Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteTo reduce the chance of sending API keys or personal information to an AI coding tool, put a sanitization check on the path to the model, keep credentials in a secret manager or trusted broker, and restrict what the tools can read. Privacy settings, permissions, and ignore files help, but none of them alone guarantees that sensitive values are removed before a request is sent.
How sensitive data can reach an LLM
A coding assistant may receive more than the text you type into its prompt. Depending on the client and workflow, model-bound content can include selected code, retrieved or indexed repository chunks, terminal output, tool results, and responses from connected MCP servers. A secret can therefore enter a request even when you did not paste it into the chat.
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Cursor: privacy settings and file exclusions have different jobs
Cursor says that its AI features send prompts and code context to model providers. Its documented codebase-indexing flow hashes and synchronizes files, uses server-side services to create embeddings, and can return relevant chunks to the client for an inference request. Cursor describes .cursorignore as a best-effort way to prevent specified files from entering AI requests, not as a universal access boundary. Its current enterprise hardening guidance says terminal and MCP tools do not honor .cursorignore, so exclusions need to be paired with appropriate permissions and filesystem controls. See Cursor Security and Security and Privacy Hardening.
Cursor’s Data Use & Privacy Overview, last updated September 3, 2026, describes Privacy Mode as a data-use protection: Cursor says it has zero-data-retention agreements with providers, while noting exceptions for abuse-prevention processing and models outside those arrangements. 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, including when you use a custom key. These are statements about handling and processing, not a claim that secret values are scrubbed from requests.
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Claude Code: local execution does not mean local inference
Anthropic says Claude Code runs locally, but sends prompts and model outputs over the network for inference, with TLS in transit. Its Claude Code Data Usage page describes that data path. Anthropic’s Claude Code Security documentation covers permission prompts for actions such as editing files and running commands, prompt-injection protections, and advice to inspect proposed commands and changes. Those controls constrain or inform agent actions; they do not establish automatic redaction of secrets from model context.
What a pre-transmission sanitizer should do
Think of zero-trust in-memory sanitization as a design objective: inspect the content at the point it becomes model-bound, regardless of whether it came from a prompt, retrieved code, a terminal, a tool, or an MCP response. The exact interception point depends on the client architecture. If the sanitizer sees only typed prompts, it cannot protect context that enters through another route.
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- Inventory the paths. Map which components can contribute content to requests: editor input, indexing and retrieval, terminal output, tool responses, MCP responses, and error messages. Mark paths the client does not let you inspect or mediate.
- Detect and minimize. Use deterministic matching for known credential formats and additional contextual detection for PII. Detection is imperfect: test supported formats and data categories, and measure false positives and false negatives in your own environment rather than assuming complete coverage.
- Replace values before transmission. Substitute matches with opaque placeholders, such as
API_KEY_1orPERSON_1. Keep the mapping and original values out of model-visible context. Restore a value only in a trusted component if a downstream operation genuinely needs it. - Set failure behavior. Decide what happens when inspection is unavailable, a request cannot be parsed, or the detector encounters an unknown format. For sensitive workflows, fail closed by blocking the request rather than silently sending unchecked content; define an auditable, restricted override process if work must continue.
- Minimize sanitizer data. Avoid retaining raw prompts or detected values in logs. Set retention, access controls, and tenant separation for policy events, mappings, telemetry, and crash information.
“In memory” describes where sanitizer state may be handled; it is not a guarantee that an original value cannot also appear in process memory, logs, crash dumps, telemetry, tool output, or a request assembled along another path. Treat those as separate exposure points in the design.
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Choose an enforcement point by coverage, not convenience
A client-side hook, local proxy, or organization gateway can be useful only to the extent that relevant content reliably passes through it. The following is an architecture comparison, not a benchmark: actual coverage depends on the client, configuration, and deployment.
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| Approach | Potential strength | Key limitation to verify |
|---|---|---|
| Client-side sanitization | May have context about selected editor or agent events before they are sent. | Check whether it observes retrieval, terminal output, tool and MCP responses, and errors—not just typed prompts—and whether another path can bypass it. |
| Local proxy | Can centralize inspection and policy for traffic that is routed through it. | Confirm that every relevant request uses the proxy, that failures block when required, and that logs do not retain sensitive content. |
| Organization gateway | Can provide a shared enforcement and administration point for managed traffic. | Verify coverage for each client and provider route, bypass resistance, tenant isolation, and credential handling. |
Evaluate any implementation against the same concrete checks: content-path coverage; bypass and fail-closed behavior; secret formats and PII categories detected; false-positive and false-negative rates; placeholder and restoration design; credential scope and expiry; telemetry and log retention; access controls; and workflow impact, including latency and overrides. Do not treat the presence of a proxy or detector as proof that all content is sanitized.
Keep credentials out of prompts and repositories
- Store API credentials in a secret manager or trusted broker, not in prompts, source files, or copied terminal output. Prefer short-lived or scoped credentials when the provider supports them.
- Exclude
.envfiles and sensitive directories from indexing where possible, then use filesystem permissions and tool restrictions to limit access. An ignore rule is a filter, not a complete access-control boundary. - Give agents only the tools and permissions required for the task. Review proposed commands and changes, and use isolation when running untrusted scripts.
- Do not ask an agent to use a secret by pasting the secret into chat. Arrange for trusted software to perform the operation without revealing the credential to the model.
Anthropic’s LLM gateway configuration documents centralized authentication and a helper example that retrieves rotating or per-user credentials from a vault. This illustrates the safer separation: the model can select an operation or connector while trusted software supplies the credential outside the model-visible prompt. Anthropic says it does not endorse or audit LiteLLM, so assess the security of any gateway you deploy, including its credential storage, access controls, and logging.
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Validate every route with synthetic data
Before relying on a sanitizer, test workflows with synthetic credentials and canary PII—not live secrets. At a controlled boundary, inspect whether test values appear in outbound requests, then check logs and error paths. Include ordinary prompts, retrieved files, terminal output, tool responses, and MCP results wherever those channels are in scope. Also test detector misses, malformed requests, sanitizer outages, and approved overrides. This establishes what your specific deployment catches; it does not establish universal detection.
For Cursor, confirm the current privacy setting, model/provider terms, and exclusion behavior for your account and configuration; policies and product flows can change. Cursor reports that more than 50% of its users have Privacy Mode enabled, but that is an adoption figure, not evidence of a leakage rate or independent sanitizer effectiveness (Cursor Security).
Quick Recap
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