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What counts as an AI prompt or context window?
A context window is the information available to a model while it responds. It can include your prompt and conversation history, but an AI coding assistant or agent may also collect project structure, file contents, terminal output, and results returned by tools. The exact data sent depends on the product and its configuration, so review the vendor’s documentation and settings rather than assuming the visible chat box is the whole context. OWASP’s Secure Coding with AI Cheat Sheet discusses these context-leakage paths.
Secrets include API keys, passwords, private keys, connection strings, tokens, and sensitive customer or business data. If a tool can read or transmit a file or command result, that information may be exposed even if you never pasted it into a chat.
How to keep secrets out of AI prompts and context windows
1. Keep credentials out of prompts
Do not paste API keys, passwords, private keys, connection strings, or real customer records into a prompt. Use placeholders such as YOUR_API_KEY or REDACTED_TOKEN in examples and debugging questions. Microsoft Learn’s Security and responsible AI for Windows development says, “Never paste API keys, passwords, or connection strings into a prompt.” It warns that credentials in prompts can pose a security risk and may appear in logs, including when using a private session.
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2. Store secrets outside AI-readable project files
Keep credentials out of files within a project tree that an AI coding tool can inspect. Use a vault service, encrypted secret store, or runtime credential facility suited to your application and deployment. Microsoft lists environment variables and Azure Key Vault as examples for server-side and CI service credentials, and describes Windows.Security.Credentials.PasswordVault for a Windows app. Those are examples for particular environments, not interchangeable solutions for every project. See Microsoft’s credential-handling guidance and OWASP’s AI coding guidance.
3. Configure the AI product’s context exclusions
Use the AI assistant’s own settings to exclude secret-bearing files and directories. OWASP gives these patterns and filenames as examples:
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Some tools provide product-specific exclusion files, such as .cursorignore or .copilotignore. The syntax, coverage, and behavior vary; confirm the current documentation for your particular product and test whether exclusions cover the files and tools you use. .gitignore controls what Git ignores; it is not an AI context boundary. OWASP describes these safeguards in its Secure Coding with AI Cheat Sheet.
4. Limit assistant access and tool permissions
Give an assistant only the files, systems, and tools needed for its current task. For agent workflows, enforce authentication, authorization, privilege separation, and limits in deterministic systems outside the model. Review consequential actions—especially those that transmit sensitive information or affect external systems—before confirming them.
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Prompt injection can arrive through untrusted content placed in the conversation context. It does not mean every AI interaction is compromised, but it is a reason to constrain access and review sensitive actions rather than trusting the model to ignore malicious instructions. OpenAI’s prompt-injection guidance discusses least access and explicit task scope; its guidance applies to OpenAI products and should not be assumed to describe every provider’s safeguards.
5. Audit context and test the workflow
Learn what your specific AI product sends and where it sends it. Where appropriate, inspect outbound requests through available logs or a network proxy. Test that exclusions and permission boundaries work as intended, including with adversarial inputs. OpenAI’s API safety best practices recommend adversarial testing and human review; its agent prompt-injection guidance explains why filtering alone is not a sufficient defense.
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- 【Superior Privacy】- Our advanced multi-layered film filter blacks out your screen when viewing from the side, while maintaining a crystal clear screen straight-on. It also protects your eyes from harmful UV and blue light. [Note: It does not block visibility directly behind you, regardless of the distance.]
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Do not use live credentials to test a control unless the test is controlled and uses a safe canary value. A prompt filter or an instruction telling the model to keep secrets private is not a substitute for keeping the secrets out of context in the first place.
6. Match deployment to the data’s sensitivity
For classified, regulated, or highly sensitive code, OWASP recommends considering self-hosted or air-gapped coding tools. Confirm that the particular deployment’s data flows and organizational approvals meet your requirements; a product label by itself does not establish that sensitive data is protected. See OWASP’s guidance on AI coding tools.
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Why system prompts are not a place to hide secrets
Do not put credentials in a system prompt or rely on its instructions to keep information secure. OWASP’s Gen AI Security Project states in its LLM07:2025 System Prompt Leakage guidance: “It’s important to understand that the system prompt should not be considered a secret, nor should it be used as a security control.” Treat system prompts as instructions, not as a protected vault or an authorization boundary.
Quick Recap
Quick checklist before using an AI assistant
- Remove real credentials and sensitive records from prompts, examples, and system instructions.
- Keep secret-bearing files outside AI-readable project trees where possible.
- Set and test exclusions in the AI product itself; do not rely on
.gitignore. - Check whether open files, project metadata, terminal output, and tool results can enter context.
- Grant the assistant only the access and permissions required for the task.
- Enforce authorization outside the model and review sensitive transmissions or external actions.
- Audit data flows and test controls without exposing live credentials.
- For highly sensitive work, verify that the deployment and its data flows satisfy organizational policy.
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