Recommended Free Tools
Yes. Text that a person cannot see—or may overlook—can influence an AI if the application passes it to the model and the model follows it. When instructions arrive inside a webpage, file, or other external material the AI is processing, OWASP calls this indirect prompt injection. It can steer an answer, but hidden text alone does not automatically give an attacker access to a computer or private data; the potential impact depends on the application’s permissions and safeguards.
How can hidden text affect an AI?
An AI model processes the representation of content supplied to it, not just what a person can see on a screen. If an application extracts text from a document or webpage and includes that text in the model’s input, instructions embedded in the material may influence the model’s response. OWASP describes inputs that are imperceptible to people but parsed by a model as a prompt-injection risk: OWASP LLM01:2025 Prompt Injection.
Hidden presentation and hidden encoding are different ways content may be difficult for a person to notice. Examples include text visually obscured in a page or document, and characters encoded so they are not readily visible. Whether either method matters depends on the application’s input pipeline: does it extract or otherwise pass that content to the model? OWASP discusses these patterns in its LLM Prompt Injection Prevention Cheat Sheet.
What is indirect prompt injection?
In a direct prompt injection, the user’s own input contains instructions intended to alter the model’s behavior. In an indirect prompt injection, the instructions arrive through external content the model is asked to process, such as an uploaded document or webpage. The person chatting with the AI does not have to be the person who put those instructions there. OWASP’s 2025 guidance distinguishes these sources of instruction.
Free tools Windows power users keep installed
One-click scans. No signup required.
#1 Best Overall
What could happen—and what does not happen automatically?
Imagine asking an assistant to summarize a webpage. The page includes instructions aimed at the AI. If the application passes those instructions to the model and the model follows them, the summary or later behavior may be steered away from what the user intended. The older OWASP LLM application guidance describes webpage and document-processing scenarios.
The result might be a misleading answer. In a system with access to sensitive information or connected tools, the consequences could be more serious, including disclosure of information or actions through those functions. But a hidden instruction does not, by itself, prove that the model will obey it, that a tool will run, or that private data will be exposed. Those outcomes depend on the model’s behavior and the surrounding application’s access controls, authorization checks, and approval steps. OWASP’s 2025 guidance discusses both the risks and the role of connected functionality at LLM01:2025 Prompt Injection.
How can developers reduce the risk?
There is no established guarantee that every prompt injection can be prevented. OWASP cautions that fool-proof prevention is unclear, so defenses should reduce the likelihood of successful manipulation and limit its impact rather than promise that hidden instructions will always be caught. Its guidance supports a layered approach:
- Mark external content as untrusted. Treat retrieved webpages, uploaded files, email, and tool output as data to analyze—not as authoritative instructions—and keep that content clearly identified and separated from the application’s own instructions.
- Limit access and permissions. Give the model and connected application only the data and tools needed for the task. Enforce authorization in application code; do not rely on the model to decide whether access is allowed.
- Require approval for consequential actions. Add an independent confirmation step before actions such as sending or deleting information.
- Validate outputs and formats. Check that responses meet expected requirements and use input and output checks as supporting controls, not as proof that all malicious instructions have been removed.
- Test and monitor. Run adversarial tests using realistic external content, and repeat them as the application, its tools, and its data access change.
These measures align with OWASP’s 2025 Prompt Injection guidance and prevention cheat sheet.
What should users do?
For an AI assistant that reads files or webpages, use only the account and file access it needs. Review proposed actions before approving them, and check important summaries or recommendations against the original source. These steps reduce exposure and help catch errors; they cannot guarantee that a model will ignore every hidden instruction.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
