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AI coding tools can expose secrets, run commands, and generate code that still needs security review. Reduce those risks by limiting what an agent can access, checking authorization and dependencies yourself, and understanding what data the service sends to model providers. The five mistakes below are practical risk areas—not a ranking of the most common failures. Available guidance does not establish comparable vulnerability rates for Cursor, v0, and Lovable.
1. Letting secrets enter prompts, project context, or browser code
If an agent can read a project file, that file may become part of the code context sent in a request. A credential pasted into a chat can be exposed in the same way. Keep API keys, database passwords, signing keys, and other secrets out of prompts and tracked project files. OWASP recommends environment variables, vault services, or encrypted secret stores rather than files in the project tree that an AI tool can read. See the OWASP Secure Coding with AI Cheat Sheet.
What to do
- Move credentials into an appropriate secret manager or environment-based configuration; do not put them in source code or sample files that contain real values.
- Limit the agent’s file access and use filesystem permissions and command approvals. Cursor’s
.cursorignorecan block agent reads and context, but Cursor says terminal and MCP tools do not honor it, so the ignore file is not a complete security boundary. See Cursor’s security hardening guidance. - If a real credential was included in a prompt, committed, or otherwise exposed, revoke or rotate it and check its use. Removing the text later does not invalidate the credential.
- Before deployment, inspect the built client assets and configuration to ensure privileged credentials are not shipped to the browser. Browser-delivered code is visible to users; keep privileged operations on a server you control.
2. Treating a generated login as proof that authorization works
Authentication establishes who a user is; authorization determines what that user may do. A polished sign-in screen or successful login does not show that every server-side operation checks permission or that one user cannot access another user’s records. This is a review requirement for any generated application, not a claim that a particular named tool routinely creates access-control flaws. OWASP advises reviewing and applying security checks to AI-generated code.
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Test permissions as separate cases
- Try the operation without signing in. It should reject requests that require an account.
- Sign in as an ordinary user and try both permitted and restricted actions. Do not rely on hiding a button in the interface as an authorization check.
- Use two separate accounts and test whether either can read, change, or delete the other’s records by changing an identifier or request.
- Test administrator-only actions with both an administrator and an ordinary account.
- Review the server-side handlers and database policies that enforce these rules, then include the cases in pre-release testing.
3. Giving an agent broad permissions and trusting its guardrails
Agentic coding tools may be able to run shell commands, install packages, edit files, access networks, or push branches. Those capabilities can make work faster, but also expand the damage an untrusted instruction, mistaken command, or compromised dependency could cause. OWASP describes these capabilities in its AI coding security guidance.
#1 Best Overall
Cursor documents that its agents can make workspace changes immediately; terminal commands require approval by default, and auto-reload can execute changes before review. Cursor also says run-mode guardrails are best-effort, not a hard security boundary. Its Agent Security documentation and hardening guidance describe approval and sandboxing controls.
Use least privilege for each task
- Keep command approval and sandboxing enabled where available; avoid modes that automatically run every command.
- Limit network access, connected integrations, and repository permissions to what the task needs.
- Review proposed commands and the resulting diff before accepting or running changes, especially when they install packages, alter configuration, or touch deployment credentials.
- Use version control so you can inspect and revert changes. Do not open an untrusted repository with broad access to local files or services.
4. Merging generated code or dependencies without independent checks
Generated code and package suggestions need the same review as code written by a person. A successful build does not establish that a dependency is safe, current, or appropriate for production. OWASP recommends auditing suggested dependencies, pinning versions, and using CI/CD checks that fail on known vulnerabilities regardless of whether code was human-written or AI-generated. Read the OWASP guidance.
Make security checks part of the merge path
- Inspect the diff, including package manifests and lockfiles, before merging.
- Run the project’s normal tests and dependency audit. Pin dependency versions and update them through the project’s established dependency-management process.
- Scan for known vulnerabilities and accidentally committed secrets, and configure CI to block releases when required checks fail.
- Require a human code review before production deployment. A second model can help spot issues, but it does not replace deterministic checks or an accountable reviewer.
5. Confusing privacy settings or compliance claims with “no data leaves” or “the app is secure”
A setting that prevents training use is not the same as a promise that prompts or code context are never transmitted. Cursor’s documentation says AI features send prompts and code context to model providers. Its Privacy Mode means code is not used for training, but the terms described by Cursor include exceptions: personal API keys follow the provider’s terms, and some models require data retention. Cursor also says Cloud Agents need repository access over time and store encrypted repository copies temporarily while an agent runs. Check the current Privacy and Data Governance and Privacy and data documentation before using sensitive material.
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Lovable’s Privacy Policy, effective September 15, 2026, says prompts and related Customer Content are transmitted to model providers. Its security page 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 vendor statements about platform data handling, not guarantees about the security of an application built with the service.
Rank #3
For v0, do not assume another platform’s controls or terms apply. Check its current privacy, retention, model-provider, and integration settings before entering sensitive project material; the sources cited here do not establish v0-specific data-handling controls.
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Check the data flow and the application separately
- Before sharing sensitive code, identify what context is transmitted, which model provider receives it, and what retention and training terms apply to your account, plan, and selected model.
- Use the strictest suitable privacy and retention settings, minimize sensitive context, and review connected integrations and deployment permissions.
- Assess vendor certifications against their stated scope and current reports. Cursor’s security page, last updated August 25, 2026, reports AIUC-1, ISO/IEC 27001:2022, ISO/IEC 42001:2023 certifications and SOC 2 Type II attestation. These concern vendor controls and scope, not the security of your generated application; consult the current Cursor security page and trust-center materials.
- Separately test the application’s authorization, data handling, dependencies, and production configuration before release.
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