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

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

The symptom is usually the same: the client portal gives different answers to the same question, or it starts obeying instructions that came from a user upload, a pasted note, or an Airtable field that should never have been treated as trusted input. In business terms, this creates support load, wrong client actions, and a real risk of exposing private data or sending bad instructions to customers.

The most likely root cause is that untrusted content is being mixed into the AI prompt without strict boundaries. The first thing I would inspect is the exact path from Airtable record to Make.com scenario to model prompt to final response, because that is where prompt injection usually sneaks in.

Triage in the First Hour

1. Pull 10 recent bad responses.

• Group them by type: wrong answer, hallucination, leaked internal detail, ignored policy, or inconsistent tone.
• Note which Airtable record triggered each one.

2. Open the Make.com scenario run history.

• Check failed runs, retries, and any branches that changed the prompt.
• Look for modules that concatenate raw Airtable text directly into the system or developer message.

3. Inspect Airtable fields used in prompts.

• Identify which fields are user-editable versus admin-only.
• Flag any field that contains long free text, HTML, markdown, file notes, or pasted emails.

4. Review the model request payloads.

• Confirm whether context includes instructions plus user content in one block.
• Check if previous conversation history is being sent back without trimming or filtering.

5. Check logs for secret exposure.

• Verify no API keys, internal URLs, token values, or hidden prompts are returned in responses or logs.

6. Review access control in Airtable and Make.com.

• Confirm who can edit records feeding the AI.
• Check whether any public form or client upload can write directly into fields used by automation.

7. Inspect fallback behavior.

• If the AI fails or returns low confidence, does the portal show a safe error?
• Or does it guess and send something anyway?

8. Check rate and retry behavior.

• Repeated retries can amplify bad outputs and create duplicate customer actions.

A simple way to map the flow before touching anything:

Root Causes

| Likely cause | What it looks like | How I confirm it | |---|---|---| | Raw user content is merged into instructions | The model follows text from a ticket, note, or upload instead of portal rules | Inspect prompt construction and see whether untrusted text sits inside system-style language | | No trust separation between fields | Admin notes and client comments are treated the same | Compare Airtable field permissions and how each field is mapped in Make.com | | Missing output constraints | Answers drift in format, tone, or policy | Review whether there is a strict schema or response template | | Weak retrieval hygiene | The model pulls irrelevant records and answers from stale data | Test with records that should not be visible to that user | | No confidence gate | Low-quality outputs still get published | Check if there is a threshold for "do not answer" or human review | | Hidden prompt leakage | Internal instructions appear in answers | Search logs and outputs for system text fragments |

The Fix Plan

My recommendation is to stop treating this in an AI feature tuning problem first. I would treat it as an API security and data boundary problem, then rebuild the prompt path so only trusted data can influence trusted instructions.

1. Separate instruction from content.

• Keep system rules fixed and short.
• Put Airtable content into a clearly labeled "context" section with quotes or JSON structure.
• Never allow user-supplied text to sit inside instruction blocks.

2. Reduce what goes into the model.

• Only send fields needed for the current task.
• Remove internal notes, admin comments, private metadata, and raw attachments unless they are essential.

3. Normalize Airtable inputs before AI use.

• Trim long text.
• Strip HTML where possible.
• Reject suspicious phrases like "ignore previous instructions" when they come from untrusted fields.
• Add length limits so one bad record cannot dominate the prompt.

4. Add output structure.

• Force responses into a strict schema such as:
• answer
• confidence
• sources
• needs_human_review
• If the model cannot comply, fail closed instead of improvising.

5. Create a trust tier model.

• Trusted: app-owned rules, curated knowledge base entries, admin-only fields.
• Untrusted: client messages, uploads, form submissions, pasted email threads.
• Only trusted data may affect policy decisions.

6. Add a refusal path.

• If input contains suspicious instruction patterns or missing context, return:

"I will not answer this automatically yet. A human will review it."

• This protects accuracy better than guessing.

7. Move sensitive logic out of Make.com strings where possible.

• If complex prompt assembly lives inside scattered modules, centralize it in one controlled step or lightweight service layer.
• That makes review easier and reduces accidental leakage.

8. Lock down Airtable permissions.

• Separate tables for public intake versus internal operations if needed.
• Restrict who can edit fields used by automations.
• Use views instead of direct table access when possible.

9. Harden Make.com scenarios.

• Turn off unnecessary scenario triggers.
• Add validation after every inbound step.
• Log only safe metadata like record ID and status code, not full prompts or secrets.

10. Add monitoring on failure patterns.

• Alert on repeated low-confidence outputs,

negative feedback spikes, human escalations, and any response containing banned phrases or internal tokens.

If I were fixing this under Launch Ready conditions, I would keep deployment boring: no redesign spree, no new vendor stack unless required. The goal is to restore trust fast without breaking working flows.

Regression Tests Before Redeploy

Before shipping anything back to production, I would run risk-based tests against real portal flows and edge cases.

1. Prompt injection tests

• Paste malicious-looking text into every user-editable field that reaches Airtable.
• Confirm it does not override system rules or trigger hidden tool use.

2. Data boundary tests

• Verify client A cannot influence client B's answers through shared records or broad queries.

3. Output consistency tests

• Ask the same question 5 times with identical inputs.
• Answers should stay within a narrow range and follow the same schema.

4. Safe failure tests

• Remove required context and confirm the system refuses to guess.
• Expected result: human review path or safe error message.

5. Permission tests

• Attempt updates from roles that should not edit AI-driving fields.

"- Confirm access is denied at the app level and in Airtable."

6. Logging tests -"Check that logs do not contain secrets,"

7."Regression checks for retries" " "-"Trigger failures intentionally" " "-"Confirm duplicate sends do not happen"

Acceptance criteria I would use:

• 100 percent of AI responses match schema on test set of 25 cases
• Zero secret values appear in logs
• Zero cross-client data leaks across test records
• At least 90 percent of known injection attempts are refused or neutralized
• Human escalation fires within 1 minute when confidence is low

Prevention

I would put guardrails around three layers: product design, automation design,"and monitoring."

• Monitoring:

-"Alert on low-confidence responses" -"Alert on repeated fallback usage" -"Track manual corrections per week" -"Watch p95 response time so fixes do not slow the portal below 2 seconds"

• Code review:

-"Any change touching prompt assembly should require review" -"I check behavior first: who can influence instructions," "what data enters the model," "and what happens on failure"

• Security:

-"Use least privilege for Airtable tokens" -"Rotate secrets regularly" -"Keep Cloudflare protection on if there is any public intake surface" -"Add rate limits so one bad actor cannot spam injections"

• UX:

-"Tell users what the AI can and cannot do" -"Show loading," "empty," "and error states clearly" -"If an answer needs review," "say so plainly instead of pretending certainty"

• Performance:

-"Cache safe lookup results where appropriate" -"Do not send oversized context blobs" -"Keep response paths short so latency stays predictable"

A good target here is simple: fewer than 2 manual escalations per day after stabilization,"with zero confirmed data leaks."

When to Use Launch Ready

I would use this sprint if:

• The portal is live but unstable
• You need safer production deployment before more clients use it
• DNS,email,and SSL setup are still messy
• Secrets are exposed across tools like Make.com,Airtable,"or hosting settings"
• You need uptime monitoring before paid traffic resumes

What you should prepare:

• Access to hosting,DNS,"Cloudflare,"Make.com,"Airtable,"and your AI provider
• A list of current automations and tables
• Examples of good answers,bad answers,and any suspected injection attempts
• Your desired escalation rule for unsafe cases

My preference is to fix security boundaries first,demand consistent output second,and only then tune prompts."That order reduces launch risk faster than trying to make the model smarter."

References

• https://roadmap.sh/api-security-best-practices
• https://roadmap.sh/ai-red-teaming
• https://roadmap.sh/qa
• https://www.make.com/en/help
• https://support.airtable.com/docs

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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.*