Detailed Introduction
DeepTeam is a framework for red-teaming large language models (LLMs) and LLM systems, designed to help researchers and engineering teams systematically discover security, robustness, and adversarial weaknesses. The project provides testing strategies, attack templates, and measurement tools to validate model boundary behaviors and risks across deployment scenarios, delivering empirical evidence to guide model hardening.
Main Features
- Attack strategies and templates for generating adversarial inputs and scenarios.
- Evaluation tooling for assessing model safety, robustness, and reproducibility.
- Extensible testing pipelines to embed red-team workflows into CI/CD and evaluation processes.
- Open-source implementation for auditability, reproducibility, and community contributions.
Use Cases
- Pre-deployment security evaluations to identify potential abuse vectors or sensitive data leakage.
- Continuous robustness regression testing in enterprise or research settings to monitor model quality.
- Comparative assessments of defense strategies under realistic attack scenarios.
Technical Features
- Focus on reproducible, quantifiable evaluation with shared attack templates and metrics.
- Integration with retrieval, logging, and monitoring systems to collect rich signals during tests.
- Modular architecture to extend new attack strategies or plug in custom model endpoints.
- Community-driven development to quickly incorporate emerging attack vectors and defenses.