confident-ai/deepteam

What it solves

DeepTeam provides a structured way to perform red teaming (adversarial penetration testing) on LLM systems, including AI agents, RAG pipelines, and chatbots. It helps developers identify security vulnerabilities, safety risks, and ethical biases before they reach production, and provides tools to block these threats in real-time.

How it works

DeepTeam uses a model_callback to wrap an LLM application and then subjects it to dynamically generated adversarial attacks. It leverages LLM-as-a-Judge metrics to evaluate the responses and produce binary pass/fail scores. The framework supports:

• Vulnerability Probes: Over 50 pre-defined vulnerabilities across categories like Data Privacy, Responsible AI, Security (e.g., SQL injection), Safety, and Agentic risks.
• Adversarial Attacks: Over 20 research-backed methods, including single-turn attacks (Prompt Injection, Base64 encoding, Roleplay) and multi-turn conversational attacks (Crescendo and Tree Jailbreaking).
• Safety Frameworks: Out-of-the-box mapping to industry standards like OWASP Top 10 for LLMs/Agents, NIST AI RMF, and MITRE ATLAS.
• Guardrails: Real-time binary classifiers that monitor inputs and outputs to block breached content in production.

Who it’s for

It is designed for AI developers and security engineers who need to stress-test their LLM applications for safety and security compliance, particularly those building complex agentic systems or RAG pipelines.

Highlights

• Extensive Attack Library: Includes a wide array of SOTA jailbreaking and obfuscation techniques.
• Agent-Specific Testing: Dedicated vulnerabilities for agentic risks like goal theft, recursive hijacking, and tool orchestration abuse.
• Framework Integration: Automatically aligns testing with global AI safety standards (OWASP, NIST, MITRE).
• Local Execution: Runs locally on the user's machine and is built on the DeepEval framework.