What is vibe coding security?
By Identra · Updated
Vibe coding security is managing the risk when people build software by prompting AI and accepting its output with little review. It covers the generated application, the data pasted in during development and the permissions the coding tools and agents hold.
Why does vibe coding need security controls?
A working demo proves the feature runs. It doesn't prove users only see their own records, or that a database service key isn't sitting in the browser bundle. When 'it works' is the acceptance test, security bugs survive every round of prompting.
There are two separate problems. One is the app that gets built. The other is what the tool can do while building it. An agent like Claude Code can read .env, run shell commands and push to a branch if it's been allowed to. Coding agent security covers that second problem. GitHub's responsible use guidance for Copilot agents also tells people to review generated code and commands before trusting them.
Generated app
- Question to ask
- Can one user pull another user's records?
- Evidence that answers it
- Server-side authorization tests
Development data
- Question to ask
- Did keys or customer data go into a prompt?
- Evidence that answers it
- Prompt rules and secret scans
Agent access
- Question to ask
- Can the agent touch production?
- Evidence that answers it
- Access limits and a record of what it ran
What could go wrong in an enterprise prototype?
Say someone in finance ops uses Cursor to build an invoice dashboard. They wire it to the shared Postgres database with a service account. The page filters invoices by department in the browser. The demo looks right.
The API still returns every department's invoices. Open DevTools, read the network response, and they're all there. Adding a login screen doesn't fix it. The server has to decide which rows each user gets.
Before a pilot, swap the service account for scoped access and enforce the department filter on the server. Then call the endpoint directly as the wrong user and confirm it refuses. Build with synthetic invoices. Name an owner who can explain the data flow, because a useful prototype tends to stick around.
How do you keep secrets and company data out of prompts?
Debugging is where it leaks. People paste .env files, stack traces with tokens and CSV exports full of customer emails into ChatGPT to get a fix. Generated code also likes to hardcode keys. After that, the value lives in chat history, commits and CI logs.
Secrets management keeps credentials out of code. You also need a rule about which data can go into which approved tool. Approving GitHub Copilot doesn't approve pasting the production customer table into it.
- Use fake records and placeholder keys in prompts.
- Load secrets from a secret store at runtime. Server keys never ship to the browser.
- Run a secret scanner in pre-commit and again in CI.
- If a key leaks, rotate it. Deleting the line from the file revokes nothing.
How should you check AI-suggested dependencies?
Assistants sometimes suggest package names that don't exist. Attackers register those names and wait. That's slopsquatting. A clean npm install proves the name exists and nothing more.
Check the name against the project's own docs. Look at the publisher, the source repo and any install scripts. Lockfiles pin whatever you picked, good or bad.
What changes when a coding agent can run commands?
Depending on what you've allowed, it can now read ~/.aws/credentials, install packages, delete files or run a deploy script. Scope that before the task starts. That's least privilege for agents.
A README or a tool response can carry prompt injection aimed at the agent. Telling it to be careful doesn't help much. Restrictions enforced outside the model do.
- Open unfamiliar repos in an isolated environment with no production credentials.
- Require approval for destructive commands, permission changes and deploys, with the exact command shown.
- Keep a record of what the agent ran and what happened.
What should pass review before release?
Someone who can explain the trust boundaries has to own it. Asking the same assistant to review its own code finds some bugs. It isn't independent review. Use human-in-the-loop AI review where the stakes call for judgment, and keep development and production access apart.
- Request another user's records directly and confirm the server refuses.
- Review queries, file uploads and anything that shells out.
- Turn off debug mode and check storage bucket permissions in the deployed app.
- Write down rollback steps and who rotates keys if something leaks.
How Identra thinks about it
Identra discovers coding agents, AI clients, MCP servers and packages on macOS and Windows endpoints. Prompts to Codex and Claude Code are checked on the device before they are sent and can be blocked by policy, and destructive agent shell commands are denied when policy is set to block. Every agent run is recorded with the user, device, AI client and outcome.
Go deeper: Identra on the endpoint
Frequently asked questions
Is all AI-assisted programming vibe coding?
No. Vibe coding usually means prompting your way to working code with little inspection. Plenty of AI-assisted work gets real code review, testing and an accountable owner.
Can a better prompt make generated code secure?
It can push generation in the right direction. It can't guarantee the result. Check the actual code, dependencies, deployment settings and runtime behavior.
Do internal prototypes need security review?
Yes, scaled to what they touch. A prototype wired to customer records or production services can do harm long before anyone outside sees it.
Should coding agents have production credentials?
Normally no. When a production change is truly needed, use short-lived, narrowly scoped access and an approval tied to that specific action.
Who is responsible for vulnerabilities in AI-generated code?
Whoever deploys it should name an owner. Generated code gets the same patching, maintenance and incident handling as any other code.