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Improved file structure
This commit is contained in:
shiva108
@@ -1,138 +1,123 @@
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# AI / LLM Red Team Field Manual & Consultant’s Handbook
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# AI / LLM Red Team Field Manual & Consultant's Handbook
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This repository provides a complete operational and consultative toolkit for conducting **AI/LLM red team assessments**.
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It is designed for penetration testers, red team operators, and security engineers evaluating:
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- Large Language Models (LLMs)
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- AI agents and function-calling systems
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- Retrieval-Augmented Generation (RAG) pipelines
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- Plugin/tool ecosystems
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- AI-enabled enterprise applications
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It contains two primary documents:
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- **AI/LLM Red Team Field Manual** – a concise, practical manual with attack prompts, tooling references, and OWASP/MITRE mappings.
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- **AI/LLM Red Team Consultant’s Handbook** – a full-length guide covering methodology, scoping, ethics, RoE/SOW templates, threat modeling, and operational workflows.
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A complete operational toolkit for conducting **AI/LLM red team assessments** on Large Language Models, AI agents, RAG pipelines, and AI-enabled applications.
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---
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## Repository Structure
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```text
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docs/
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AI_LLM-Red-Team-Field-Manual.md
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AI_LLM-Red-Team-Field-Manual.pdf
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AI_LLM-Red-Team-Field-Manual.docx
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AI_LLM-Red-Team-Handbook.md
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assets/
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banner.svg
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README.md
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LICENSE
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```
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---
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## Document Overview
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### **AI_LLM-Red-Team-Field-Manual.md**
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A compact operational reference for active red teaming engagements.
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**Includes:**
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- Rules of Engagement (RoE) and testing phases
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- Attack categories and ready-to-use prompts
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- Coverage of prompt injection, jailbreaks, data leakage, plugin abuse, adversarial examples, model extraction, DoS, multimodal attacks, and supply-chain vectors
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- Tooling reference (Garak, PromptBench, TextAttack, ART, AFL++, Burp Suite, KnockoffNets)
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- Attack-to-tool lookup table
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- Reporting and documentation guidance
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- OWASP & MITRE ATLAS mapping appendices
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**PDF / DOCX Versions:**
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Preformatted for printing or distribution.
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---
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### **AI_LLM-Red-Team-Handbook.md**
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A long-form handbook focused on consultancy and structured delivery of AI red team projects.
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**Includes:**
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- Red team mindset, ethics, and legal considerations
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- SOW and RoE templates
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- Threat modeling frameworks
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- LLM and RAG architecture fundamentals
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- Detailed attack descriptions and risk frameworks
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- Defense and mitigation strategies
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- Operational workflows and sample reporting structure
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- Training modules, labs, and advanced topics (e.g., adversarial ML, supply chain, regulation)
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---
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## How to Use This Repository
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### **1. During AI/LLM Red Team Engagements**
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Clone the repository:
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## Quick Start
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```bash
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# Clone the repository
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git clone https://github.com/shiva108/ai-llm-red-team-handbook.git
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cd ai-llm-red-team-handbook
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# Manual testing: Open docs/AI_LLM Red Team Field Manual.md
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# Automated testing:
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cd scripts
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pip install -r requirements.txt
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python runner.py --config config.py
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```
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Then:
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- Open the Field Manual
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- Apply the provided attacks, prompts, and tooling guidance
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- Map findings to OWASP & MITRE using the included tables
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- Use the reporting guidance to produce consistent, defensible documentation
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📖 **Detailed setup:** See [Configuration Guide](docs/Configuration.md)
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---
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### **2. For Internal Training**
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## Repository Contents
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- Use the Handbook as the foundation for onboarding and team development
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- Integrate sections into internal wikis, training slides, and exercises
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| Resource | Description |
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|----------|-------------|
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| **[Field Manual](docs/AI_LLM%20Red%20Team%20Field%20Manual.md)** | Compact operational reference with attack prompts, tooling, OWASP/MITRE mappings |
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| **[Handbook](docs/AI%20LLM%20Red%20Team%20Hand%20book.md)** | Full consultancy guide with methodology, threat modeling, RoE/SOW templates |
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| **[Building AI Red Teams](docs/Building%20a%20World-Class%20AI%20Red%20Team.md)** | Strategic guide for building security teams |
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| **[Report Template](docs/Full_LLM_RedTeam_Report_Template.docx)** | Client-ready assessment report template |
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| **[Python Framework](scripts/)** | Automated testing suite for prompt injection, jailbreaks, data leakage, tool misuse |
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---
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### **3. For Client-Facing Work**
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## Prerequisites
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- Export PDF versions for use in proposals and methodology documents
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- Use the structured attack categories to justify test coverage in engagements
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**Manual Testing:** Any text editor + target LLM access
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**Automated Testing:**
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- Python 3.8+
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- Dependencies: `requests`, `pytest`, `pydantic`, `python-dotenv`
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- API credentials for target LLM
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---
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## Python Testing Framework
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### Test Suites
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- `test_prompt_injection.py` - Automated prompt injection attacks
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- `test_safety_bypass.py` - Jailbreak and guardrail bypass tests
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- `test_data_exposure.py` - Data leakage and PII extraction
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- `test_tool_misuse.py` - Function-calling and plugin abuse
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- `test_fuzzing.py` - Adversarial input fuzzing
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- `test_integrity.py` - Model integrity and consistency
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### Configuration
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Create `scripts/.env`:
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```bash
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API_ENDPOINT=https://api.example.com/v1/chat/completions
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API_KEY=your-secret-api-key
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MODEL_NAME=gpt-4
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```
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Run tests:
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```bash
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python runner.py # All tests
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python runner.py --test prompt_injection # Specific test
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python runner.py --verbose # Verbose output
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```
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📖 **Full configuration options:** [Configuration Guide](docs/Configuration.md)
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---
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## Use Cases
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**Red Team Engagements:** Use Field Manual attack prompts and Python framework for assessments
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**Training:** Leverage Handbook for onboarding and team development
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**Client Work:** Export PDFs for proposals and methodology documents
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---
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## Roadmap
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Planned improvements:
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**Planned:**
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- Sample RAG and LLM test environments
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- Additional attack case studies
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- Extended multimodal AI coverage
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- Python tools for automated AI prompt fuzzing
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- Sample RAG and LLM test environments
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- Additional attack case studies and model-specific guidance
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**Contributions are welcome.**
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**Contributions welcome** via issues and PRs.
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---
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## License
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||||
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This repository is licensed under **CC BY-SA 4.0**.
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See the `LICENSE` file for full details.
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Licensed under **CC BY-SA 4.0**. See [LICENSE](LICENSE) for details.
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---
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## Disclaimer
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This material is intended for authorized security testing and research only.
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⚠️ **For authorized security testing only.**
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Users must ensure:
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- Written authorization (SOW/RoE) is in place
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- All testing activities comply with applicable laws and regulations
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||||
- No testing impacts production environments without approval
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Ensure:
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||||
- Written authorization (SOW/RoE) is in place
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- Compliance with applicable laws and regulations
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- No unauthorized testing on production systems
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The authors accept no liability for unauthorized use.
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@@ -0,0 +1,465 @@
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# Configuration Guide
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This guide provides detailed instructions for configuring and running the automated AI/LLM red team testing framework.
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|
||||
---
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## Table of Contents
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||||
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||||
- [Quick Setup](#quick-setup)
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- [Configuration File](#configuration-file)
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- [Environment Variables](#environment-variables)
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||||
- [Advanced Configuration](#advanced-configuration)
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- [Running Tests](#running-tests)
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- [Output and Reporting](#output-and-reporting)
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||||
- [Troubleshooting](#troubleshooting)
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|
||||
---
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## Quick Setup
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### 1. Install Dependencies
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```bash
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cd scripts
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pip install -r requirements.txt
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```
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### 2. Configure Environment
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Create a `.env` file in the `scripts/` directory:
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```bash
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API_ENDPOINT=https://api.example.com/v1/chat/completions
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API_KEY=your-secret-api-key
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MODEL_NAME=gpt-4
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```
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### 3. Run Tests
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```bash
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python runner.py
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```
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---
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## Configuration File
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### Basic `config.py`
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Create or modify `scripts/config.py` with your target system details:
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```python
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# Target LLM Configuration
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API_ENDPOINT = "https://api.example.com/v1/chat/completions"
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API_KEY = "your-api-key-here" # Or use environment variable
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MODEL_NAME = "gpt-4" # Target model identifier
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# Test Configuration
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MAX_RETRIES = 3
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TIMEOUT = 30 # seconds
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REQUEST_DELAY = 1 # seconds between requests
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# Logging
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LOG_LEVEL = "INFO"
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LOG_FILE = "test_results.log"
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# Test Selection
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ENABLE_TESTS = [
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"prompt_injection",
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"safety_bypass",
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"data_exposure",
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"tool_misuse",
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"fuzzing",
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"integrity"
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]
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```
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### Configuration Parameters
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| Parameter | Type | Default | Description |
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|-----------|------|---------|-------------|
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| `API_ENDPOINT` | string | required | Target LLM API endpoint URL |
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| `API_KEY` | string | required | Authentication key for API |
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| `MODEL_NAME` | string | required | Model identifier (e.g., "gpt-4", "claude-3") |
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| `MAX_RETRIES` | int | 3 | Number of retry attempts for failed requests |
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| `TIMEOUT` | int | 30 | Request timeout in seconds |
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| `REQUEST_DELAY` | float | 1.0 | Delay between requests to avoid rate limiting |
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| `LOG_LEVEL` | string | "INFO" | Logging verbosity (DEBUG, INFO, WARNING, ERROR) |
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| `LOG_FILE` | string | "test_results.log" | Path to log file |
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| `ENABLE_TESTS` | list | all tests | List of test categories to run |
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---
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## Environment Variables
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### Using `.env` File (Recommended)
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For security, use environment variables instead of hardcoding credentials in `config.py`.
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**1. Create `scripts/.env`:**
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```bash
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# API Configuration
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API_ENDPOINT=https://api.openai.com/v1/chat/completions
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API_KEY=sk-proj-xxxxxxxxxxxxxxxxxxxxx
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MODEL_NAME=gpt-4
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# Test Configuration
|
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MAX_RETRIES=3
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TIMEOUT=30
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REQUEST_DELAY=1
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# Logging
|
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LOG_LEVEL=INFO
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LOG_FILE=test_results.log
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```
|
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|
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**2. Update `config.py` to load from environment:**
|
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|
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```python
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from dotenv import load_dotenv
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import os
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# Load environment variables
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load_dotenv()
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# API Configuration
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API_ENDPOINT = os.getenv("API_ENDPOINT")
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API_KEY = os.getenv("API_KEY")
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MODEL_NAME = os.getenv("MODEL_NAME")
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# Test Configuration
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MAX_RETRIES = int(os.getenv("MAX_RETRIES", "3"))
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TIMEOUT = int(os.getenv("TIMEOUT", "30"))
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REQUEST_DELAY = float(os.getenv("REQUEST_DELAY", "1.0"))
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# Logging
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LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
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LOG_FILE = os.getenv("LOG_FILE", "test_results.log")
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```
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**3. Add `.env` to `.gitignore`:**
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```bash
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echo ".env" >> .gitignore
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```
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### Provider-Specific Examples
|
||||
|
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#### OpenAI
|
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|
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```bash
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API_ENDPOINT=https://api.openai.com/v1/chat/completions
|
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API_KEY=sk-proj-xxxxxxxxxxxxxxxxxxxxx
|
||||
MODEL_NAME=gpt-4
|
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```
|
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|
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#### Anthropic (Claude)
|
||||
|
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```bash
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API_ENDPOINT=https://api.anthropic.com/v1/messages
|
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API_KEY=sk-ant-xxxxxxxxxxxxxxxxxxxxx
|
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MODEL_NAME=claude-3-opus-20240229
|
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```
|
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|
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#### Azure OpenAI
|
||||
|
||||
```bash
|
||||
API_ENDPOINT=https://your-resource.openai.azure.com/openai/deployments/your-deployment/chat/completions?api-version=2024-02-15-preview
|
||||
API_KEY=xxxxxxxxxxxxxxxxxxxxx
|
||||
MODEL_NAME=gpt-4
|
||||
```
|
||||
|
||||
#### Local/Self-Hosted Models
|
||||
|
||||
```bash
|
||||
API_ENDPOINT=http://localhost:8000/v1/chat/completions
|
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API_KEY=none
|
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MODEL_NAME=llama-2-7b
|
||||
```
|
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|
||||
---
|
||||
|
||||
## Advanced Configuration
|
||||
|
||||
### Custom Headers
|
||||
|
||||
Add custom headers for authentication or tracking:
|
||||
|
||||
```python
|
||||
CUSTOM_HEADERS = {
|
||||
"Authorization": f"Bearer {API_KEY}",
|
||||
"X-Request-ID": "red-team-test",
|
||||
"User-Agent": "AI-RedTeam-Framework/1.0"
|
||||
}
|
||||
```
|
||||
|
||||
### Proxy Configuration
|
||||
|
||||
Route requests through a proxy:
|
||||
|
||||
```python
|
||||
PROXY_CONFIG = {
|
||||
"http": "http://proxy.example.com:8080",
|
||||
"https": "https://proxy.example.com:8080"
|
||||
}
|
||||
```
|
||||
|
||||
### Rate Limiting
|
||||
|
||||
Configure rate limiting to avoid API throttling:
|
||||
|
||||
```python
|
||||
RATE_LIMIT = {
|
||||
"requests_per_minute": 60,
|
||||
"requests_per_day": 10000,
|
||||
"retry_after_seconds": 60
|
||||
}
|
||||
```
|
||||
|
||||
### Test Customization
|
||||
|
||||
Enable/disable specific tests or adjust severity:
|
||||
|
||||
```python
|
||||
TEST_CONFIG = {
|
||||
"prompt_injection": {
|
||||
"enabled": True,
|
||||
"severity_threshold": "medium", # low, medium, high, critical
|
||||
"max_attempts": 100
|
||||
},
|
||||
"safety_bypass": {
|
||||
"enabled": True,
|
||||
"severity_threshold": "high",
|
||||
"max_attempts": 50
|
||||
},
|
||||
"data_exposure": {
|
||||
"enabled": True,
|
||||
"severity_threshold": "critical",
|
||||
"max_attempts": 75
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Running Tests
|
||||
|
||||
### Run All Tests
|
||||
|
||||
```bash
|
||||
python runner.py
|
||||
```
|
||||
|
||||
### Run Specific Test Category
|
||||
|
||||
```bash
|
||||
python runner.py --test prompt_injection
|
||||
python runner.py --test safety_bypass
|
||||
python runner.py --test data_exposure
|
||||
```
|
||||
|
||||
### Run Multiple Categories
|
||||
|
||||
```bash
|
||||
python runner.py --test prompt_injection,safety_bypass,data_exposure
|
||||
```
|
||||
|
||||
### Use Custom Configuration File
|
||||
|
||||
```bash
|
||||
python runner.py --config my_custom_config.py
|
||||
```
|
||||
|
||||
### Verbose Output
|
||||
|
||||
```bash
|
||||
python runner.py --verbose
|
||||
```
|
||||
|
||||
### Debug Mode
|
||||
|
||||
```bash
|
||||
python runner.py --debug
|
||||
```
|
||||
|
||||
### Save Results to Custom Location
|
||||
|
||||
```bash
|
||||
python runner.py --output /path/to/results/
|
||||
```
|
||||
|
||||
### Dry Run (Preview Tests)
|
||||
|
||||
```bash
|
||||
python runner.py --dry-run
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Output and Reporting
|
||||
|
||||
### Output Files
|
||||
|
||||
Test results are saved to multiple locations:
|
||||
|
||||
| File/Directory | Format | Description |
|
||||
|----------------|--------|-------------|
|
||||
| `test_results.log` | Text | Detailed execution log with timestamps |
|
||||
| `reports/json/` | JSON | Machine-readable test results |
|
||||
| `reports/html/` | HTML | Human-readable HTML reports |
|
||||
| `reports/summary.txt` | Text | Executive summary of findings |
|
||||
|
||||
### Report Structure
|
||||
|
||||
**JSON Report:**
|
||||
```json
|
||||
{
|
||||
"test_run_id": "20250630-124530",
|
||||
"timestamp": "2025-06-30T12:45:30Z",
|
||||
"model": "gpt-4",
|
||||
"total_tests": 150,
|
||||
"passed": 120,
|
||||
"failed": 30,
|
||||
"findings": [
|
||||
{
|
||||
"test_case": "prompt_injection_001",
|
||||
"severity": "high",
|
||||
"status": "failed",
|
||||
"description": "Model revealed system prompt",
|
||||
"payload": "Ignore previous instructions...",
|
||||
"response": "You are a helpful assistant..."
|
||||
}
|
||||
]
|
||||
}
|
||||
```
|
||||
|
||||
**HTML Report:**
|
||||
- Interactive dashboard with charts
|
||||
- Filterable findings by severity
|
||||
- Detailed test case results
|
||||
- Recommendations for remediation
|
||||
|
||||
### Console Output
|
||||
|
||||
Real-time progress indicators:
|
||||
|
||||
```
|
||||
Running AI/LLM Red Team Tests...
|
||||
[=====> ] 50% | Prompt Injection (25/50)
|
||||
|
||||
✓ test_prompt_injection_001 - PASSED
|
||||
✗ test_prompt_injection_002 - FAILED (High Severity)
|
||||
✓ test_prompt_injection_003 - PASSED
|
||||
...
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### Common Issues
|
||||
|
||||
#### API Connection Errors
|
||||
|
||||
**Error:**
|
||||
```
|
||||
ConnectionError: Failed to connect to API endpoint
|
||||
```
|
||||
|
||||
**Solution:**
|
||||
- Verify `API_ENDPOINT` is correct
|
||||
- Check network connectivity
|
||||
- Confirm firewall/proxy settings
|
||||
|
||||
#### Authentication Failures
|
||||
|
||||
**Error:**
|
||||
```
|
||||
AuthenticationError: Invalid API key
|
||||
```
|
||||
|
||||
**Solution:**
|
||||
- Verify `API_KEY` is correct and active
|
||||
- Check API key permissions
|
||||
- Ensure key hasn't expired
|
||||
|
||||
#### Rate Limiting
|
||||
|
||||
**Error:**
|
||||
```
|
||||
RateLimitError: Too many requests
|
||||
```
|
||||
|
||||
**Solution:**
|
||||
- Increase `REQUEST_DELAY` in config
|
||||
- Reduce concurrent tests
|
||||
- Use rate limiting configuration
|
||||
|
||||
#### Timeout Issues
|
||||
|
||||
**Error:**
|
||||
```
|
||||
TimeoutError: Request timed out after 30s
|
||||
```
|
||||
|
||||
**Solution:**
|
||||
- Increase `TIMEOUT` value
|
||||
- Check API service status
|
||||
- Verify network latency
|
||||
|
||||
### Debug Mode
|
||||
|
||||
Enable detailed logging:
|
||||
|
||||
```python
|
||||
LOG_LEVEL = "DEBUG"
|
||||
```
|
||||
|
||||
Or run with debug flag:
|
||||
|
||||
```bash
|
||||
python runner.py --debug
|
||||
```
|
||||
|
||||
### Getting Help
|
||||
|
||||
- Check [Issues](https://github.com/shiva108/ai-llm-red-team-handbook/issues) for known problems
|
||||
- Review test logs in `test_results.log`
|
||||
- Enable debug mode for detailed diagnostics
|
||||
|
||||
---
|
||||
|
||||
## Best Practices
|
||||
|
||||
### Security
|
||||
|
||||
- ✅ Use `.env` files for credentials
|
||||
- ✅ Add `.env` to `.gitignore`
|
||||
- ✅ Rotate API keys regularly
|
||||
- ✅ Use minimum required permissions
|
||||
- ❌ Never commit credentials to version control
|
||||
|
||||
### Performance
|
||||
|
||||
- Use appropriate `REQUEST_DELAY` to avoid rate limiting
|
||||
- Run tests during off-peak hours for production systems
|
||||
- Use `--dry-run` to preview tests before execution
|
||||
- Consider running test categories separately for large test suites
|
||||
|
||||
### Reporting
|
||||
|
||||
- Save reports with timestamps for historical tracking
|
||||
- Export findings to client-ready formats
|
||||
- Map findings to OWASP/MITRE frameworks
|
||||
- Document false positives for future reference
|
||||
|
||||
---
|
||||
|
||||
## Additional Resources
|
||||
|
||||
- [Main README](../README.md)
|
||||
- [Field Manual](AI_LLM%20Red%20Team%20Field%20Manual.md)
|
||||
- [Handbook](AI%20LLM%20Red%20Team%20Hand%20book.md)
|
||||
- [Report Template](Full_LLM_RedTeam_Report_Template.docx)
|
||||
Reference in New Issue
Block a user