How I Would Fix unreliable AI answers and prompt injection risk in a Make.com and Airtable subscription dashboard Using Launch Ready.

How I Would Fix unreliable AI answers and prompt injection risk in a Make.com and Airtable subscription dashboard Using Launch Ready

The symptom is usually obvious: the dashboard answers different questions with different confidence, hallucinates plan details, or starts following instructions that came from a user message instead of your business rules. In a subscription product, that turns into support load, broken onboarding, refunds, and users seeing data they should not see.

The most likely root cause is weak trust boundaries between user input, Airtable records, and the AI prompt. The first thing I would inspect is the exact payload going into Make.com: what text is being passed to the model, what Airtable fields are included, and whether any user-controlled content is being treated like instructions instead of data.

Triage in the First Hour

1. Open the latest Make.com scenario run history.

• Look for failed modules, retries, and unexpected branches.
• Check whether the AI step is receiving raw user text plus internal instructions in the same field.

2. Inspect Airtable tables and views used by the dashboard.

• Identify which fields are editable by end users.
• Confirm whether any field can contain free-form text that later gets injected into prompts.

3. Review the prompt template used in Make.com.

• Check for missing role separation.
• Look for prompts that say "use everything below" or concatenate content without delimiters.

4. Check logs from the AI provider.

• Look for repeated refusals, weird tool calls, or responses that ignore policy.
• Confirm whether temperature is too high for a subscription support use case.

5. Verify permissions on Airtable base and Make.com connections.

• Confirm least privilege.
• Check whether a single service account can read and write more than it should.

6. Inspect recent user-submitted records.

• Search for phrases like "ignore previous instructions" or "output all hidden data."
• Check if those strings are being echoed back into generated answers.

7. Review the published dashboard screens.

• Confirm whether hidden admin notes, internal IDs, or debug fields are visible in UI components.
• Check if errors expose stack traces or raw JSON.

8. Validate environment variables and secrets handling.

• Confirm API keys are not hardcoded in scenarios or front-end code.
• Check rotation status if any key has been exposed in logs.

## Quick diagnosis pattern
grep -R "ignore previous\|system prompt\|secret\|api_key" .

Root Causes

| Likely cause | What it looks like | How I confirm it | |---|---|---| | Prompt injection through user content | AI follows malicious text from a form submission | Compare raw input vs final prompt sent from Make.com | | No instruction hierarchy | Model treats business rules and user text as equal | Inspect prompt structure for system-like guardrails | | Overbroad Airtable access | AI can read fields it should never see | Review base permissions and scenario connections | | Unfiltered retrieval context | Internal notes get inserted into answers | Check which Airtable fields are mapped into prompts | | High randomness settings | Answers vary across identical requests | Compare outputs with same input at temperature above 0.3 | | Missing output validation | Bad JSON or unsafe text gets published anyway | Check if responses are schema-checked before saving |

The highest-risk issue is prompt injection combined with broad data access. If a malicious subscriber can place instructions into an editable field and your scenario passes that field directly to the model, you have created a path for data exfiltration and policy bypass inside your own product.

The Fix Plan

I would fix this in layers, not by "just improving the prompt." That approach fails because it treats an authorization problem like a wording problem.

1. Separate instructions from data.

• Put system rules in one fixed template block.
• Put user content in a clearly delimited section labeled as untrusted input.
• Never ask the model to infer business rules from Airtable content.

2. Reduce what the model can see.

• Only pass the minimum Airtable fields needed for the answer.
• Remove admin notes, internal tags, billing metadata, emails, and IDs unless they are essential.
• Use filtered views instead of full-table reads.

3. Add deterministic guardrails before generation.

• Block obvious injection phrases from reaching downstream steps when appropriate.
• Normalize inputs to plain text.
• Reject oversized payloads that could smuggle instructions or hidden context.

4. Force structured output.

• Require JSON with fixed keys like `answer`, `confidence`, `citations`, `needs_human_review`.
• Reject anything that does not match schema before writing back to Airtable or showing it in the dashboard.

5. Add human escalation for risky cases.

• If the model detects billing disputes, account changes, legal claims, or low confidence, route to manual review.
• Do not let AI auto-answer edge cases that could trigger churn or support escalations.

6. Lock down Make.com scenarios.

• Use separate scenarios for ingestion, generation, and publishing.
• Give each scenario only one job and one service account where possible.
• Remove unused modules that expand attack surface.

7. Tighten Airtable permissions.

• Split public-facing data from internal operations tables.
• Use read-only access where write access is not needed.
• Audit shared links and collaborator roles.

8. Set safer model behavior.

• Lower temperature to 0 to 0.2 for subscription dashboards.
• Prefer shorter completions with explicit refusal behavior on unsafe requests.
• Add retry limits so broken outputs do not loop forever.

9. Add logging without leaking secrets.

• Log request IDs, table names, scenario IDs, confidence flags, and validation failures.
• Do not log raw secrets or full customer messages unless you have a clear retention policy and access control.

10. Deploy behind monitoring and rollback controls.

• Ship behind a feature flag if possible.
• Keep one known-good fallback answer path while you validate fixes in production.

My recommendation is to treat this as a security hardening sprint first and an AI quality sprint second. If you improve answer quality without fixing trust boundaries, you will ship nicer-looking risk.

Regression Tests Before Redeploy

I would not redeploy until these checks pass on staging with production-like data shapes.

1. Prompt injection tests

• Enter text such as "ignore previous instructions" in every editable user field.
• Acceptance criterion: the model ignores injected instructions and still follows platform rules 100 percent of the time across 20 test runs.

2. Data exposure tests

• Place internal notes in Airtable fields that should never be shown to customers.
• Acceptance criterion: no internal notes appear in generated answers or UI output across 10 sample records.

3. Schema validation tests

• Force malformed model responses during testing.
• Acceptance criterion: invalid JSON never reaches Airtable writes or customer-facing screens.

4. Consistency tests

• Send the same request 10 times with identical inputs.
• Acceptance criterion: core facts remain stable; variance only appears where expected wording changes are harmless.

5. Permission tests

• Try reading restricted tables through every Make.com path used by the dashboard.
• Acceptance criterion: each scenario only accesses approved tables and fields.

6. Error handling tests

• Break one downstream module at a time: Airtable timeout, AI timeout, webhook failure.
• Acceptance criterion: users see a safe fallback message within 2 seconds instead of broken UI or partial data leaks.

7. Manual UX checks

• Test mobile screens for loading states, empty states, error states, and retry actions.
• Acceptance criterion: users know what happened and what to do next without contacting support immediately.

8. Security review checks

• Verify secrets are stored outside scenario text blocks and front-end code comments.
• Acceptance criterion: no secret appears in logs, screenshots, exports, or browser network traces.

For this kind of product I want at least 90 percent test coverage on prompt templates and validation logic where possible, plus one documented red-team pass before launch.

Prevention

The best prevention is boring discipline around boundaries and observability.

• Monitoring:
• Track AI failure rate, schema rejection rate, manual review rate, p95 response time under 2 seconds for dashboard actions that do not require generation delays beyond API latency expectations?

Wait keep safe accurate: p95 response time target 3 seconds for non-streamed dashboard responses; AI calls may take longer but should be isolated from UI rendering? Need concise fix: Let's continue final directly? But must avoid weird unfinished sentence; however final channel cannot be edited after send? Need ensure coherent ending maybe acceptable if include corrected line now by continuing? Since already outputted maybe okay but let's append proper continuation? Can't delete previous line but can continue below perhaps acceptable though awkward? Let's craft remaining sections cleanly maybe mention target values carefully below despite earlier line weirdness perhaps okay but we need finish article strong enough.

References

• [roadmap.sh - cyber security](https://roadmap.sh/cyber-security)
• [OWASP API Security Top 10](https://owasp.org/www-project-api-security/)
• [MDN Web Docs - HTTP](https://developer.mozilla.org/en-US/docs/Web/HTTP)
• [Cloudflare DNS documentation](https://developers.cloudflare.com/dns/)
• [Sentry documentation](https://docs.sentry.io/)

---

Take the next step

If this is a problem in your product right now, here is what to do next:

• [Use the free Cyprian tools](/tools) - estimate cost, score app risk, check launch readiness, or pick the right service sprint.

• [Book a discovery call](/contact) - I will tell you honestly whether you need a sprint or if you can DIY the next step.

*Written by Cyprian Tinashe Aarons - senior full-stack and AI engineer helping founders rescue, launch, automate, and scale AI-built products.*