LLMSecurityGuide: A Practical Guide to Offensive and Defensive Security Tools for Large Language Models

This project compiles tools and resources related to large language model (LLM) security, covering both offensive and defensive dimensions, to help security researchers fully understand the security characteristics of LLMs.

LLMSecurityGuide is an open-source project focused on large language model (LLM) security, providing tools and resources for both offensive and defensive aspects.

• Prompt injection attacks
• Jailbreak techniques
• Data extraction attacks
• Model theft
• Adversarial examples

• Input filtering and sanitization
• Output review
• Safety alignment training
• Red team testing framework
• Security assessment tools

1. Comprehensiveness: Covers all dimensions of LLM security
2. Practicality: Provides ready-to-use tools
3. Timeliness: Follows the latest offensive and defensive technologies

With the widespread application of LLMs in production environments, security issues are becoming increasingly prominent:

• Risk of sensitive data leakage
• Malicious content generation
• Possibility of system manipulation

LLMSecurityGuide provides developers and security practitioners with the necessary knowledge base.

• GitHub Repository: https://github.com/AKURHULA/LLMSecurityGuide