Vibe Coding Security Checklist for AI Coding Agents
An AI coding agent can turn a clear request into working screens and integrations quickly. The trade-off is that it may also read files, run commands, install packages, call connected tools, and write code before you notice a dangerous assumption. A secure workflow keeps that speed while limiting what the agent can see, do, and ship.
For example, imagine a small logistics firm in India building an internal booking portal. If an agent has broad access to the repository and terminal, a prompt or command could expose customer data or a deployment key. This is a hypothetical example, not a client result. This checklist focuses on the build process; for underlying model and application risks, see our OWASP guide to LLM application risks.
Keep the agent inside a safe workspace
Start in a development repository or branch, not a production host. Give the agent only the folders and test data needed for the task. Keep production environment files, API keys, database exports, customer records, and backups outside the workspace it can read. Prefer synthetic or masked data for development.
Ignore rules help, but they are not a vault. Cursor documents that ignored files can still be read by terminal commands and MCP tools, even when those files are blocked from the agent's direct file access. Check the exact access boundaries in whichever coding tool you use; do not assume that .gitignore or .cursorignore protects a secret from every connected tool. Cursor's ignore-file guidance explains this distinction.
Do not paste production credentials into prompts. Use short-lived, least-privilege credentials only when a task genuinely needs them, and keep them in an approved secret store rather than source files. Treat prompts, agent transcripts, terminal output, and session logs as possible places a credential could be exposed. If a real key appears in any of them, revoke or rotate it and check its use; deleting a message does not invalidate the key.
Grant MCP servers and plugins only the access they need
An MCP server or editor plugin extends the agent's reach. Before installing one, identify who maintains it, where its source code lives, what it can read or change, which credentials it needs, and whether it can send data to an external service. Remove integrations that nobody uses. Enable only the individual tools needed for the current job, not an entire collection by default.
During use, inspect the tool name and arguments before approval. A server's description or risk label is not proof of what it will do: MCP tool annotations are hints, not enforcement, and should be treated as untrusted unless the server itself is trusted. Keep real customer data and production credentials away from an unreviewed integration. MCP's explanation of tool annotations clarifies why a reassuring label cannot replace permission boundaries.
Make command approval meaningful
Repeated approval prompts can become background noise. Avoid blanket auto-approve or “YOLO” modes that let an agent run every terminal command. Keep approval on for commands that delete files, install dependencies, change permissions, run database migrations, upload data, push code, or deploy. Cursor's security guidance describes command approvals and run modes as guardrails, while Anthropic warns that skipping all permission prompts removes an important protection. Cursor agent security and Anthropic's permission-mode analysis are useful product-specific references.
If you enable limited auto-run, allow only familiar, low-impact commands such as your reviewed test or formatting tasks. Use a sandbox where possible, restrict write access to the project, and limit network and host access. Review the command itself, not just the agent's explanation of it. A safe-looking test script can still invoke other code.
Verify every package and generated change
When an agent suggests a dependency, verify that the exact package name exists and is the one you intended. A plausible name can be a typo-squat: npm describes attacks that register lookalike packages to catch mistaken installs. Check the official project or publisher, repository, release history, and why the dependency is needed. Review new and changed entries in the manifest and lockfile, and inspect install scripts and requested permissions.
Run your normal dependency and vulnerability checks, but do not treat a clean scan as proof that a package is safe. Keep dependency additions small, prefer established packages already approved by your team, and use the correct private registry or package scope for internal libraries. npm's threat guidance covers typosquatting and dependency confusion; the practical safeguard is to verify before installing, not after a build succeeds.
Then read the generated diff file by file. Look for unexpected files, hard-coded credentials, new network calls, permissive defaults, and changes outside the requested feature. Run tests and security checks yourself rather than relying on the agent's claim that they passed.
Put a human between generated code and production
Require a human review before merging, and protect the default branch with pull requests and required checks. GitHub's cloud-agent guidance identifies unvalidated code, sensitive information, and prompt injection as risks; its protections require human review before an agent-authored change is merged. Smaller teams can apply the same principle with one reviewer who did not write the prompt. GitHub's risk and mitigation guidance describes that review boundary.
Make the review specific to the feature. For authentication and data access, trace each request from the user interface to the server and database. Confirm the server checks identity and permission for the requested record, including tenant or account boundaries; do not rely on a hidden button or client-side check. Test unauthenticated, low-privilege, and cross-account requests. Check token handling, error paths, and logging for accidental disclosure.
Require tests and security checks to pass before merge, and keep deployment approval separate from code generation. Branch protection can enforce reviews and status checks rather than relying on a reminder in chat. GitHub's protected-branch documentation explains those controls. The agent may draft the change; a person owns the decision to ship it.
The Bottom Line
You do not need to slow every coding task to a crawl. Give the agent a limited workspace, keep secrets out of its context, scrutinize tools and commands, verify dependencies, and make a human review a release gate. These controls let a small team capture useful speed while keeping the authority to merge and deploy in human hands.
Get an independent review of your coding-agent permissions, secrets, and release gates before a risky change reaches users.
Book an AI security review →
Member discussion: