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Inspect the complete, final request immediately before your application sends it to an LLM provider. Include more than the visible user message: system and developer instructions, retrieved documents, tool arguments, metadata, structured fields, and text or media derived from files can all carry sensitive data or untrusted instructions. Remove or redact information the task does not need, block requests that violate your policy, and enforce permissions in trusted application code. A scanner can help, but it cannot guarantee that a request is safe.
What can an outbound LLM request contain?
The request is the full payload your application is about to transmit—not just the text a user typed. Its contents depend on your app and the provider endpoint, but may include:
- System and developer instructions, including rules or context assembled by your application.
- User messages and conversation history.
- Retrieved text from websites, files, databases, or connectors.
- Tool definitions, tool arguments, and structured input fields.
- Metadata and text or media extracted from uploaded files.
Review the final serialized request at the network boundary. Inspecting an earlier form or only the user-visible message can miss content added later by retrieval, orchestration, or tool-building code.
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How to inspect the request before sending it
- Capture the final payload. Intercept or construct the serialized request immediately before the provider call, after all prompts, retrieved material, tool inputs, and other fields have been assembled.
- Inventory content-bearing fields. Identify every place that can carry text or media, including instructions, conversation history, retrieved content, tool arguments, metadata, and file-derived material. Keep the inventory aligned with the actual endpoint and request format your application uses.
- Apply your data policy. Remove fields that are unnecessary for the task. Redact sensitive values when the task can proceed without them, and block requests that breach your application’s rules. Avoid placing credentials or other secrets in prompts.
- Treat external content as untrusted. User input, websites, files, and connector results can contain instructions intended to manipulate a model. Flag suspicious material where useful, but do not rely on a universal detector to identify every prompt injection.
- Enforce decisions outside the model. Validate tool calls and sensitive actions in trusted application code. A model-readable instruction is not an access-control boundary.
- Send only after the checks pass. Make the allow, redact, or block decision part of the application’s request path so that a failed check cannot silently become an unchecked transmission.
What inspection can—and cannot—protect against
It can reduce unnecessary disclosure
Minimizing the amount of information sent limits exposure when a prompt, retrieved document, or application workflow contains data the task does not require. OpenAI’s Safety best practices says: “Limiting the amount of text a user can input into the prompt helps avoid prompt injection.” That is a mitigation, not a guarantee that remaining content is safe.
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It cannot guarantee prompt-injection detection
Prompt injection can appear as harmful instructions embedded in otherwise ordinary content, including material from external sources. OpenAI’s November 7, 2025 explanation of prompt injection describes the risk, while its deep research guidance warns that tools and external sources create security risks and that no automated filter catches every case. Pre-send inspection can flag or block some risky material, but false positives and missed detections remain possible.
It does not replace authorization
Keep secrets and authorization decisions in application-controlled systems. OWASP’s LLM07:2025 System Prompt Leakage warns against storing secrets in prompts or relying on prompt secrecy for access control. Check permissions before executing a tool or sensitive action, regardless of what the model requests or what an inspection layer approves.
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Check retention for the exact provider route
Inspection before transmission does not tell you what happens after the provider receives the request. Review the provider’s current documentation for the specific endpoint, account configuration, and route your application uses. Distinguish provider abuse-monitoring logs from application state, and verify any retention controls or exceptions that apply.
For example, OpenAI’s API data-controls documentation, reviewed October 7, 2026, says API content is not used to train or improve models unless a customer opts in, and describes abuse-monitoring logs as retained for up to 30 days by default. That duration is a default subject to endpoint, account settings, and applicable exceptions; it should not be read as a universal retention period for every kind of API data.
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Anthropic’s Claude Platform API and data retention documentation describes API retention and zero-data-retention arrangements. Requests made through Amazon Bedrock or Google Cloud are subject to the respective cloud provider’s processing and retention documentation. Do not assume one provider’s policy applies to another route, or that a broad statement means a particular request is never stored.
Log inspection without creating another exposure
Inspection logs can help with debugging and monitoring, but copying complete prompts into logs creates another place where sensitive content may be stored or accessed. Decide what the application needs to retain, restrict access to those records, and avoid logging full payloads when a lower-detail outcome—such as a rule triggered or a request blocked—will serve the purpose. This is an application design choice, not a universal logging prescription.
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Build the review into the request path
A useful design separates three questions: what is being sent, whether it is needed, and whether the application is allowed to act on it. Reviewing the final payload addresses the first; minimization and redaction address the second; trusted application-side permission checks address the third. Keep those controls distinct, because success in one does not establish success in the others.
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