How do I set up an LLM firewall for streaming text inspection and tool-call filtering in our app?

A practical “LLM firewall” is usually a policy enforcement layer that sits between your app and the model/tools. For streaming text inspection and tool-call filtering, design it as a proxy/sidecar with two separate controls:

1. Token stream inspector for model output as it arrives.
2. Tool policy gate for any function/tool invocation before it executes.

1) Recommended architecture

Client/App → LLM Firewall → Model API and Model tool-call → LLM Firewall → Allowed tool executor

Core components:

• Policy engine: rules for allowed content, allowed tools, allowed arguments, max lengths, regex/JSON-schema checks, PII/DLP checks, etc.
• Streaming buffer/inspector: evaluates chunks/tokens as they arrive.
• Tool-call validator: checks tool name, arguments, user permissions, rate limits, and risk.
• Action router: can allow, block, redact, truncate, require human review, or sandbox.

2) Streaming text inspection

For streaming responses, you need to inspect incrementally without waiting for the full completion.

Common patterns

• Chunk-level scanning: inspect each SSE/WebSocket chunk.
• Sliding window scanning: keep the last N characters/tokens to catch patterns split across chunks.
• Stateful classifiers: maintain context so you can detect ongoing policy violations.

What to inspect

• Secrets/API keys
• PII/PHI
• Disallowed content
• Prompt injection attempts in model output if it echoes hidden instructions
• Policy-specific keywords or patterns
• Unsafe URLs or code blocks if your app disallows them

Handling violations

• Soft redact: replace offending spans with [REDACTED]
• Abort stream: stop the model response if severe
• Quarantine: buffer until classification is safe, then release
• Annotate: add safety notices to downstream consumers

Important implementation note

If you need strict guarantees, do not forward raw stream chunks directly to the user. Instead:

1. Receive chunk
2. Run inspection
3. Emit safe chunk or block
4. Log decision with audit metadata

3) Tool-call filtering

Treat tool calls as untrusted requests from the model.

Validate before execution

Check:

• Tool name allowlist
• Argument schema against JSON Schema/Pydantic
• User authorization: can this user trigger that action?
• Resource constraints: limits on file access, records, network domains, spend, etc.
• Risk heuristics: suspicious URLs, SQL-like input, destructive actions
• Context checks: does the tool call match the user’s intent?

Recommended controls

• Allowlist only: no arbitrary tools
• Per-tool policies:
  • read_user_profile: allowed
  • delete_account: require human confirmation
  • send_email: restrict recipients/domains
  • execute_sql: only parameterized queries, no raw SQL from model
• Argument normalization: canonicalize URLs, paths, emails before checks
• Least privilege: tools should operate only on scoped data

Example policy rules

• Block any tool call with unknown tool_name
• Block if arguments fail schema validation
• Block if path escapes approved directory
• Block if URL domain not in allowlist
• Require human approval for irreversible operations
• Limit number of tool calls per conversation

4) Practical policy flow

A simple decision pipeline:

1. Input to model
   • Sanitize user text
   • Detect prompt injection or policy violations
2. Model output stream
   • Chunk inspection
   • Redact/block as needed
3. Tool call request
   • Parse structured tool call
   • Validate schema
   • Check authorization/policies
   • Execute only if approved
4. Post-execution
   • Inspect tool results before sending back to model/user

5) Implementation sketch

Streaming response gate

Pseudo-flow:

for chunk in model_stream:
    verdict = inspect(chunk, conversation_state)
    if verdict == "allow":
        forward(chunk)
    elif verdict == "redact":
        forward(redact(chunk))
    elif verdict == "block":
        stop_stream()
        send_safe_error()
        log_incident()

Tool-call gate

on_tool_call(tool_name, args, user_context):
    if tool_name not in allowed_tools:
        deny()
    if not validate_schema(tool_name, args):
        deny()
    if not authorize(user_context, tool_name, args):
        deny()
    if risk_score(args) > threshold:
        require_human_approval()
    return execute_tool(args)

6) Use a policy engine

Instead of hardcoding rules, define them in something like:

• OPA/Rego
• Cedar
• Custom YAML policies
• Guardrails/JSON Schema + custom checks

Example policy categories:

• Content safety
• Data loss prevention
• Tool access control
• Output formatting
• Rate limiting
• Human approval thresholds

7) Logging and observability

Log:

• User/session ID
• Model ID and prompt version
• Stream chunks and verdicts
• Tool call name, args hash, verdict
• Policy triggered
• Latency added by firewall

Be careful to avoid logging sensitive payloads in plaintext; store hashes or redacted excerpts.

8) Deployment patterns

• Inline proxy: simplest, but adds latency
• Sidecar: good for microservices
• API gateway plugin: centralized governance
• Dedicated internal service: best for multi-app org-wide policy

9) Failure modes to design for

• Partial stream already sent before violation detected
• Tool call arguments split or nested unexpectedly
• Model tries to smuggle instructions in JSON strings
• False positives on benign content
• Policy drift as toolset changes

Mitigations:

• Buffer a small amount before release
• Strict schema parsing
• Versioned policies
• Human-review path for sensitive operations
• Continuous testing with adversarial prompts

10) Minimal checklist

• Put the model behind a proxy/firewall
• Inspect streamed output incrementally
• Block/redact unsafe chunks
• Allowlist tools only
• Validate tool-call schemas
• Enforce authZ and per-tool policies
• Restrict destructive actions
• Log decisions safely
• Add rate limits and human approval for high-risk actions

If you want, I can also give you:

1. a reference architecture diagram,
2. a Python/FastAPI streaming proxy example, or
3. an OPA/Rego policy sample for tool-call filtering.