Lovable Security Scan: Review Findings Before Publishing

Treat a Lovable security scan as a list of findings to investigate, then verify each fix before publishing. Keep the original warning, the behavior it describes, and the evidence that a change resolved it; an assistant saying “fixed” is not enough. Tools like fenbs keep those findings and unfinished checks available when another person or AI continues the review.

This workflow starts with findings you already have. The Lovable debugging guide↗ (opens in a new tab) documents investigation and targeted fixes, but does not establish a scanner launch path, coverage list, or severity scale. Use the actual finding in front of you rather than assuming a particular button or set of checks exists.

What should you do with the first finding?

1. Copy the exact finding into your private review record, removing credentials and personal data.
2. Identify the feature, file, or data flow it names. If it gives no location, mark that as an open question.
3. Write the expected safe behavior in one sentence: who should be allowed to do what?
4. Ask for an explanation and a narrow proposed fix before changing the app.
5. Decide how you will test both the allowed action and the action that should be refused.

For example, imagine a warning about one customer seeing another customer’s booking. Your expected behavior might be: a signed-in customer can read their own booking and cannot read someone else’s. This is a hypothetical check, not a claim about what Lovable’s scanner detects. The useful part is turning the warning into a behavior you can investigate.

Do not combine every warning into “make my app secure.” A small, concrete question gives the reviewer a clear result to check. The broader guide to vibe coding security covers risks around generated apps; here, stay focused on the finding you need to resolve.

Where can you investigate without starting a build?

Use the mode picker next to the chat input and select Plan. Lovable’s Plan mode guide↗ (opens in a new tab) documents a structured, editable plan before implementation. Review it carefully: selecting Approve switches to Build and starts implementation, while Skip declines the plan without starting that build.

Investigate this security finding before changing the app:
[Paste the finding without secrets or personal data.]
Explain the affected feature and the relevant code.
Separate what you confirmed from what is still uncertain.
Propose the smallest fix and explain its effect on normal users.
List a test for the allowed action and a test for the action that must be refused.
Do not broaden the change to unrelated features.

If you only need an explanation in the conversation, Lovable’s Chat mode guide↗ (opens in a new tab) describes discussing the current app without changing its code or data. Connected-tool writes are separate: review any tool action rather than assuming an app discussion makes all outside actions read-only. Keep credentials and real customer records out of the investigation prompt.

How should you sort findings into useful next steps?

Keep the scanner’s wording and add your own review status beside it. Do not invent a mapping between a label and a publishing decision. You need enough evidence to understand the affected behavior and a person who can take responsibility for the next step.

These are suggested review labels, not Lovable scanner statuses. For a finding you believe is mistaken, record the reason and evidence instead of silently dropping it. If you cannot explain the finding or the proposed fix, ask someone qualified to review that part before relying on it. Uncertainty should stay visible.

What proves a fix worked?

Use a before-and-after check against the behavior you wrote down. For the hypothetical booking example, test with accounts and sample records you are authorized to use. Confirm that the customer can still read their own booking, then test that they cannot retrieve the other account’s booking. Keep the result tied to the app version you reviewed.

• Original problem: repeat the conditions that produced the finding or the unsafe behavior.
• Normal use: confirm the intended action still works for the right person.
• Refused action: check the relevant boundary, not just whether a button disappeared.
• Remaining uncertainty: say what you could not reproduce or did not test.

If you rerun the available security check, compare its output with the original finding as well. A different warning count alone does not explain what changed. Save the exact check, result, and limitation in fenbs’s work record, so the next assistant can distinguish a verified fix from an unfinished suggestion.

What if repeated fixes make the app worse?

Stop changing several things at once. Lovable’s debugging guide recommends root-cause investigation in Plan mode when repeated attempts fail, and restoring a working version if fixes have tangled the code. Explain which finding you were addressing, what was changed, and what stopped working. Ask how those changes relate before approving another patch.

A rollback is a recovery step, not evidence that the security issue has gone away. Return the finding to an open state until the relevant behavior is checked again. Use the Lovable prompt library for bounded change requests, and Lovable Instructions for durable guidance after a person approves the lesson.

How to keep the review ready for next time with fenbs

Keep the work so you can carry on from exactly where you left off, on any device and in any other AI. Save the findings, the checks that passed, and the next thing a person needs to review. A short, accurate record is more useful than copying the entire investigation chat.

1. Create a free account.
2. Connect Lovable to fenbs.
3. Ask it to save each finding with the proposed fix, the checks you ran, and anything still uncertain.
4. Ask it to keep a reusable before-publishing checklist with the checks your app needs.
5. Before stopping, ask it to save what remains and where the next reviewer should begin.

Lovable supports remote custom chat connectors↗ (opens in a new tab) through MCP, a standard way for AI apps to use outside tools; workspace controls can disable them. fenbs keeps the review record. It does not scan your app or certify it as secure, so it is not a replacement for the technical review your app needs.

For the choice of development tools, read Lovable vs Claude Code. For more ways to keep useful work after an AI session, browse AI at work. Before publishing, leave every unresolved finding with a named next action rather than turning “not checked” into “done.”