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To reduce the chance that API keys or personal information reach a model, inspect and sanitize content at the point it becomes part of an outbound request—not just in a privacy setting or an ignore file. Cursor and Claude Code document useful privacy, exclusion, and permission controls, but those controls do not establish that secret values are removed from model-bound prompts, retrieved code, terminal output, or tool results.
What actually stops a secret from reaching an LLM?
A separate enforcement boundary must see the content before it is sent for inference, apply policy, and prevent transmission when required. In a strong design, that boundary checks the assembled model-bound request, including the prompt and relevant context—not merely files selected in the editor.
This is different from two other kinds of protection:
- Data-use controls govern matters such as provider retention or use of data for training. They do not necessarily remove sensitive values from a request.
- Access and permission controls limit which files or actions an agent can access or perform. They reduce exposure opportunities but do not prove that content the agent can access is sanitized before inference.
“Zero-trust in-memory sanitization” is best understood as a design objective: treat every model-bound string as potentially sensitive, inspect it at an enforcement point, and avoid persisting the sanitizer’s sensitive state. It is not a feature or guarantee established for Cursor or Claude Code by the vendor documentation cited here.
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How Cursor and Claude Code handle data
Cursor: privacy settings and file exclusions have different jobs
Cursor says its AI features send prompts and code context to model providers. Its security documentation describes codebase indexing: files are hashed and synchronized, server-side services create embeddings, and relevant code chunks can be returned to the client and sent to the server for inference. Cursor describes .cursorignore as a best-effort way to keep specified files out of AI requests.
That exclusion is not a complete access boundary. Cursor’s security and privacy hardening guidance says terminal and MCP tools do not honor .cursorignore. If an agent can read a sensitive file through a terminal command or tool, an ignore rule alone may not keep its contents out of context. Pair exclusions with filesystem permissions and tool restrictions.
Cursor’s Data Use & Privacy Overview, last updated September 3, 2026, describes Privacy Mode as preventing training use and says Cursor maintains zero-data-retention agreements with providers. The page also describes exceptions, including abuse-prevention processing and models with different retention terms. If you use your own API key, the provider’s privacy terms govern that data; Cursor also says requests still pass through its backend for final prompt building. Check the current policy and account settings because model, provider, and plan details can change.
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Privacy Mode can therefore address data-use concerns without being a content scrubber. It should not be treated as proof that a key or PII detected in accessible context is removed before a request is sent.
Claude Code: local execution does not mean local inference
Anthropic says Claude Code runs locally, while user prompts and model outputs travel over the network for model interaction; its data-usage documentation describes TLS in transit. Local execution is not the same as keeping prompt content on the machine.
Claude Code’s security documentation describes permission prompts for actions such as editing files and running commands, prompt-injection protections, and guidance to inspect proposed commands and changes. These controls govern agent actions and reduce risk, but do not establish automatic removal of secrets from context sent to the model.
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Anthropic’s LLM gateway configuration guide documents centralized gateway configurations and an API-key helper that can retrieve rotating or per-user credentials from a vault. This illustrates a safer separation: let the model request an operation while trusted software supplies the credential outside the model-visible prompt. Review the gateway and vault’s own security, access, and logging practices before deployment.
Where to put the sanitization boundary
The enforcement point must sit on the actual path to inference. A sanitizer that sees only editor text can miss retrieved code, terminal output, tool results, or MCP responses. A proxy or gateway can centralize inspection only if relevant traffic reliably passes through it; a client-side hook may understand specific editor events but can miss other routes. The right design depends on the client’s architecture and the organization’s ability to enforce the path.
- Inventory model-bound channels. Map editor prompts, indexed or retrieved code, terminal output, tool and MCP responses, and errors that can be included in context. Identify where each is assembled and sent.
- Inspect before transmission. Put the enforcement component before the model-bound request leaves the trusted boundary. Confirm it sees the assembled content, not just one input source.
- Apply layered detection. Use deterministic detection for known credential formats and additional contextual classification for PII. Detection quality varies; define supported categories and test both false positives and false negatives.
- Replace rather than reveal. Substitute detected values with opaque placeholders such as
<SECRET_1>or<PERSON_1>. Keep any mapping and original value outside model-visible context. - Fail according to policy. If the policy requires a clean request and inspection fails, block transmission rather than silently bypassing the sanitizer. Make any approved override explicit and auditable.
- Keep logs and state minimal. Avoid retaining raw prompts, detected values, placeholder mappings, or credentials in routine logs. Define access controls and retention for telemetry, crash dumps, and error reporting as well as the sanitizer itself.
- Reintroduce secrets only in trusted code when necessary. Prefer having a broker or connector retrieve a scoped credential to perform an approved downstream operation. Do not restore the real value into model-visible text simply to make a generated command work.
“In memory” alone is not a guarantee: the original value can still appear in process memory, logs, crash dumps, telemetry, tool output, or a request assembled through another route. Minimize persistence, but enforce coverage and data handling across the whole path.
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How to keep credentials out of prompts and repositories
- Store API credentials in a secret manager or trusted credential broker, not in prompts or committed files. Use short-lived or narrowly scoped credentials when the provider supports them.
- Keep
.envfiles and sensitive directories excluded from AI context where possible, then restrict filesystem access and agent tools so exclusions are not the only control. - Let an agent select an approved operation or connector; have trusted software retrieve and attach the credential outside the model-visible prompt.
- Use separate development credentials with limited permissions where feasible, and rotate or revoke a credential if it has already been exposed.
These practices reduce the amount of sensitive material available to the assistant. They complement, rather than replace, inspection of outbound content.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare sanitization approaches
The following are evaluation criteria, not benchmark results. The cited vendor documentation does not validate a particular sanitizer or establish complete detection.
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| Approach | Potential advantage | Question to verify |
|---|---|---|
| Client-side hook | May inspect events close to where prompts or tool results enter a client workflow. | Does it see every model-bound channel, including retrieval, terminal output, tool responses, and errors—or only selected events? |
| Local proxy or gateway | Can centralize request inspection and policy for traffic routed through it. | Can requests bypass it? Does it inspect the full request before transmission, and what happens if it is unavailable? |
| Organization gateway | Can provide a shared enforcement and administrative point across managed workflows. | Which clients and routes are covered? What are its logging, retention, access-control, and tenant-separation practices? |
For any option, assess detection categories and testing method, false positives and false negatives, latency, override handling, failure behavior, log retention, and whether credentials can be supplied by trusted code without reappearing in model-visible context.
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How to test the boundary safely
Test with synthetic credentials and canary PII—not live secrets or real personal records—across each workflow you intend to protect. At a controlled outbound boundary, verify what is transmitted and whether the sanitizer blocks or replaces the test values. Also inspect logs, telemetry, crash and error paths, tool results, and bypass routes. Record which channels were tested and treat untested paths as unverified rather than assuming coverage.
Repeat the checks after changing clients, models, gateway configuration, tools, or privacy settings. A test of one path does not establish that all other paths are covered.
What to do if a key was already exposed
- Revoke or rotate the credential at the issuing provider; do not rely on removing it from a prompt or repository as the sole response.
- Review the credential’s scope and usage history for unauthorized activity, following the provider’s incident procedures.
- Remove the value from files, logs, or other accessible locations where appropriate, and prevent it from being reintroduced into model context.
- Use synthetic canaries to test the affected workflow and close the route that exposed the credential before returning it to normal use.
Removal from a local file cannot recall a value already transmitted. Handle the credential as exposed and follow the issuer’s revocation process.
Quick Recap
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