Secret Management for Cursor

Cursor is a code editor built around AI assistance. Its agent can execute terminal commands, call APIs through tool use, and generate code that interacts with external services. Like any AI-powered development environment, Cursor needs access to credentials -- and like any AI-powered environment, those credentials can leak through multiple channels that do not exist in traditional editors.

How Cursor Uses Credentials

Cursor's AI agent interacts with secrets in several ways:

• Terminal execution: Cursor's agent runs shell commands in the integrated terminal. Any environment variable set in your shell profile is available to these commands and visible in terminal output.
• Code generation: When the agent generates code that calls APIs, it may pull API keys from environment variables, .env files, or even hardcode values it has seen in the codebase.
• Tool use and extensions: Cursor extensions and MCP integrations call external APIs with credentials passed through configuration.
• Codebase indexing: Cursor indexes your project files. If .env files are not excluded, credentials in those files become part of the index and can surface in AI responses.

The Problem

Traditional secret management assumes a human operator who understands what is sensitive. An AI agent does not make that distinction -- as explained in why AI agents leak secrets, a DATABASE_URL with an embedded password is just another string to the model.

Terminal history and output: When Cursor's agent runs echo $DATABASE_URL to debug a connection issue, the raw connection string appears in terminal output, which feeds back into the AI context.

Extension logs: VS Code-based editors (including Cursor) write extension output to log files. Extensions that handle authentication may log credential exchanges during debugging.

AI-generated code: If the agent sees STRIPE_SECRET_KEY=sk_live_abc123 in the environment, it may include that literal value in generated test files or configuration code. Even if you catch it in review, the value has already been committed to the AI provider's request logs.

MCP tool responses: Cursor supports MCP servers for tool use. A database MCP server that returns an error like authentication failed for user admin with password s3cret has leaked credentials into the conversation context.

How KeyZero Solves This

Shell Hooks for Automatic Secret Loading

KeyZero's shell hooks detect .keyzero.toml when you cd into a project directory and resolve secrets automatically. In Cursor's integrated terminal, this means credentials are available as environment variables without being stored in .env files or shell profiles.

Add the shell hook to your shell configuration:

# .bashrc or .zshrc
eval "$(kz hook bash)"   # or zsh/fish

Now when Cursor opens a terminal in your project directory:

# .keyzero.toml in project root
[secrets]
STRIPE_SECRET_KEY = { provider = "1password", ref = "op://Dev/Stripe/secret-key" }
DATABASE_URL      = { provider = "vault", ref = "secret/data/dev/postgres/url" }
REDIS_URL         = { provider = "keychain", ref = "myapp-redis-url" }
AWS_ACCESS_KEY_ID = { provider = "aws-sts", ref = "arn:aws:iam::123456789:role/dev" }

Secrets are resolved from their respective backends and injected into the shell environment. No .env file exists on disk. If Cursor's agent reads the project files, it finds .keyzero.toml with vault references, not raw credentials.

Blind Mode for Agent Tasks

When Cursor's agent needs to make API calls or run processes that use credentials, blind mode ensures the agent never sees real values:

kz run --blind -- npm run dev

The development server gets opaque tokens (KZ_TOK_xxxx) instead of real credentials. KeyZero's local proxy intercepts outgoing requests and swaps tokens for real values at the network edge. If the agent inspects the environment or logs, it sees only the opaque tokens. Learn how this works in blind mode explained.

Wrapping MCP Servers

If you use MCP servers with Cursor, wrap them with kz run to prevent credential leakage through tool responses:

{
  "mcpServers": {
    "database": {
      "command": "kz",
      "args": ["run", "--blind", "--", "node", "./mcp-servers/db-server.js"]
    }
  }
}

The MCP server process receives credentials through the KeyZero proxy. Error messages that might contain connection strings will show opaque tokens instead of real passwords.

Example: Cursor Workspace Configuration

A full setup for a Cursor workspace with multiple services:

# .keyzero.toml
[secrets]
OPENAI_API_KEY       = { provider = "keychain", ref = "openai-key" }
GITHUB_TOKEN         = { provider = "1password", ref = "op://Dev/GitHub-PAT/token" }
DATABASE_URL         = { provider = "vault", ref = "secret/data/dev/db/url" }
STRIPE_SECRET_KEY    = { provider = "1password", ref = "op://Dev/Stripe/secret-key" }
AWS_ACCESS_KEY_ID    = { provider = "aws-sts", ref = "arn:aws:iam::123456789:role/dev" }
AWS_SECRET_ACCESS_KEY = { provider = "aws-sts", ref = "arn:aws:iam::123456789:role/dev" }

Start your dev server through KeyZero:

kz run --blind -- npm run dev

Or rely on the shell hook -- every new terminal Cursor opens in the project directory will have secrets resolved automatically. The AI agent can execute commands, generate code, and call APIs. Credentials flow through the proxy, never through the context window.

Cursor-Specific Considerations

Cursor maintains a persistent workspace context that spans multiple files and terminal sessions. Secrets that leak into one terminal session can propagate to AI suggestions across the entire workspace. KeyZero's approach -- resolving credentials outside the editor process and using opaque tokens in blind mode -- means that even if Cursor's AI indexes terminal output or reads environment state, it captures only references that have no value outside the KeyZero proxy. This is particularly important for team environments where multiple developers share Cursor workspace configurations through version control. The same approach applies to other AI coding tools like Claude Code, and you can explore more deployment patterns in five patterns for secret-safe AI deployments.