How I Would Fix unreliable AI answers and prompt injection risk in a React Native and Expo subscription dashboard Using Launch Ready.

How I Would Fix unreliable AI answers and prompt injection risk in a React Native and Expo subscription dashboard Using Launch Ready

The symptom is usually obvious: the dashboard gives confident but wrong answers, or it starts echoing user content back into the AI response in ways that should never happen. In a subscription product, that turns into bad account guidance, support tickets, refund requests, and trust loss fast.

The most likely root cause is that the app is sending too much untrusted text into the model, with weak system instructions and no output validation. The first thing I would inspect is the full request path from the Expo client to the AI provider, including what context gets attached, where secrets live, and whether any user-generated content can override instructions.

Triage in the First Hour

1. Check recent support tickets and failed user journeys.

• Look for wrong billing answers, plan confusion, or responses that mention hidden internal data.
• Count how many failures happened in the last 24 hours.

2. Inspect AI request logs.

• Review prompts, tool calls, model responses, token counts, and error rates.
• Confirm whether user messages are being mixed with system instructions.

3. Open the Expo app flows that trigger AI output.

• Test onboarding, subscription status screens, billing help, and account settings.
• Check if any screen allows pasted text to be sent directly into the model.

4. Review environment variables and secrets handling.

• Confirm API keys are not bundled into the mobile app.
• Check for leaked keys in `.env`, build config, or client-side code.

5. Inspect backend or serverless functions.

• Verify there is a server-side proxy between the app and the model provider.
• Confirm auth checks happen before any AI request is made.

6. Review model outputs against expected formats.

• If you expect JSON, confirm responses are actually valid JSON.
• If you expect short answers, check for long rambling output or policy drift.

7. Check monitoring dashboards.

• Look at error spikes, latency spikes, and retry loops.
• If p95 latency is above 2 seconds on AI calls, users will feel it immediately.

8. Reproduce one bad answer manually.

• Use a known risky input containing instructions like "ignore previous directions."
• Confirm whether the app follows those instructions or safely ignores them.

## Quick diagnostics I would run on the backend logs
grep -R "ignore previous" logs/ | tail -20
grep -R "tool_call\|function_call" logs/ | tail -20
grep -R "OPENAI_API_KEY\|ANTHROPIC_API_KEY" . --exclude-dir=node_modules --exclude-dir=.git

Root Causes

| Likely cause | What it looks like | How I would confirm it | |---|---|---| | Prompt injection through user content | Model follows malicious instructions inside chat input or profile notes | Test with hostile text in a safe staging copy and inspect whether system rules are ignored | | No server-side trust boundary | Expo app calls AI provider directly or exposes keys in client code | Search network calls and build output for provider endpoints and secrets | | Weak prompt structure | System prompt is vague and user content is appended without clear separation | Review prompt template and see if roles are mixed together | | No output schema validation | Model returns free-form text when app expects strict fields | Check whether responses are parsed without validation or fallback handling | | Overbroad context retrieval | RAG pulls private account data not needed for the current task | Inspect retrieval filters and confirm only minimum necessary data is sent | | Missing authz checks before AI use | A user can ask about another user's plan or invoices through indirect prompts | Verify account ownership checks happen before any data lookup or tool call |

The biggest business risk here is not just wrong answers. It is accidental exposure of customer data through over-shared context or unsafe tool use. That can create support load, legal risk under GDPR or UK GDPR, and a very expensive cleanup later.

The Fix Plan

First, I would move all AI calls behind a server-side endpoint if they are not already there. The mobile app should send only an authenticated request with a narrow purpose like "explain current subscription status," not raw internal notes or full account history.

Second, I would separate trusted instructions from untrusted content. System rules must stay fixed at the top level, while user text gets treated as data only. Any content pulled from tickets, emails, reviews, or uploaded files should be explicitly marked as untrusted.

Third, I would reduce what context gets sent to the model. For a subscription dashboard, most answers do not need full profile history; they need plan name, renewal date, payment state, and maybe one support note. Less context means less attack surface and lower token cost.

Fourth, I would force structured output for any answer that drives UI behavior. If the screen expects `status`, `reason`, `next_action`, then validate that shape before rendering anything to users. If validation fails, show a safe fallback instead of guessing.

Fifth, I would add an allowlist for tools and actions. The model should never be able to trigger arbitrary functions from user text alone; it should only call pre-approved actions with strict input validation and authorization checks.

Sixth, I would add human escalation paths for uncertain cases. If confidence is low or the answer touches billing changes, refunds, account deletion, or legal language, route it to support instead of letting the model improvise.

My preferred implementation order:

1. Lock down secrets and move provider access server-side. 2. Split trusted system prompts from untrusted user content. 3. Add schema validation on every AI response. 4. Add authorization checks before retrieval or tool use. 5. Add logging with redaction so we can debug safely. 6. Ship behind a feature flag to 10 percent of users first.

This is one place where speed without control creates more work later. A 48 hour fix sprint should aim to reduce incident risk now rather than rebuild everything at once.

Regression Tests Before Redeploy

I would not redeploy until these checks pass:

• Prompt injection test:
• Input contains hostile instructions like "ignore all prior rules."
• Expected result: model refuses to follow them and stays within scope.

• Data exposure test:
• Use a test account with private billing notes.
• Expected result: no other user's data appears anywhere in output.

• Schema validation test:
• Break one field in a mocked response.
• Expected result: UI shows fallback state instead of crashing.

• Authz test:
• Try accessing another user's subscription details with a valid session.
• Expected result: request is denied before any AI call runs.

• Error handling test:
• Simulate provider timeout or rate limit failure.
• Expected result: graceful message appears within 2 seconds.

• Mobile UX test:
• Check loading states on iOS and Android devices.
• Expected result: no frozen spinner longer than 3 seconds without feedback.

Acceptance criteria I would use:

• Zero exposed API keys in client bundles.
• All AI requests go through authenticated backend routes.
• Response schema validation passes at 100 percent on staging fixtures.
• p95 AI response time stays under 2 seconds on cached paths and under 4 seconds on uncached paths.
• No critical security findings in code review before release.
• At least 20 red-team test cases pass without unsafe behavior.

Prevention

I would put three guardrails in place so this does not come back next month.

First is monitoring. Track prompt failures separately from normal API errors so you can see when answer quality drops before customers complain. Log redacted prompts, validation failures, refusal rates, tool calls, latency p95/p99, and fallback usage.

Second is code review discipline. Any change touching prompts, retrieval logic, authz checks, or secrets handling needs senior review before merge. I care more about behavior changes than style changes here because one sloppy edit can expose customer data across accounts.

Third is security by design. Keep least privilege on every service account used by your backend functions. Rotate keys regularly, set CORS narrowly if needed for web surfaces too, and never let the mobile app hold privileged credentials.

I also recommend simple UX guardrails:

• Show when an answer came from AI versus deterministic account data.
• Offer "contact support" when confidence is low.
• Keep destructive actions out of free-text chat entirely.
• Use clear empty states when no subscription data exists yet.

For performance safety:

• Cache non-sensitive plan metadata where possible.
• Avoid sending large message histories on every tap.
• Trim third-party scripts from any web companion surface tied to this flow because they add noise and risk without helping answer quality.

When to Use Launch Ready

Use Launch Ready when you need this fixed fast without turning your product into a science project.

This sprint fits best if:

• Your Expo app already works but AI answers are unreliable.
• You suspect prompt injection or data leakage risk.
• You need production-safe deployment before paid traffic lands on it.
• You want one senior engineer to clean up launch blockers quickly.

What I need from you before starting:

• Repo access for the Expo app and any backend functions.
• Access to your hosting provider and DNS registrar if deployment work is included.
• Current env var list without secret values pasted into chat unnecessarily.
• A short description of which screens use AI and what each one should do.
• One staging account plus one test subscription record for QA.

book here: https://cal.com/cyprian-aarons/discovery

References

1. roadmap.sh API Security Best Practices: https://roadmap.sh/api-security-best-practices 2. roadmap.sh Cyber Security: https://roadmap.sh/cyber-security 3. roadmap.sh AI Red Teaming: https://roadmap.sh/ai-red-teaming 4. OpenAI API Security best practices: https://platform.openai.com/docs/guides/safety-best-practices 5. Expo documentation: https://docs.expo.dev/

---

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.*