加载中
正在获取最新内容，请稍候...
The LLM Red Teaming Framework
A comprehensive open-source framework designed for systematically identifying and mitigating vulnerabilities in Large Language Models (LLMs) through automated testing and analysis.
Python
Added on 2025年6月11日359
Stars50
ForksPython
LanguageProject Introduction
Summary
The LLM Red Teaming Framework is an essential tool for evaluating the safety and security of Large Language Models. It automates the process of discovering potential risks and vulnerabilities before deployment, enabling developers and researchers to build more reliable and safer AI systems.
Problem Solved
Large Language Models can exhibit harmful behaviors, including generating toxic or biased content, revealing sensitive information, or being susceptible to prompt injection attacks. Manually identifying these risks is time-consuming and inefficient. This framework provides a systematic, automated approach to proactively discover and document these vulnerabilities.
Core Features
Adversarial Prompt Generation
Automated generation of adversarial prompts to test LLM robustness against various attacks (e.g., jailbreaking, prompt injection).
Vulnerability Scanning
Scans LLM outputs for predefined undesirable content such as toxicity, bias, privacy violations, and security vulnerabilities.
Comprehensive Reporting
Provides detailed reports on identified vulnerabilities, including severity, attack type, and example inputs/outputs.
Tech Stack
Python
PyTorch/TensorFlow
Transformers (Hugging Face)
Docker
SQLAlchemy (Optional for reporting DB)
使用场景
The framework can be applied in various scenarios where rigorous testing of LLM behavior is required:
场景一：新模型安全评估
Details
Evaluate a newly fine-tuned or pre-trained LLM model against known attack types to assess its inherent safety level.
User Value
Provides a baseline safety score and identifies specific weaknesses of the model.
场景二：应用集成前/后测试
Details
Regularly scan the LLM endpoint used by an application to detect potential regressions in safety or newly discovered vulnerabilities.
User Value
Ensures the LLM component of an application remains safe and robust over time.
场景三：合规性验证
Details
Used as part of compliance checks for deploying LLMs in regulated industries.
User Value
Generates documented evidence of safety testing for audits and regulatory requirements.
Recommended Projects
You might be interested in these projects
apachedoris
Apache Doris is an easy-to-use, high performance and unified analytics database.
Java
138293466
vaxilux-ui
X-UI is a powerful web panel designed to manage the Xray core, offering support for multiple protocols and user accounts. It simplifies the deployment, configuration, and monitoring of Xray servers through an intuitive graphical interface.
JavaScript
176297792
Automatticharper
Harper is a fast, offline, and privacy-first grammar checker powered by Rust. It is designed for users who value security and speed, enabling grammar and style checks without sending text over the internet.
Rust
5936143