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World desk5 min

How to Avoid 5 Security Mistakes When Building With AI Coding Tools

AI coding tools can read project context, change files, run commands, and send code to model providers. These five practical checks help reduce security risk before you build and ship.

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AI coding tools can help write an application, but they can also read project context, make file changes, run commands, and send prompts or code to model providers. The safest approach is to limit what an agent can access, verify what the generated application allows each user to do, and review code and dependencies before release. These five mistakes are practical risk areas, not a measured ranking of the most common failures across Cursor, v0, and Lovable.

1. Can AI coding tools expose API keys or other secrets?

Yes, if a secret is stored where an agent can read it or is pasted into a prompt. A project file may become part of the context sent to a model, and a credential accidentally included in a prompt should be treated as exposed. Secrets embedded in browser-delivered code are also available to users who inspect the application.

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Keep secrets out of agent-readable files

OWASP recommends storing secrets in environment variables, vault services, or encrypted secret stores rather than in files inside the project tree. If a credential has been exposed, revoke or rotate it; deleting the text from the prompt or file does not invalidate the old credential. See the OWASP Secure Coding with AI Cheat Sheet.

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Cursor’s .cursorignore can block agent reads and context, but it is not a complete access boundary: Cursor says terminal and MCP tools do not honor those rules. Pair ignore rules with filesystem permissions and tool approvals, as described in Cursor’s security hardening guidance.

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Check what ships to the browser

Keep privileged credentials on the server and inspect built client assets before deployment. A key intended to authorize server-side access must not be treated as secret if it is included in code delivered to a user’s browser.

2. Does a successful login mean the app’s data is protected?

No. Authentication establishes who a user is; authorization determines what that user may do. A generated sign-in screen, or a successful login, does not prove that every server-side action checks permissions or that one user cannot access another user’s records.

Test permissions at the server and data layer

Review server-side operations and database policies, then test the same actions under different roles and accounts. Include these cases before release:

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  • Unauthenticated access: can a visitor retrieve or change protected data?
  • Ordinary-user access: can a signed-in user perform only the actions their role allows?
  • Administrator access: are elevated actions restricted to authorized administrators?
  • Cross-account access: can one user read, change, or delete another user’s records by changing an identifier or request?

OWASP advises reviewing and testing AI-generated code; its guidance does not establish that any one of these named tools routinely produces broken access control. Treat authorization as an application requirement to verify, not a defect to assume or dismiss.

3. Should you let an AI agent run terminal commands without review?

Not by default. Agentic coding tools may run shell commands, install packages, edit files, access networks, and push branches. Broad permissions can turn a mistaken instruction or unsafe change into an action with consequences outside the code suggestion itself. OWASP outlines these capabilities in its AI coding security guidance.

Keep execution permission narrow

Cursor says agents can make workspace changes immediately, while terminal commands require approval by default. Its documentation also warns that run-mode guardrails are best-effort, not a hard security boundary; auto-reload can execute changes before you review them. See Cursor’s agent security documentation.

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For any tool, keep approvals and sandboxing enabled where available, restrict network and integration access to what the task needs, and inspect commands and diffs. Avoid opening an untrusted repository with broad permissions. Cursor’s organizational guidance recommends sandboxing and Auto-review rather than “Run Everything” (security hardening).

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Use version control as a recovery layer

Work on a branch or otherwise preserve a clean, recoverable version before an agent makes substantial changes. Review the proposed diff before merging, and do not treat an agent’s permission prompt or safety mode as proof that a command is harmless.

4. What should you check before merging AI-generated code?

Review generated code and dependencies using the same release controls you use for human-written code. AI suggestions can include unsuitable packages or implementation errors, and a plausible explanation is not a substitute for a test or audit.

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Make checks part of the release workflow

  1. Inspect the diff. Confirm what changed and why, including configuration, access checks, and newly introduced packages.
  2. Run the project’s tests. Include tests for permissions and data isolation, not only whether the interface appears to work.
  3. Audit and pin dependencies. Check suggested packages before merging, pin versions, and manage updates through the project’s normal dependency process.
  4. Scan for known vulnerabilities and secrets. Make these checks part of CI/CD, with failures blocking a release where appropriate.
  5. Require human review before production. Keep responsibility for the code and its deployment with the team shipping it.

OWASP puts the key CI rule plainly: “Configure CI/CD pipelines to fail on known vulnerabilities in dependencies, regardless of whether the code was human-written or AI-generated.” A second model may help surface issues, but it should not replace deterministic checks or accountable review. Read the OWASP cheat sheet.

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5. Does privacy mode mean no code leaves your computer?

No. “Not used for training” and “never transmitted” describe different things. Cursor documents that AI features send prompts and code context to model providers. Its Privacy Mode means code is not used for training, but personal API keys are subject to the provider’s terms, and some models require data retention. Cursor also says Cloud Agents need repository access over time and keep encrypted repository copies temporarily while an agent runs. Check the applicable terms in its privacy and data governance documentation and privacy and data help page.

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Check the specific plan, model, and workflow

Lovable says prompts and related Customer Content are transmitted to model providers. Its policy says Business and Enterprise content is not used to train Lovable models; Free and Pro users can turn off the training setting in Account Settings → Privacy. These are platform data-handling statements, not a security assessment of the app you build. See Lovable’s security page and its Privacy Policy, effective September 15, 2026.

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For v0, do not assume that another platform’s controls or terms apply. Check v0’s current privacy terms, available settings, model-provider arrangements, and integration permissions before sharing sensitive code or data. For any platform, minimize sensitive context and understand retention and deployment settings before starting a task.

Keep vendor controls separate from application security

A vendor certification or attestation concerns the vendor’s audited controls and stated scope; it does not certify the security of an application created with the tool. Cursor’s security page, last updated August 25, 2026, reports AIUC-1, ISO/IEC 27001:2022, and ISO/IEC 42001:2023 certifications and a SOC 2 Type II attestation. Check the current scope and reports through Cursor’s security page rather than treating those claims as a substitute for testing your app.

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