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 this: the dashboard answers confidently, but the answer changes depending on wording, misses obvious subscription details, or starts following instructions that came from user content instead of your system rules. In business terms, that means bad support replies, broken onboarding, incorrect billing guidance, and a real chance of data exposure if the model starts treating untrusted text as instructions.

The most likely root cause is weak separation between trusted instructions and untrusted Airtable content. The first thing I would inspect is the exact payload flowing from Make.com into the model call: system prompt, user input, Airtable fields, any concatenated notes, and whether the assistant has access to tools or records without strict filtering.

Triage in the First Hour

1. Pull 10 recent failed conversations from logs.

• Look for wrong subscription status, invented plan names, or responses that ignore policy.
• Tag each failure as "hallucination", "prompt injection", "bad retrieval", or "tool misuse".

2. Open the Make.com scenario run history.

• Check which modules fed data into the AI step.
• Confirm whether Airtable fields are being passed raw into one long prompt.

3. Inspect the Airtable schema.

• Identify any fields that can contain free text from customers, admins, or imported content.
• Flag fields like "notes", "message", "description", "feedback", or "internal comments".

4. Review the AI prompt template.

• Check for missing role separation.
• Look for phrases like "follow all instructions in this record" or "use everything below as context".

5. Verify model settings and tool permissions.

• Check temperature, max tokens, function/tool access, and any external calls.
• Confirm whether the model can write back to Airtable or trigger actions without approval.

6. Inspect error handling and fallbacks.

• See what happens when Airtable returns empty data, multiple matches, or stale records.
• Confirm there is a safe fallback like "I will not verify that answer right now."

7. Review authentication and access scopes.

• Check Make.com connections, Airtable API tokens, and any shared credentials.
• Make sure nothing has broader access than it needs.

8. Test with one known malicious input in a staging copy only.

• Use harmless instruction-like text to see if the assistant follows user content over system policy.
• Do not test against production data.

Example diagnostic check:
- Input source: Airtable field -> AI prompt
- Risk: free text merged into system context
- Fix: wrap as quoted data + label as untrusted

Root Causes

| Likely cause | What it looks like | How I would confirm it | |---|---|---| | Untrusted Airtable text is mixed into instructions | The model obeys customer notes as if they were admin rules | Inspect raw prompt assembly in Make.com and look for concatenation without boundaries | | No instruction hierarchy | System rules are vague or missing | Review prompt template for clear priority order: system > developer > user > retrieved data | | Weak retrieval filtering | The model sees irrelevant records and answers from the wrong subscription | Compare returned Airtable rows against the actual account ID and plan status | | Tool access is too broad | The model can query or update records it should not touch | Audit Make.com module permissions and any write actions triggered by AI output | | No confidence gating | The assistant always answers even when data is incomplete | Check whether there is a threshold for missing fields or conflicting records | | Prompt injection through stored content | A note field contains hidden instructions that influence output | Search records for imperative language like "ignore previous" or "send this to..." |

The business risk here is not just wrong answers. It is support churn, refund requests, broken self-serve flows, and accidental disclosure of internal subscription data to the wrong user.

The Fix Plan

First, I would stop treating Airtable records as prompt text. I would treat them as data objects with strict labels, then build a safer prompt around them.

1. Separate trusted instructions from untrusted data.

• Put your system policy in one fixed block.
• Put user input in another block.
• Put Airtable output in a third block labeled as untrusted reference data.

2. Reduce what goes into the model.

• Send only the minimum fields needed to answer the question.
• Do not pass internal notes unless they are truly required.

3. Add record-level verification before generation.

• Match by authenticated user ID, email hash, or account ID.
• Reject any response if more than one active subscription matches.

4. Add a safe fallback path.

• If data is missing or conflicting, return a short human escalation message.
• Example: "I will not verify your subscription status right now. Support will confirm it manually."

5. Lock down Make.com scenarios.

• Remove unnecessary write permissions.
• Split read-only lookup scenarios from update scenarios.
• Require explicit approval before any action that changes billing or account state.

6. Normalize Airtable fields before AI use.

• Strip HTML where possible.
• Truncate long text fields.
• Remove hidden prompts or suspicious instruction patterns from free-text fields.

7. Add deterministic checks after model output.

• Validate plan names against an allowlist.
• Validate status values against known states like active, paused, canceled, trialing.
• Reject outputs that mention unsupported actions.

8. Lower model creativity for support tasks.

• Use low temperature settings for factual dashboard responses.
• For subscription support flows, I would usually keep temperature near 0 to 0.2.

9. Log every decision point safely.

• Store request ID, account ID hash, matched record IDs, confidence outcome, and fallback reason.
• Do not log secrets or full personal data.

10. Create an escalation path for edge cases.

• If prompt injection is detected or confidence drops below threshold, route to a human queue instead of guessing.

My recommendation is to fix this at the orchestration layer first rather than trying to "prompt engineer" around it. Prompt-only fixes are fragile; boundary controls plus validation are what make this production-safe.

Regression Tests Before Redeploy

Before shipping anything back to users, I would run these QA checks in staging with anonymized test accounts.

1. Subscription accuracy tests

• Given an active account, the assistant returns the correct plan and renewal date 10 out of 10 times.
• Given a canceled account, it never says active unless source data says reactivated.

2. Prompt injection tests

• A note field containing instruction-like text does not override system policy.
• The assistant ignores attempts to change role behavior from stored content.

3. Authorization tests

• One user cannot see another user's subscription details through reused IDs or guessed emails.
• Cross-account leakage rate must be 0%.

4. Missing-data tests

• If Airtable has no matching record, the assistant refuses to guess and escalates cleanly.
• No fabricated plans or dates appear in fallback responses.

5. Conflict tests

• If two records match one identity key, response must stop and escalate manually.

-- The system must not pick one at random.

6. Output validation tests

• Only approved statuses appear in responses.

Active means active; canceled means canceled; nothing else gets invented.

7. Load and latency checks

• P95 response time should stay under 2 seconds for normal lookups if your stack allows it at current volume.
• If latency rises above that because of retries or slow Airtable reads, cache safe read-only metadata where appropriate.

Acceptance criteria I would use:

• 0 critical prompt injection escapes in staging test set of at least 20 cases
• 100% correct subscription state on verified fixtures
• 100% fallback on missing identity match
• No unauthorized record reads across accounts
• No write actions without explicit approval

Prevention

I would put guardrails around three layers: workflow design, review discipline, and monitoring.

• Monitoring
• Track failed matches, fallback rate, manual escalations, and suspicious prompt patterns every day.
• Alert if injection-like phrases spike above baseline by more than 20%.

• Code review
• Review every change to prompts, mappings, filters, and scenario branches like production code.
• Never approve a workflow that mixes policy text with raw customer content without clear boundaries.

• Security controls
• Use least privilege API keys for Airtable and Make.com connections.

\- Rotate secrets every 90 days if you have ongoing admin access risk.\n \- Keep write actions behind separate approval steps.\n

• UX guardrails

\- Tell users when an answer is based on live account data versus general help content.\n \- Show clear error states instead of pretending certainty.\n

• Access to Make.com with edit rights\n2. Airtable base access plus schema notes\n3. Current AI prompt templates\n4. Sample failing conversations\n5. List of supported subscription states and actions\n6. Any compliance constraints for US,\nUK,\nor EU customers\n\nIf you want me to do this properly,\nI would scope it as a short rescue sprint:\nboundary fixes,\nauthorization checks,\nfallback logic,\nstaging validation,\nand deployment hardening.\nThat gives you a safer launch path than trying to patch symptoms one by one while customers are already using it.\n\n## References\n\n- https://roadmap.sh/api-security-best-practices\n- https://roadmap.sh/ai-red-teaming\n- https://roadmap.sh/qa\n- https://www.make.com/en/help/docs\n- https://support.airtable.com/docs/api-introduction

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Take the next step

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

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