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DeepTeam

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Free plan APISelf-hosted
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Spec score 7.8 #10 of 27 in AI Red Teaming Tools

What it is

DeepTeam is an open-source Python framework for red teaming large language model systems. It offers more than 50 ready-to-use vulnerability checks covering bias, toxicity, personal-information leakage, prompt leakage, and security risks. Simulated attacks include jailbreaks, prompt injection, automated evasion, data extraction, and response manipulation. Vulnerability-specific LLM-as-a-Judge metrics return a binary score with reasoning. Supported reference frameworks 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 listings include GitHub Actions, GitLab CI, Jenkins, CircleCI, Buildkite, and Azure Pipelines. Installation uses pip in a Python virtual environment, and attacks are dynamically simulated without a prepared dataset. Results can be saved locally or sent to Confident AI for centralized viewing and production red teaming through a UI. The framework is Apache 2.0 licensed and supports self-hosted or API use.

Spec sheet

Purpose
DeepTeam is an open-source framework for red teaming LLM systems and finding risks and vulnerabilities.Read 8 Oct 2026 · trydeepteam.com
License
The site describes DeepTeam as an open-source LLM red teaming framework licensed under Apache 2.0.Read 8 Oct 2026 · trydeepteam.com
Maker
The site credits Confident AI Inc. and says DeepTeam is brought to users by the team behind DeepEval.Read 8 Oct 2026 · trydeepteam.com
Adversarial attacks
The framework simulates attacks including jailbreaking, prompt injection, automated evasion, data extraction, and response manipulation.Read 8 Oct 2026 · trydeepteam.com
CI/CD integrations
The site lists GitHub Actions, GitLab CI, Jenkins, CircleCI, Buildkite, and Azure Pipelines under CI/CD.Read 8 Oct 2026 · trydeepteam.com
Community support
The site links to a Discord community and GitHub repository.Read 8 Oct 2026 · trydeepteam.com
Dataset requirement
The documentation says a prepared dataset is not required because attacks are dynamically simulated during red teaming.Read 8 Oct 2026 · trydeepteam.com
Model providers
The site lists OpenAI, Claude, Gemini, Azure OpenAI, AWS Bedrock, Vertex AI, Mistral, LiteLLM, and Portkey among model providers.Read 8 Oct 2026 · trydeepteam.com
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.Read 8 Oct 2026 · trydeepteam.com
Risk scoring
DeepTeam evaluates LLM outputs with vulnerability-specific LLM-as-a-Judge metrics that return a binary score and reasoning.Read 8 Oct 2026 · trydeepteam.com
Setup
The documentation installs DeepTeam in a Python virtual environment with `pip install -U deepteam`.Read 8 Oct 2026 · trydeepteam.com
Standards
Supported frameworks include OWASP, NIST AI RMF, MITRE ATLAS, EU AI Act, BeaverTails, and Aegis.Read 8 Oct 2026 · trydeepteam.com
Vulnerability coverage
The documentation says DeepTeam offers 50+ ready-to-use vulnerabilities, including bias, toxicity, PII leakage, prompt leakage, and security risks.Read 8 Oct 2026 · trydeepteam.com

Plans and prices

Open-source framework Free Apache 2.0 licensed · Python framework · install with pip Read 8 Oct 2026 · trydeepteam.com

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Strong points and limits

Strong points

  • 50+ ready-to-use vulnerability checks
  • Dynamic attacks require no prepared dataset
  • Binary scores include reasoning
  • Many model-provider integrations listed
  • Apache 2.0 open source

Limits

  • Requires Python environment installation
  • Self-hosted deployment is specified
  • Central UI uses Confident AI

Who it suits

It suits teams assessing LLM vulnerabilities in development or production workflows. Python users wanting open-source, self-hosted red teaming and CI/CD integrations are the clearest audience.

Verdict

DeepTeam provides broad, standards-aware LLM red-team checks with dynamic attacks and CI/CD options. Its open-source, self-hosted model suits teams prepared to install and operate a Python framework.

Where Specifiction places it

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