AI Red Teaming Framework - AI Security Red Team Testing

Basic Information

• Domain: AI Red Teaming
• Main Frameworks and Tools: PyRIT (Microsoft), Garak (NVIDIA), Promptfoo, DeepTeam
• Related Standards: OWASP, NIST AI RMF, MITRE ATLAS, EU AI Act
• Type: AI Security Assessment Methodology and Toolset
• Status: Rapidly evolving field, compliance required by August 2026 under EU AI Act

Overview

AI Red Teaming is a systematic method for evaluating the security of AI systems by simulating adversarial attacks. With the widespread deployment of LLMs and agentic AI systems, AI Red Teaming has evolved from academic research to a necessary practice in production environments. Key techniques include prompt injection, jailbreaking attacks, data extraction, model inversion, and data poisoning. OWASP, NIST, MITRE ATLAS, and the EU AI Act provide authoritative structured frameworks for AI Red Teaming.

Main Open Source Tools

PyRIT (Microsoft)

• Positioning: Red Team Automation Framework
• Features: Multi-turn conversation orchestration, complex attack chains, audio/image/math transformers, Azure Content Safety scoring engine
• Integration: Azure AI Foundry
• Best Use Case: Programmatic multi-turn attack orchestration, proprietary/fine-tuned model testing

Garak (NVIDIA)

• Positioning: LLM Vulnerability Scanner
• Features: 100+ attack modules, 20k+ prompts/runs, AVID community sharing
• Best Use Case: Batch automated vulnerability scanning

Promptfoo

• Positioning: Evaluation + Red Teaming Integration
• Features: 50+ vulnerability types, adaptive red teaming, MCP testing, compliance mapping
• Status: Acquired by OpenAI in March 2026, remains MIT open source
• Best Use Case: Developer-friendly red team testing and CI/CD integration

DeepTeam

• Positioning: Open Source LLM Red Team Framework
• Features: 40+ vulnerability classes, 10+ adversarial attack strategies, OWASP/NIST alignment
• Best Use Case: Comprehensive red team testing for local execution

Attack Technique Classification

• Prompt Injection: Direct injection, indirect injection
• Jailbreaking Attacks: Role-playing, DAN, multilingual jailbreaking
• Data Extraction: Training data extraction, system prompt leakage
• Model Inversion: Inferring training data characteristics
• Data Poisoning: Contaminating training or fine-tuning data
• Tool Misuse: Leveraging agent tools to perform unintended operations
• Multimodal Attacks: Attacks via images, audio, etc.

Compliance Frameworks

Target Users

• AI Security Red Team Members
• AI Compliance Audit Teams
• CISOs and Security Leaders
• AI Product and Engineering Teams
• Regulatory Compliance Departments

Industry Trends

• AI Red Teaming becomes a standard process for AI product releases by 2026
• EU AI Act requires full compliance by August 2026
• Agentic AI Security emerges as a new focus
• Rapid growth in MCP security testing demand
• Adaptive and multi-turn attacks become mainstream methods

Relationship with OpenClaw Ecosystem

AI Red Teaming is a core component of the OpenClaw ecosystem's security assurance system. OpenClaw should establish a systematic red team testing process, conducting comprehensive security assessments before each new agent release or model update. Recommended tool combination:

1. Daily Development: Promptfoo (CI/CD integration, developer-friendly)
2. Comprehensive Scanning: Garak (100+ attack vector coverage)
3. In-depth Testing: PyRIT (complex multi-turn attack orchestration)
4. Compliance Audit: Alignment with OWASP LLM Top 10 + Agentic Top 10

It is recommended to correlate red team testing results with the configuration of protection tools like Guardrails AI and NeMo Guardrails, forming a closed-loop security process of "test-discover-protect-verify."