ShieldGPT：为大型语言模型构建智能安全防火墙

ShieldGPT is a comprehensive security solution designed for large language models (LLMs), built on DistilBERT and microservice architecture. It provides real-time threat detection, risk scoring, attack protection, and other core functions to safeguard LLM applications from malicious prompts, injection attacks, and suspicious input patterns. This post will break down its background, technical details, application scenarios, and more.

With LLMs widely applied across industries, security threats like prompt injection attacks, malicious inputs, and suspicious requests are becoming increasingly prominent. Traditional security measures struggle to address AI-specific attack patterns, creating an urgent need for LLM-tailored security solutions. ShieldGPT was developed to fill this gap, focusing on protecting LLM applications from such threats.

ShieldGPT adopts a microservice architecture, integrating React frontend, Node.js backend, and Python detection services to form a complete security system. Its core capabilities include real-time threat detection, risk analysis, prompt purification, and detailed security monitoring. The tech stack includes:

• Frontend: React19 + Vite7 + Tailwind CSS4 + Framer Motion
• Backend: Node.js + Express5 + MongoDB/Mongoose
• AI Detection Service: Python3 + Flask + DistilBERT (Hugging Face Transformers) + PyTorch

ShieldGPT's key security features are:

1. DistilBERT-based Threat Detection: Uses zero-shot classification to identify malicious prompts without pre-training on specific attack patterns, offering semantic understanding, generalization to unseen attacks, and lightweight deployment.
2. Risk Scoring System: Computes a 0-1 risk score based on model confidence, threat labels (MALICIOUS/SUSPICIOUS/SAFE), and input feature analysis, enabling flexible security policy configuration.
3. Prompt Purification: Automatically cleans inputs to remove/escape dangerous characters/patterns while preserving original intent.
4. Rate Limiting & Abuse Protection: Restricts requests per IP (100 requests/15 mins by default) to prevent resource exhaustion or attack probing.

ShieldGPT uses a three-layer microservice architecture:

• Frontend: React19-based, with chat interface (real-time security analysis) and security dashboard (threat stats, attack trends, logs, blocked IPs).
• Backend: Express server handling API routing, rate limiting, communication with detection/LLM services, and MongoDB log storage.
• Python Detection Service: Flask-based, loading pre-trained DistilBERT for prompt classification, returning threat labels/confidence, and supporting model hot updates.

ShieldGPT applies to:

1. Enterprise LLM Application Protection: Acts as a front-end security gateway for internal LLM apps (customer service bots, code assistants) to prevent prompt injection and sensitive info leaks.
2. Public AI Service Protection: Uses rate limiting and IP blocking to defend against automated attacks on public chat services.
3. Dev/Test Security Validation: Helps dev teams test LLM app security, identify prompt injection vulnerabilities before deployment.

ShieldGPT supports flexible deployment:

• Local Development: Run MongoDB, Python detection service, Node backend, and React frontend together for debugging.
• Production Deployment: Containerize services (Docker/K8s) for elastic scaling. Key API endpoints:
• POST /api/analyze: Analyze prompt risk.
• GET /api/logs: Retrieve analysis logs.
• GET /api/stats: Get dashboard stats.
• GET /api/blocked-ips: List blocked IPs.

ShieldGPT reflects the trend from passive to active intelligent detection in LLM security. Unlike rule-based systems, deep learning-based methods better handle semantic threats. Its architecture (independent AI detection service) allows flexible model updates and decoupling from business logic, making it reusable across scenarios. For developers/enterprises, it provides a complete reference implementation for building LLM security gateways.