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DeepTeam
What it is
DeepTeam is an open-source Python framework for red teaming large language model systems. It simulates adversarial attacks and evaluates vulnerabilities including bias, toxicity, personal-information leakage, prompt leakage, and security risks. Documentation lists more than 50 ready-made vulnerabilities, with attacks such as jailbreaking, prompt injection, automated evasion, data extraction, and response manipulation. Vulnerability-specific LLM-as-a-Judge metrics return a binary score with reasoning. Supported standards include OWASP, NIST AI RMF, MITRE ATLAS, the EU AI Act, BeaverTails, and Aegis. Listed model providers include OpenAI, Claude, Gemini, Azure OpenAI, AWS Bedrock, Vertex AI, Mistral, LiteLLM, and Portkey. CI/CD connections include GitHub Actions, GitLab CI, Jenkins, CircleCI, Buildkite, and Azure Pipelines. A prepared dataset is unnecessary because attacks are simulated dynamically. Results can be stored locally or sent to Confident AI for centralized viewing and production red teaming. DeepTeam is Apache 2.0 licensed, self-hosted, and installed with pip.
For and against
For
- Open-source Apache 2.0 license
- Covers more than 50 vulnerability types
- Simulates jailbreak and prompt-injection attacks
- Supports major model providers and CI/CD tools
- No prepared dataset required
Against
- Self-hosted deployment required
- Python installation is required
- Results centralization uses Confident AI
- Framework focuses on LLM red teaming
The verdict
DeepTeam offers broad, dynamically generated adversarial testing with standards, providers, and CI/CD integrations. Its free, self-hosted model suits teams that can manage Python-based security workflows.
Who it suits
It suits developers and security teams evaluating LLM behavior before or during deployment. Teams using self-hosted Python tooling and CI/CD pipelines may benefit from its listed integrations.
Plans and prices
Open-source framework
Apache 2.0 licensed · Python framework · install with pip
Read 8 Oct 2026 · trydeepteam.com
On the record
- Adversarial attacks
- The framework simulates attacks including jailbreaking, prompt injection, automated evasion, data extraction, and response manipulation.trydeepteam.com · read 8 Oct 2026
- CI/CD integrations
- The site lists GitHub Actions, GitLab CI, Jenkins, CircleCI, Buildkite, and Azure Pipelines under CI/CD.trydeepteam.com · read 8 Oct 2026
- Community support
- The site links to a Discord community and GitHub repository.trydeepteam.com · read 8 Oct 2026
- Dataset requirement
- The documentation says a prepared dataset is not required because attacks are dynamically simulated during red teaming.trydeepteam.com · read 8 Oct 2026
- License
- The site describes DeepTeam as an open-source LLM red teaming framework licensed under Apache 2.0.trydeepteam.com · read 8 Oct 2026
- Maker
- The site credits Confident AI Inc. and says DeepTeam is brought to users by the team behind DeepEval.trydeepteam.com · read 8 Oct 2026
- Model providers
- The site lists OpenAI, Claude, Gemini, Azure OpenAI, AWS Bedrock, Vertex AI, Mistral, LiteLLM, and Portkey among model providers.trydeepteam.com · read 8 Oct 2026
- Purpose
- DeepTeam is an open-source framework for red teaming LLM systems and finding risks and vulnerabilities.trydeepteam.com · read 8 Oct 2026
- Results
- Risk assessments can be saved locally or sent to Confident AI, where users can view results centrally and run red teaming on production applications through a UI.trydeepteam.com · read 8 Oct 2026
- Risk scoring
- DeepTeam evaluates LLM outputs with vulnerability-specific LLM-as-a-Judge metrics that return a binary score and reasoning.trydeepteam.com · read 8 Oct 2026
- Setup
- The documentation installs DeepTeam in a Python virtual environment with `pip install -U deepteam`.trydeepteam.com · read 8 Oct 2026
- Standards
- Supported frameworks include OWASP, NIST AI RMF, MITRE ATLAS, EU AI Act, BeaverTails, and Aegis.trydeepteam.com · read 8 Oct 2026
- Vulnerability coverage
- The documentation says DeepTeam offers 50+ ready-to-use vulnerabilities, including bias, toxicity, PII leakage, prompt leakage, and security risks.trydeepteam.com · read 8 Oct 2026
