LLM Security Firewall

A production-ready security layer for AI applications that prevents prompt injection, data exfiltration, and policy violations.

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The Problem

Companies deploying LLMs face critical security risks:

• ❌ Prompt Injection - Users manipulate AI behavior with crafted inputs
• ❌ Data Exfiltration - Sensitive data leaks through AI responses
• ❌ Jailbreaks - Users bypass content policies and safety guardrails
• ❌ PII Exposure - Personal information revealed in outputs
• ❌ No Audit Trail - Can't track what users ask or what AI responds
• ❌ Cost Overruns - Uncontrolled API usage drains budgets

The Solution

LLM Security Firewall sits between your application and AI providers, providing:

✅ Real-time Threat Detection - Block attacks before they reach the AI ✅ Content Filtering - Redact sensitive data in requests and responses ✅ Policy Enforcement - Define and enforce usage policies ✅ Complete Audit Logs - Track every interaction for compliance ✅ Cost Control - Rate limiting and budget management ✅ Multi-Provider Support - Works with OpenAI, Anthropic, Azure, local models

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Quick Start

Installation

pip install llm-security-firewall

Basic Usage

from llm_firewall import LLMFirewall, FirewallConfig

# Initialize firewall
config = FirewallConfig(
    providers=["openai", "anthropic"],
    enable_prompt_injection_detection=True,
    enable_pii_redaction=True,
    enable_audit_logging=True,
)

firewall = LLMFirewall(config)

# Protect your LLM calls
async with firewall.protect() as session:
    response = await session.chat(
        provider="openai",
        model="gpt-4",
        messages=[{"role": "user", "content": user_input}]
    )

# Firewall automatically:
# - Scans for prompt injection
# - Redacts PII
# - Logs the interaction
# - Enforces rate limits
# - Returns safe response

As Middleware (FastAPI)

from fastapi import FastAPI
from llm_firewall.middleware import LLMFirewallMiddleware

app = FastAPI()

# Add firewall middleware
app.add_middleware(
    LLMFirewallMiddleware,
    config=config,
    api_key_header="X-API-Key",
)

@app.post("/api/chat")
async def chat(request: ChatRequest):
    # Requests automatically protected by firewall
    return await llm_client.chat(request)

As Proxy Server

# Start proxy server
llm-firewall-proxy --config config.yaml --port 8080

# Point your LLM SDK at the proxy
export OPENAI_BASE_URL=http://localhost:8080/openai
export ANTHROPIC_BASE_URL=http://localhost:8080/anthropic

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Features

🛡️ Threat Detection

Prompt Injection Detection

• Pattern-based detection (known attack vectors)
• ML-based classification (custom trained model)
• Confidence scoring (0-100%)
• Automatic blocking or warning mode

Jailbreak Prevention

• Detects attempts to bypass content policies
• Recognizes role-play attacks ("pretend you're...")
• Identifies multi-turn manipulation attempts

Data Exfiltration Protection

• Scans for requests asking for system prompts
• Detects attempts to extract training data
• Prevents credential/key harvesting

🔒 Content Security

PII Redaction

• Automatic detection and redaction:
  • Email addresses
  • Phone numbers
  • Credit card numbers
  • SSN/Tax IDs
  • IP addresses
  • API keys/tokens
• Reversible redaction (restore in logs)
• Custom redaction patterns

Response Filtering

• Scan AI responses for sensitive data
• Block or redact policy violations
• Custom content rules

Data Loss Prevention (DLP)

• Prevent specific data types from being sent/received
• Configurable allow/deny lists
• Regex-based pattern matching

📊 Policy Engine

Access Control

• User-based quotas (requests per hour/day)
• Model access restrictions
• Feature gating (by tier, role, etc.)
• IP allowlist/denylist

Cost Management

• Budget limits per user/team
• Token usage tracking
• Cost alerts and auto-cutoff
• Provider cost comparison

Compliance

• GDPR mode (data minimization)
• HIPAA compliance features
• SOC 2 audit logging
• Data residency controls

📈 Observability

Audit Logging

• Complete request/response logging
• Threat detection events
• Policy violations
• Performance metrics
• Export to SIEM (Splunk, Elastic, Datadog)

Real-time Monitoring

• Live dashboard
• Threat alerts (Slack, PagerDuty, webhook)
• Usage analytics
• Performance metrics

Analytics

• User behavior analysis
• Attack pattern detection
• Model performance comparison
• Cost optimization insights

---

Architecture

┌─────────────────┐
│   Application   │
└────────┬────────┘
         │
    ┌────▼────────────────────────────────────────┐
    │       LLM Security Firewall                 │
    │                                             │
    │  ┌──────────────────────────────────────┐  │
    │  │    Input Protection Layer             │  │
    │  │  • Prompt Injection Detection         │  │
    │  │  • PII Redaction                      │  │
    │  │  • Request Validation                 │  │
    │  └──────────────────────────────────────┘  │
    │                                             │
    │  ┌──────────────────────────────────────┐  │
    │  │    Policy Engine                      │  │
    │  │  • Access Control                     │  │
    │  │  • Rate Limiting                      │  │
    │  │  • Cost Management                    │  │
    │  └──────────────────────────────────────┘  │
    │                                             │
    │  ┌──────────────────────────────────────┐  │
    │  │    Output Protection Layer            │  │
    │  │  • Response Scanning                  │  │
    │  │  • Content Filtering                  │  │
    │  │  • Data Redaction                     │  │
    │  └──────────────────────────────────────┘  │
    │                                             │
    │  ┌──────────────────────────────────────┐  │
    │  │    Audit & Monitoring                 │  │
    │  │  • Event Logging                      │  │
    │  │  • Metrics Collection                 │  │
    │  │  • Alert Management                   │  │
    │  └──────────────────────────────────────┘  │
    └────────┬────────────────────────────────────┘
             │
    ┌────────▼────────┐
    │  AI Providers   │
    │  • OpenAI       │
    │  • Anthropic    │
    │  • Azure        │
    │  • Local LLMs   │
    └─────────────────┘

---

Configuration

YAML Configuration

# config.yaml
firewall:
  # Detection settings
  detection:
    prompt_injection:
      enabled: true
      threshold: 0.7  # 70% confidence
      action: block  # or 'warn'

    jailbreak:
      enabled: true
      patterns:
        - "ignore previous instructions"
        - "pretend you are"
        - "act as if"

    data_exfiltration:
      enabled: true
      sensitive_keywords:
        - "api key"
        - "password"
        - "secret"

  # Content filtering
  content:
    pii_redaction:
      enabled: true
      types:
        - email
        - phone
        - ssn
        - credit_card

    response_filtering:
      enabled: true
      block_patterns:
        - r"\b[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}\b"

  # Policy engine
  policy:
    rate_limiting:
      enabled: true
      limits:
        default: 100/hour
        premium: 1000/hour

    cost_management:
      enabled: true
      budget_limits:
        daily: 100.00
        monthly: 2000.00

    access_control:
      allowed_models:
        - gpt-4
        - gpt-4-turbo
        - claude-3-sonnet

  # Logging & monitoring
  logging:
    audit_log:
      enabled: true
      destination: postgresql
      retention_days: 90

    alerts:
      webhook_url: https://hooks.slack.com/services/YOUR/WEBHOOK
      alert_on:
        - high_risk_threat
        - policy_violation
        - budget_exceeded

Environment Variables

# Provider API keys
export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-ant-...

# Firewall settings
export LLM_FIREWALL_CONFIG=config.yaml
export LLM_FIREWALL_LOG_LEVEL=INFO
export LLM_FIREWALL_DATABASE_URL=postgresql://localhost/firewall

# Security
export LLM_FIREWALL_SECRET_KEY=your-secret-key
export LLM_FIREWALL_ENCRYPTION_KEY=your-encryption-key

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Use Cases

Enterprise SaaS Application

# Protect customer-facing chatbot
firewall = LLMFirewall(config)

@app.post("/api/chat")
async def chat(message: str, user_id: str):
    # Enforce per-user rate limits
    # Block prompt injection
    # Redact PII from user messages
    # Log all interactions for compliance

    async with firewall.protect(user_id=user_id) as session:
        return await session.chat(
            provider="anthropic",
            model="claude-3-sonnet",
            messages=[{"role": "user", "content": message}]
        )

Internal Developer Tools

# Protect code generation assistant
config = FirewallConfig(
    enable_prompt_injection_detection=True,
    enable_code_safety_analysis=True,  # Scan generated code
    enable_cost_tracking=True,
    cost_alerts={
        "daily": 50.00,
        "per_request": 0.50,
    }
)

firewall = LLMFirewall(config)

RAG Application

# Protect retrieval-augmented generation
async with firewall.protect() as session:
    # Prevent prompt injection in retrieved documents
    # Block attempts to extract training data
    # Redact sensitive data from vector DB results

    results = vector_db.search(query)
    response = await session.chat(
        provider="openai",
        model="gpt-4",
        messages=[
            {"role": "system", "content": "Use these docs: " + results},
            {"role": "user", "content": user_query}
        ]
    )

---

Deployment

Docker

# Pull image
docker pull llmsecurity/firewall:latest

# Run as proxy
docker run -p 8080:8080 \
  -e OPENAI_API_KEY=$OPENAI_API_KEY \
  -v $(pwd)/config.yaml:/app/config.yaml \
  llmsecurity/firewall:latest

Kubernetes

apiVersion: apps/v1
kind: Deployment
metadata:
  name: llm-firewall
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: firewall
        image: llmsecurity/firewall:latest
        env:
        - name: OPENAI_API_KEY
          valueFrom:
            secretKeyRef:
              name: llm-api-keys
              key: openai
        ports:
        - containerPort: 8080

AWS Lambda (Serverless)

# lambda_handler.py
from llm_firewall import LLMFirewall, FirewallConfig

config = FirewallConfig.from_env()
firewall = LLMFirewall(config)

def lambda_handler(event, context):
    user_input = event['body']['message']

    async with firewall.protect() as session:
        response = await session.chat(
            provider="openai",
            model="gpt-4",
            messages=[{"role": "user", "content": user_input}]
        )

    return {"statusCode": 200, "body": response}

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Performance

Latency Overhead:

• Pattern-based detection: ~5-10ms
• ML-based detection: ~20-50ms
• PII redaction: ~10-15ms
• Total average: ~35-75ms added latency

Throughput:

• 10,000+ requests/sec (pattern-based only)
• 2,000+ requests/sec (full protection)
• Scales horizontally with Redis cache

Resource Usage:

• Memory: ~200MB base + ~50MB per 1000 req/sec
• CPU: ~0.5 cores at 1000 req/sec

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Pricing

Open Source (MIT License)

• ✅ Core security features
• ✅ Self-hosted deployment
• ✅ Community support

Cloud (SaaS)

• Starter: $99/month

  • 100K requests/month
  • Basic threat detection
  • 30-day audit logs

• Professional: $499/month

  • 1M requests/month
  • Advanced ML detection
  • 90-day audit logs
  • Slack/PagerDuty alerts

• Enterprise: Custom pricing

  • Unlimited requests
  • Custom ML models
  • Unlimited log retention
  • SLA & dedicated support
  • On-premise deployment

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Roadmap

Q1 2026

• Core firewall MVP
• Prompt injection detection
• PII redaction
• Web dashboard
• SIEM integrations

Q2 2026

• Advanced ML models
• Multi-language support
• Custom policy DSL
• Terraform provider

Q3 2026

• Real-time collaboration features
• Advanced analytics
• Compliance certifications
• Enterprise features

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Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

MIT License - see LICENSE for details.

Support

• 📖 Documentation
• 💬 Discord Community
• 🐛 Issue Tracker
• 📧 Email Support

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