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JoySafety - JD's Open-Source Large Model Security Framework

Author: AI小集 · Original source: AI工具集

What is JoySafety

JoySafety is an open-source large model security framework from JD, providing enterprises with a mature, reliable, and free large model security protection solution. The model is based on various atomic capability modules (such as BERT, FastText, Transformer, etc.) and a flexible architecture design, supporting high concurrency, high scalability, and dynamic policy updates. It can effectively intercept high-risk content and ensure the safe and stable operation of AI applications. The model is easy to use, supporting one-click Docker startup and standardized API interfaces for quick integration.

JoySafety

Main Features of JoySafety

  • Content Security Detection: Through sensitive content interception, red-line automated responses, and intelligent correction, it ensures that output content complies with legal and ethical requirements, providing enterprises and users with a safe and reliable AI interaction environment.
  • Data Security Protection: Detects and prevents data leakage, identifies and protects sensitive information in user inputs and model outputs, and ensures that data processing complies with privacy regulations.
  • Prompt Attack Defense: Based on multi-turn dialogue recognition technology, it intelligently analyzes context, identifies and defends against malicious prompt attacks, ensuring the security and stability of AI dialogue systems.
  • Policy Orchestration and Dynamic Updates: Supports dynamically adjusting security policies through APIs, enabling hot-plugging of atomic capabilities, flexibly adapting to different business scenarios, and updating detection modules without interrupting services.
  • High Scalability and Load Balancing: Supports clustered deployment, and through horizontal scaling and intelligent load balancing technology, it enhances the system's concurrent processing capability to meet high-concurrency business needs.
  • Ease of Use and Integration: Enables one-click startup via Docker Compose, provides standardized API interfaces, supports multi-language calls, and facilitates quick integration with existing systems.

Technical Principles of JoySafety

  • Multi-Model Collaborative Detection: Classifies and detects input content through multiple pre-trained models (BERT/FastText/Transformer, etc.) to identify potential risks. : Combines sensitive word libraries and red-line knowledge bases to provide precise interception and automated response strategies.
  • Asynchronous Detection Mechanism: Adopts an asynchronous output mechanism, decoupling detection tasks from the main business process, greatly improving response speed and reducing user waiting time. Through multi-turn dialogue context, it intelligently identifies potential prompt attacks or malicious guidance to ensure dialogue security.
  • Dynamic Policy Updates: Hot-plugging of atomic capabilities supports dynamically updating detection modules at runtime without restarting services. Through a lightweight orchestration engine, detection policies can be flexibly configured to adapt to different business needs.
  • Intelligent Load Balancing: Supports horizontal scaling, enhancing the system's concurrent processing capability through load balancing technology. Based on a windowed detection strategy, it finds a balance between performance and accuracy, optimizing system resource utilization.

JoySafety Project Address

  • GitHub Repository：https://github.com/jd-opensource/JoySafety

Application Scenarios of JoySafety

  • AI Shopping Guide: Provides e-commerce platforms with safe and reliable recommended content, avoiding misleading consumers and ensuring that recommended information complies with legal and ethical standards.
  • Logistics Customer Service: In logistics customer service scenarios, it intercepts inappropriate remarks and sensitive information, provides compliant customer support, and improves service quality.
  • Sales Assistant: Help salespeople prevent sensitive information leaks when communicating with customers, safeguard trade secrets, and avoid legal risks.
  • Medical Consultation: Filter non-compliant content in medical consultations to ensure that the medical advice provided is safe and reliable and complies with medical regulations and ethical requirements.
  • Merchant Workbench: Protect merchants' data security on the platform, prevent privacy leaks, and ensure that merchant information complies with privacy regulations.