Advent Prompt Pwn vs DeepTeam
Advent Prompt Pwn
adventcybersecurity.com
DeepTeam
trydeepteam.comSide by side
| Figure | Advent Prompt Pwn | DeepTeam |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | — |
| API | Yes | Yes |
| Self-hosted | — | Yes |
| Plans published | 0 | 1 |
| Spec score · AI Red Teaming Tools | 8.0 (#6) | 7.8 (#10) |
| License | The project’s GitHub page identifies the source as Apache License 2.0. | The site describes DeepTeam as an open-source LLM red teaming framework licensed under Apache 2.0. |
| Maker | The product is maintained by Advent Cybersecurity LLC; the maker page identifies Jashid Sany as its founder. | The site credits Confident AI Inc. and says DeepTeam is brought to users by the team behind DeepEval. |
| Purpose | Advent Prompt Pwn is a typed Python toolkit for authorized adversarial testing of language models and AI applications. | DeepTeam is an open-source framework for red teaming LLM systems and finding risks and vulnerabilities. |
Plans
In their own words
Advent Prompt Pwn
Advent Prompt Pwn has no write-up on Specifiction yet; its figures are above.
DeepTeam
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.