Pen-test your LLM without leaving your terminal

What it does

DeepTeam is an open-source Python framework for red teaming large language models and AI agents. It simulates adversarial attacks—jailbreaking, prompt injection, multi-turn exploitation, and encoding obfuscation—to probe for vulnerabilities such as PII leakage, SQL injection, bias, and toxic outputs. Everything runs locally using an LLM-as-a-Judge scoring system that returns binary pass/fail results with reasoning, and it includes guardrails intended to block these issues in production.

The interesting bit

Instead of treating red teaming as a manual, one-off audit, DeepTeam packages more than 50 vulnerability categories and 20+ research-backed attack methods into repeatable, automated tests. It is built on top of the DeepEval evaluation framework, which means the same infrastructure used for model benchmarking can now be turned against the model as an adversary.

Key highlights

• Covers six vulnerability domains: Data Privacy, Responsible AI, Security, Safety, Business, and Agentic risks, plus support for custom criteria.
• Attack library includes single-turn and multi-turn conversational probes, from classic prompt injection and roleplay to ROT13, Base64, leetspeak, and “adversarial poetry.”
• Runs fully offline with any LLM provider you plug in; scoring and reasoning happen on your machine.
• Includes guardrails to mitigate identified risks in production environments.
• Optional integration with the Confident AI platform for centralized risk dashboards and production monitoring.

Verdict

Worth exploring if you need systematic, local adversarial testing for LLM apps, agents, or RAG pipelines. Probably overkill if your use case is narrow enough to be covered by a few manual prompt checks.