual boundary issue.
The most common injection inlet
- Network search: The web pages captured by the model may contain induced text such as "ignore previous rules" or "output a certain content instead".
- Upload documents: PDFs, Markdown, web snapshots, and KB entries may have words that affect the model's judgment.
- Multi-tool systems: When the model continues to call browsers, databases, and code tools, the impact of injection can be amplified.
Where is its most dangerous place?
| Risk points | performance |
|---|---|
| The rules are overridden | The model begins to ignore system requirements or overreach execution |
| The answer is biased | The model treats dirty content as a trusted context |
| The toolchain is misled | The agent continues to search and adjust the tool according to the contaminated instructions |
There is also a difference between prompt injection and jailbreak prompts. Jailbreaking is usually a user challenging model rules head-on; Prompt injection is more like a rule that is tainted from the side, and the model itself may not realize that the content should not be executed. The reason why it has been mentioned more and more frequently in the past two years is that the model no longer only reads the chat box, but begins to read web pages, read files, read knowledge bases, and connect tools. As the input surface expands, the number of entrances increases.
