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LLM Guard is an open-source security toolkit designed to protect large language model applications from various security risks and adversarial attacks. The library acts as a protective layer between users and language models by analyzing inputs and outputs before they reach or leave the model. It includes scanning mechanisms that detect malicious prompts, prompt injection attempts, toxic content, and other harmful inputs that could compromise AI systems. The toolkit also helps prevent sensitive information leaks by identifying secrets such as API keys or credentials before they are processed by the model. LLM Guard supports both input and output filtering pipelines, allowing developers to sanitize prompts and validate generated responses in real time. The library integrates easily with existing AI frameworks and can be deployed in production environments to enhance the security posture of LLM-based applications.
Features
- Input scanners that detect prompt injection and adversarial prompt attacks
- Output filters that identify harmful or policy-violating responses
- Secret detection system that prevents exposure of API keys or credentials
- Content sanitization tools that remove toxic or unsafe language
- Integration with AI frameworks and LLM pipelines for production deployment
- Security monitoring that evaluates prompts and responses in real time
Project Samples
