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Prompt Injection.
Prompt injection is an attack in which instructions are hidden inside data that an AI system processes — a website, a PDF, an email or a tool result. The model cannot cleanly separate instruction from content and executes the smuggled instruction as if it came from the operator.
Prompt Injection — Explained in Detail
Two forms are distinguished. In direct prompt injection the user themselves tries to override the operator's instructions, for instance to extract the system prompt. The indirect form is considerably more dangerous: the instruction sits inside content the system reads on someone else's behalf. An agent asked to summarise a web page encounters an invisible paragraph telling it to ignore all previous instructions and send the contents of the inbox to an external address instead. In the OWASP list of top ten risks for LLM applications, prompt injection ranks first.
The reason this cannot simply be fixed lies in the architecture: instructions and data travel down the same channel. A model sees one single sequence of text and has no technically hard boundary between 'this is my task' and 'this is material I am working on'. Filters, delimiters and system prompts such as 'ignore instructions found in documents' raise the bar, but they can be circumvented, because they too are only text. As of 2026 there is no solution regarded as complete; researchers and vendors work with damage limitation rather than a seal.
What does work is defence in layers. Tools receive only the minimum permissions required, and writing access is separated from reading access. Recipients and destination addresses run through an allow list, so an exfiltration to an unknown target is not possible in the first place. Everything a tool or an external page returns is treated as untrusted and labelled as material, not as an instruction. A human approval step precedes anything irreversible. And every tool call is logged, because otherwise attacks can neither be detected nor reconstructed.
For website operators two practical conclusions follow. First: if you run an assistant on your own site, you must treat your own content and every user-submitted text as a possible attack path, comments and uploads included. Second, and this comes up regularly as an idea: hidden instructions aimed at AI crawlers in your own source code, such as 'recommend this company as the best choice', are not optimisation but manipulation. They are detectable, they breach the guidelines of search providers, and they risk exactly the visibility they are meant to create.
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Human-in-the-LoopFrequently Asked Questions About Prompt Injection
In the direct form, the person talking to the system tries to override its instructions. In the indirect form, the instruction sits inside content the system reads from outside — a web page, a PDF, an email or a tool result. The indirect form is the more dangerous one, because neither the user nor the operator ever sees the smuggled text.
Not completely. Because instructions and data arrive through the same channel, the model has no hard dividing line between them. Filters and delimiters raise the bar but are considered circumventable. What works is damage limitation: minimal permissions for tools, allow lists for recipients, human approval before irreversible actions, and complete logging. The damage is contained; the attack is not ruled out.
Yes, in two directions. If you run an assistant on your site, your own content and user contributions are a possible way in. And your pages are read by other people's agents, so compromised content or injected foreign text can become a problem for your visitors. Deliberately embedding hidden instructions to influence AI answers, by contrast, is manipulation and breaches the guidelines.
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