Scoped credentials for your Cursor agent
Cursor is an AI-powered code editor whose agent can inspect a repository, change files, run shell commands, and use configured tools while it works through a task. That combination is useful for implementation and debugging, but it also joins code-context risk with service-access risk: a prompt, a dependency, or an instruction in a repository can influence what the agent attempts next. If the editor can read a broadly privileged API key from an environment file, shell session, or tool configuration, a request to inspect a Stripe issue, open a GitHub pull request, or deploy a Vercel project can inherit far more authority than the task needs. Treat the Cursor agent as a distinct workload, not as your developer account. Give it a named, task-appropriate route to only the services and permissions it needs, and keep the durable source credentials outside the repository and prompt context.
Why does your Cursor agent have root access?
A Cursor agent given a broadly privileged Stripe, GitHub, Cloudflare, or deployment token can perform every operation that token permits, regardless of whether the current task is a code review, a test fix, or a production change. The practical boundary should be narrower: repository access for the repository under repair, deployment access for the intended project, and read-only access when the agent only needs diagnostic context. Avoid placing reusable secrets in dotfiles, pasted prompts, generated diffs, or command output, where they may be copied into logs or future context. Gazebo gives the agent a separate access profile and checks credential retrieval against that profile. Its access records answer a focused operational question — which agent identity requested which service credential and when — while profile-level revocation lets you stop that identity without immediately changing a shared underlying key. Scope does not replace code review, branch protection, or provider-side permission design; it makes those controls usable with an agent that can act quickly across tools.
How Gazebo works with Cursor
- 1
Connect the services Cursor legitimately needs, then review the provider-side credential before exposing it to any agent. Prefer a repository-scoped GitHub token, a single Vercel project, a non-production Stripe key, or similarly constrained service credentials over an owner or organisation-wide token.
- 2
Create a named Cursor access profile for one project, environment, or bounded task. Select only the connected services required by that work; do not use a convenient catch-all profile for unrelated repositories or production systems.
- 3
Add Gazebo's MCP endpoint and its bearer token to Cursor's MCP configuration. Keep that configuration out of source control and do not paste service keys into agent instructions as a fallback.
- 4
When the agent needs a service credential, it requests it through get_credential at runtime. Gazebo evaluates the profile, logs the retrieval, and supplies the configured scoped credential rather than requiring the agent to search environment files.
- 5
Review the access history alongside the diff, command history, provider activity, and deployment result. If the task changes, the agent behaves unexpectedly, or the work is complete, revoke the profile; create a new narrowly scoped profile for the next task instead of reusing old access.
What this looks like in practice
Billing work without full Stripe access
For a subscription bug, start with a credential that can inspect the specific Stripe data needed for diagnosis, ideally in a test environment. If the implementation needs to create test fixtures, use a separate profile or provider credential with only that capability. Do not let a request to explain a billing issue silently become authority to alter live customers, prices, refunds, or webhooks.
Deployment access without production credentials
Give a deployment-focused Cursor profile access to the one Vercel project involved in the change. Decide separately whether it needs to deploy, inspect logs, or manage environment settings; those are different risks. Have the agent prepare a diff and deployment plan first, then review the target branch and environment before allowing a production action.
Audit what your agent actually did
Use the credential-access record as one part of incident reconstruction. Correlate its timestamp and agent profile with the Cursor task, commit, CI run, provider audit events, and resulting deployment. If a credential request is unexpected, revoke the profile first to contain further retrieval, then inspect the task instructions and repository changes before restoring a smaller access path.
Keep tool instructions separate from authority
Treat text the agent reads — issue comments, markdown files, generated logs, and web responses — as input rather than approval to use a powerful tool. State the allowed service, repository, and environment in the task, and require a review point before destructive commands or production deployments. A narrow credential profile ensures that an instruction to fetch, export, or reconfigure an unrelated service cannot succeed simply because a developer's key was available.
Set up Gazebo for Cursor
Connect your services once. Your Cursor agent gets exactly the access it needs — nothing more.
Get started freeServices Cursor commonly connects to
Further reading
Zero Trust for AI Agents: What It Means and How to Apply It
Zero trust means every credential request is verified, scoped, and logged — regardless of where the agent runs. Here's what the four core primitives look like in practice.
1Password for AI Agents and Gazebo: Two Credential Layers, How They Fit Together
1Password for Claude handles web login credentials for browsing agents. Gazebo handles programmatic API credentials for coding agents. They solve different layers of the same problem and can run together.
AI Agent Security Checklist: Cursor, Replit, and Copilot
Six things to do before shipping AI agents to production — scoped credentials, approval gates, audit logs, and a revocation path that doesn't take down everything else.
AI Agent Secrets Management: 6 Operational Best Practices
A practical operating checklist for securing AI agent credentials: scoped identities, access logs, revocation, and keeping keys out of prompts.
IAM for AI Agents: Identity Architecture for Autonomous Systems
The identity architecture for autonomous agents: scoped profiles, brokered credentials, approval gates, and lifecycle controls beyond human IAM.
What Is a Secrets Broker for AI Agents?
A secrets manager stores your credentials. A secrets broker controls which AI agent can retrieve them, under what conditions, and what it can do with them. Here's why the distinction matters.