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Use case detail

Prompt-injection risk for Subscription app teams using eval replay

A governed Tier-0 support workflow for subscription app teams handling prompt-injection risk with eval replay, human approval, policy checks, and audit evidence.

Author
The Tier-0 Team
Updated
May 24, 2026
Risk
critical
Surface
Evals
Direct answer

What Tier-0 does in this scenario.

Tier-0 helps subscription app teams handle prompt-injection risk by combining eval replay with the product's core support loop: verified intake, project routing, AI triage, approved-source context, policy-bound drafting, human approval, and audit evidence. The AI can prepare support work, but the product contract keeps customer-facing side effects under deterministic policy and human review.

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Q&A

Questions this page answers.

These answers are visible page content for readers and answer engines. They are not marked as QAPage data because this is not a user-submitted forum thread.

Can Tier-0 automatically resolve prompt-injection risk for subscription app teams?

No. Tier-0 can prepare the support work for prompt-injection risk: classify the request, summarize the thread, retrieve approved sources, draft a reply, and surface risk. Customer-facing sends and sensitive side effects still require policy checks and human approval.

Which Tier-0 surface matters most for eval replay?

Evals and replay is connected to the Evals surface. In this scenario, it should test prompt-injection, classification, and draft-safety behavior against stored fixtures, while keeping raw email replay, golden tests, schema validation visible to the reviewer.

What should the reviewer verify before sending?

The reviewer should verify the workspace, project route, customer context, approved knowledge sources, applicable policy, draft tone, and audit trail. For prompt-injection risk, the page should never imply a refund, account action, timeline, legal conclusion, or security claim unless the product policy and reviewer support it.

What happens when approved knowledge is missing?

The safe answer is review or escalation. Tier-0 should not invent source citations or pretend the workspace has a policy that is not present in approved project knowledge.

1.0

Why prompt-injection risk need a governed workflow

Subscription app teams usually face billing sensitivity, customer churn risk, approval gates. When the request involves prompt-injection risk, Tier-0 treats the message as operational support work instead of a generic chatbot exchange. The goal is to help a reviewer understand the customer need, the project context, the policy boundary, and the next safe action.

  • How should support systems handle prompt-injection attempts in emails and documents?
  • Risk level: critical. Signals to review: hostile instructions, hidden text, bypass attempts.
  • The system must treat external content as data, flag suspicious instructions, and require review.
2.0

How Evals and replay fits the Tier-0 support loop

Evals and replay is tied to the Evals surface. It is designed to test prompt-injection, classification, and draft-safety behavior against stored fixtures. The workflow does not turn the model into an operator. It gives operators structured context, draft assistance, and evidence so they can move faster without hiding risk.

  • Control: raw email replay.
Product limits

What this page does not claim.

These limits are part of the content, not fine print. They keep the page aligned with the documented product contract.

Tier-0 is not a mailbox host, custom SMTP provider, cold outreach tool, or enterprise ticketing suite.
AI-generated replies do not send themselves in V1; customer-facing sends require review and approval.
The product does not perform autonomous refunds, cancellations, billing changes, password resets, or destructive actions.
Source citations depend on approved project knowledge. If knowledge is missing, the safe response is review or escalation, not invention.
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Continue through the use case graph.

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Control: golden tests.
  • Control: schema validation.
    • 3.0

    What the AI prepares

    The AI layer can classify the request, summarize the thread, retrieve approved knowledge, draft a reply, cite supporting sources, and propose escalation. Those outputs remain untrusted until they pass schema validation, policy checks, and human review. That boundary is the point of Tier-0: AI prepares the work while the workspace remains accountable for what customers receive.

    • Classification covers intent, urgency, sentiment, confidence, and risk.
    • Drafts should cite approved project knowledge when a source was used.
    • Low confidence, high risk, and policy-sensitive replies stay review-bound.
    4.0

    What a reviewer should check before sending

    For prompt-injection risk, the reviewer should confirm the route, customer identity, project policy, source coverage, draft tone, and audit path. Tier-0 is intentionally not a cold email platform, generic CRM, custom SMTP host, or fully autonomous support bot. The support action should remain tied to a real customer request and a project-specific support policy.

    • Confirm the thread belongs to the right workspace and project.
    • Check whether approved knowledge actually supports the proposed answer.
    • Confirm the draft does not promise refunds, timelines, account changes, or legal conclusions without review.
    5.0

    How this page stays factual

    This page describes the documented Tier-0 product contract: verified support intake, project-scoped policy, knowledge retrieval, AI drafting, human approval, security checks, and audit logging. It does not claim autonomous resolution, guaranteed rankings, provider uptime, or outcomes that are not present in the product documentation.

    • The model may propose; deterministic policy and human approval own customer-facing actions.
    • External customer content and crawled sources are treated as untrusted input.
    • Every meaningful support action should preserve evidence for later review.
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    Dashboard guideAnother factual Tier-0 route connected by audience, intent, workflow, docs, or the use case library.