Quick Introduction
- Detect vulnerabilities such as bias, misinformation, PII leakage, over-reliance on context, harmful content, and more - Simulate adversarial attacks with 10+ methods (jailbreaks, prompt injection, ROT13, automated evasion, data extraction, etc.) - Customize assessments to OWASP Top 10 for LLMs, NIST AI Risk Management, or your own security guidelines - Leverage DeepEval under the hood for robust metric evaluation, so you can run both regular and adversarial tests
Getting Started
# Install DeepTeam
pip install -U deepteam
# Clone the repo and run the example
git clone https://github.com/confident-ai/deepteam.git
cd deepteam
python3 -m venv venv && source venv/bin/activate
python examples/red_teaming_example.py
Why DeepTeam?
Most LLM safety tooling focuses on known benchmarks—DeepTeam dynamically simulates attacks at runtime, so you catch novel, real-world threats and can track improvements over time via reusable attack suites.
We’d love to hear:
- Which vulnerabilities you worry about most - How you integrate red-teaming into your CI/CD pipelines - Feature requests, contributions, and your toughest jailbreak stories