How do I set up a prompt firewall for restricting sensitive requests in an API middleware layer?

A good way to think about a prompt firewall is: it’s a policy enforcement layer in front of your model/API that inspects each request and decides whether to:

1. allow it,
2. block it,
3. redact or transform it, or
4. route it to a safer workflow.

For an API middleware layer, the goal is to stop sensitive or disallowed requests before they ever reach the model.

1) Define what you want to restrict

Start by writing a policy. Common categories:

• PII / secrets: passwords, API keys, tokens, SSNs, credit card numbers
• Sensitive business data: internal docs, customer records, proprietary code
• Abuse requests: malware, phishing, fraud, credential theft
• Policy-sensitive content: disallowed sexual content, self-harm, illegal instructions, etc.
• Prompt injection / exfiltration attempts: “ignore previous instructions,” “show system prompt,” “dump hidden context”

Make the policy explicit:

• What is always blocked
• What is allowed with redaction
• What is allowed only for certain users/roles
• What is allowed only if the data is synthetic/anonymized

2) Put the firewall in the request pipeline

A simple architecture:

Client → Auth → Prompt Firewall → Model Gateway → Model

And on the way back:

Model → Response Firewall → Client

You usually want both:

• Input firewall: blocks or sanitizes requests
• Output firewall: prevents sensitive leakage in responses

3) Use layered detection, not just one classifier

A robust prompt firewall combines several checks:

A. Rule-based checks

Fast and deterministic for obvious violations:

• Regex for API keys, JWTs, credit cards, SSNs
• Blocklists for known abuse phrases
• Allowlists for approved tools/actions
• Domain-specific policies

B. Classification model

Use a lightweight moderation or policy classifier to label requests:

• “PII present”
• “Credential request”
• “Malware intent”
• “Prompt injection”
• “Safe / unsafe”

C. Context-aware checks

A request can be safe alone but unsafe in context:

• User role
• Tenant/org policy
• Source application
• Conversation history
• Tool permissions
• Sensitive tags on retrieved documents

D. Retrieval and tool guards

If your app uses RAG or tools:

• Filter retrieved documents before injection into the prompt
• Only expose data the user is authorized to see
• Require approval for high-risk tools
• Validate tool arguments

4) Decide on actions for each policy violation

Typical actions:

• Block with a clear error
• Redact sensitive spans and continue
• Escalate to a human reviewer
• Route to safe completion (e.g., generic advice instead of specifics)
• Log for audit with minimized sensitive content

Example policy mapping:

• API key detected → redact + block if user tries to exfiltrate
• SSN in a support message → redact and allow
• “How do I phish users?” → block
• “Summarize this internal document” without access → block
• “Explain how to harden phishing detection” → allow

5) Implement middleware as a gatekeeper

Here’s a simple conceptual flow in middleware:

1. Receive request
2. Normalize text
   • lowercase
   • strip unicode tricks
   • remove zero-width chars
3. Run fast rule checks
4. Run classifier
5. Check user/tenant permissions
6. Redact or block based on policy
7. Forward sanitized prompt to model
8. Check model output
9. Log decision and risk score

6) Example pseudo-logic

def prompt_firewall(request, user):
    text = normalize(request.prompt)

    findings = []

    if contains_secrets(text):
        findings.append("secret")
    if contains_pii(text):
        findings.append("pii")
    if looks_like_prompt_injection(text):
        findings.append("injection")
    if policy_classifier(text) == "malicious":
        findings.append("malicious")

    if "malicious" in findings:
        return deny("Request violates safety policy")

    if "secret" in findings and not user.is_privileged:
        return deny("Sensitive secrets are not allowed")

    if "pii" in findings:
        text = redact_pii(text)

    sanitized = text
    return allow(sanitized)

7) Handle prompt injection specifically

Prompt injection defenses should look for:

• Requests to reveal system prompts, hidden policies, chain-of-thought, tool schemas
• Attempts to override instructions: “ignore previous”
• Encoded/obfuscated text
• “Print the full conversation”
• Data exfiltration requests from retrieved context

Mitigations:

• Keep system prompts out of the user-visible prompt if possible
• Separate instructions from data clearly
• Use structured templates
• Don’t let retrieved text become instructions
• Treat external content as untrusted
• Restrict tools with authorization and schemas

8) Add response filtering too

Even if the input is okay, the model can leak sensitive data.

Check outputs for:

• PII
• secrets
• internal-only content
• disallowed instructions
• tool results that should not be shown

If found:

• redact
• regenerate with constraints
• replace with a safe refusal

9) Logging, audit, and privacy

You need observability, but be careful:

• Log policy labels and hashes, not raw sensitive text
• Store redacted snippets only when needed
• Keep audit trails for denial decisions
• Monitor false positives/negatives
• Review by human analysts for edge cases

10) Test with adversarial cases

Create a test suite with:

• obvious attacks
• obfuscated attacks
• multilingual prompts
• code blocks containing secrets
• copied logs
• roleplay jailbreaks
• prompt injection in documents
• tool misuse attempts

Measure:

• precision/recall
• false block rate
• bypass rate
• latency impact

11) Practical deployment tips

• Put the firewall before expensive model calls
• Cache classification for repeated prompts
• Use a two-stage approach: cheap filters first, deeper analysis second
• Make policies configurable per tenant or app
• Version your policies
• Have a manual override for trusted operators
• Fail closed for high-risk operations

12) Example middleware design

A common production setup:

• Gateway layer: authentication, rate limiting
• Prompt firewall: content scanning, policy engine, redaction
• Prompt builder: assembles system/user/context safely
• Model router: selects model by sensitivity
• Response firewall: output scan and redact
• Audit service: immutable logs, metrics, alerts

13) Minimal policy checklist

You’re ready when you can answer:

• What counts as sensitive?
• What’s blocked vs redacted?
• Who can access what?
• How do you detect secrets/PII?
• How do you handle prompt injection?
• What gets logged?
• How do you test bypass attempts?
• What happens on classifier uncertainty?

If you want, I can also provide:

1. a reference architecture diagram,
2. example code for a FastAPI/Express middleware prompt firewall, or
3. a policy schema you can use to define allow/block/redact rules.