diff --git a/README.md b/README.md
index 4fad193..e7d13ce 100644
--- a/README.md
+++ b/README.md
@@ -1,138 +1,123 @@
-# AI / LLM Red Team Field Manual & Consultant’s Handbook
+# AI / LLM Red Team Field Manual & Consultant's Handbook
 
 ![Repository Banner](assets/banner.svg)
 
-This repository provides a complete operational and consultative toolkit for conducting **AI/LLM red team assessments**.  
-It is designed for penetration testers, red team operators, and security engineers evaluating:
+![License](https://img.shields.io/badge/License-CC%20BY--SA%204.0-lightgrey.svg)
+![Python](https://img.shields.io/badge/Python-3.8%2B-blue.svg)
+![Contributions Welcome](https://img.shields.io/badge/Contributions-Welcome-brightgreen.svg)
+![Last Updated](https://img.shields.io/badge/Last%20Updated-November%202025-orange.svg)
 
-- Large Language Models (LLMs)  
-- AI agents and function-calling systems  
-- Retrieval-Augmented Generation (RAG) pipelines  
-- Plugin/tool ecosystems  
-- AI-enabled enterprise applications  
-
-It contains two primary documents:
-
-- **AI/LLM Red Team Field Manual** – a concise, practical manual with attack prompts, tooling references, and OWASP/MITRE mappings.  
-- **AI/LLM Red Team Consultant’s Handbook** – a full-length guide covering methodology, scoping, ethics, RoE/SOW templates, threat modeling, and operational workflows.
+A complete operational toolkit for conducting **AI/LLM red team assessments** on Large Language Models, AI agents, RAG pipelines, and AI-enabled applications.
 
 ---
 
-## Repository Structure
-
-```text
-docs/
-  AI_LLM-Red-Team-Field-Manual.md
-  AI_LLM-Red-Team-Field-Manual.pdf
-  AI_LLM-Red-Team-Field-Manual.docx
-  AI_LLM-Red-Team-Handbook.md
-assets/
-  banner.svg
-README.md
-LICENSE
-```
-
----
-
-## Document Overview
-
-### **AI_LLM-Red-Team-Field-Manual.md**
-
-A compact operational reference for active red teaming engagements.
-
-**Includes:**
-
-- Rules of Engagement (RoE) and testing phases  
-- Attack categories and ready-to-use prompts  
-- Coverage of prompt injection, jailbreaks, data leakage, plugin abuse, adversarial examples, model extraction, DoS, multimodal attacks, and supply-chain vectors  
-- Tooling reference (Garak, PromptBench, TextAttack, ART, AFL++, Burp Suite, KnockoffNets)  
-- Attack-to-tool lookup table  
-- Reporting and documentation guidance  
-- OWASP & MITRE ATLAS mapping appendices  
-
-**PDF / DOCX Versions:**  
-Preformatted for printing or distribution.
-
----
-
-### **AI_LLM-Red-Team-Handbook.md**
-
-A long-form handbook focused on consultancy and structured delivery of AI red team projects.
-
-**Includes:**
-
-- Red team mindset, ethics, and legal considerations  
-- SOW and RoE templates  
-- Threat modeling frameworks  
-- LLM and RAG architecture fundamentals  
-- Detailed attack descriptions and risk frameworks  
-- Defense and mitigation strategies  
-- Operational workflows and sample reporting structure  
-- Training modules, labs, and advanced topics (e.g., adversarial ML, supply chain, regulation)
-
----
-
-## How to Use This Repository
-
-### **1. During AI/LLM Red Team Engagements**
-
-Clone the repository:
+## Quick Start
 
 ```bash
+# Clone the repository
 git clone https://github.com/shiva108/ai-llm-red-team-handbook.git
 cd ai-llm-red-team-handbook
+
+# Manual testing: Open docs/AI_LLM Red Team Field Manual.md
+
+# Automated testing:
+cd scripts
+pip install -r requirements.txt
+python runner.py --config config.py
 ```
 
-Then:
-
-- Open the Field Manual  
-- Apply the provided attacks, prompts, and tooling guidance  
-- Map findings to OWASP & MITRE using the included tables  
-- Use the reporting guidance to produce consistent, defensible documentation  
+📖 **Detailed setup:** See [Configuration Guide](docs/Configuration.md)
 
 ---
 
-### **2. For Internal Training**
+## Repository Contents
 
-- Use the Handbook as the foundation for onboarding and team development  
-- Integrate sections into internal wikis, training slides, and exercises  
+| Resource | Description |
+|----------|-------------|
+| **[Field Manual](docs/AI_LLM%20Red%20Team%20Field%20Manual.md)** | Compact operational reference with attack prompts, tooling, OWASP/MITRE mappings |
+| **[Handbook](docs/AI%20LLM%20Red%20Team%20Hand%20book.md)** | Full consultancy guide with methodology, threat modeling, RoE/SOW templates |
+| **[Building AI Red Teams](docs/Building%20a%20World-Class%20AI%20Red%20Team.md)** | Strategic guide for building security teams |
+| **[Report Template](docs/Full_LLM_RedTeam_Report_Template.docx)** | Client-ready assessment report template |
+| **[Python Framework](scripts/)** | Automated testing suite for prompt injection, jailbreaks, data leakage, tool misuse |
 
 ---
 
-### **3. For Client-Facing Work**
+## Prerequisites
 
-- Export PDF versions for use in proposals and methodology documents  
-- Use the structured attack categories to justify test coverage in engagements  
+**Manual Testing:** Any text editor + target LLM access
+
+**Automated Testing:**
+- Python 3.8+
+- Dependencies: `requests`, `pytest`, `pydantic`, `python-dotenv`
+- API credentials for target LLM
+
+---
+
+## Python Testing Framework
+
+### Test Suites
+
+- `test_prompt_injection.py` - Automated prompt injection attacks
+- `test_safety_bypass.py` - Jailbreak and guardrail bypass tests
+- `test_data_exposure.py` - Data leakage and PII extraction
+- `test_tool_misuse.py` - Function-calling and plugin abuse
+- `test_fuzzing.py` - Adversarial input fuzzing
+- `test_integrity.py` - Model integrity and consistency
+
+### Configuration
+
+Create `scripts/.env`:
+```bash
+API_ENDPOINT=https://api.example.com/v1/chat/completions
+API_KEY=your-secret-api-key
+MODEL_NAME=gpt-4
+```
+
+Run tests:
+```bash
+python runner.py                           # All tests
+python runner.py --test prompt_injection   # Specific test
+python runner.py --verbose                 # Verbose output
+```
+
+📖 **Full configuration options:** [Configuration Guide](docs/Configuration.md)
+
+---
+
+## Use Cases
+
+**Red Team Engagements:** Use Field Manual attack prompts and Python framework for assessments
+
+**Training:** Leverage Handbook for onboarding and team development
+
+**Client Work:** Export PDFs for proposals and methodology documents
 
 ---
 
 ## Roadmap
 
-Planned improvements:
+**Planned:**
+- Sample RAG and LLM test environments
+- Additional attack case studies
+- Extended multimodal AI coverage
 
-- Python tools for automated AI prompt fuzzing  
-- Sample RAG and LLM test environments  
-- Additional attack case studies and model-specific guidance  
-
-**Contributions are welcome.**
+**Contributions welcome** via issues and PRs.
 
 ---
 
 ## License
 
-This repository is licensed under **CC BY-SA 4.0**.  
-See the `LICENSE` file for full details.
+Licensed under **CC BY-SA 4.0**. See [LICENSE](LICENSE) for details.
 
 ---
 
 ## Disclaimer
 
-This material is intended for authorized security testing and research only.
+⚠️ **For authorized security testing only.**
 
-Users must ensure:
-
-- Written authorization (SOW/RoE) is in place  
-- All testing activities comply with applicable laws and regulations  
-- No testing impacts production environments without approval  
+Ensure:
+- Written authorization (SOW/RoE) is in place
+- Compliance with applicable laws and regulations
+- No unauthorized testing on production systems
 
 The authors accept no liability for unauthorized use.
diff --git a/docs/Configuration.md b/docs/Configuration.md
new file mode 100644
index 0000000..21d890c
--- /dev/null
+++ b/docs/Configuration.md
@@ -0,0 +1,465 @@
+# Configuration Guide
+
+This guide provides detailed instructions for configuring and running the automated AI/LLM red team testing framework.
+
+---
+
+## Table of Contents
+
+- [Quick Setup](#quick-setup)
+- [Configuration File](#configuration-file)
+- [Environment Variables](#environment-variables)
+- [Advanced Configuration](#advanced-configuration)
+- [Running Tests](#running-tests)
+- [Output and Reporting](#output-and-reporting)
+- [Troubleshooting](#troubleshooting)
+
+---
+
+## Quick Setup
+
+### 1. Install Dependencies
+
+```bash
+cd scripts
+pip install -r requirements.txt
+```
+
+### 2. Configure Environment
+
+Create a `.env` file in the `scripts/` directory:
+
+```bash
+API_ENDPOINT=https://api.example.com/v1/chat/completions
+API_KEY=your-secret-api-key
+MODEL_NAME=gpt-4
+```
+
+### 3. Run Tests
+
+```bash
+python runner.py
+```
+
+---
+
+## Configuration File
+
+### Basic `config.py`
+
+Create or modify `scripts/config.py` with your target system details:
+
+```python
+# Target LLM Configuration
+API_ENDPOINT = "https://api.example.com/v1/chat/completions"
+API_KEY = "your-api-key-here"  # Or use environment variable
+MODEL_NAME = "gpt-4"  # Target model identifier
+
+# Test Configuration
+MAX_RETRIES = 3
+TIMEOUT = 30  # seconds
+REQUEST_DELAY = 1  # seconds between requests
+
+# Logging
+LOG_LEVEL = "INFO"
+LOG_FILE = "test_results.log"
+
+# Test Selection
+ENABLE_TESTS = [
+    "prompt_injection",
+    "safety_bypass",
+    "data_exposure",
+    "tool_misuse",
+    "fuzzing",
+    "integrity"
+]
+```
+
+### Configuration Parameters
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `API_ENDPOINT` | string | required | Target LLM API endpoint URL |
+| `API_KEY` | string | required | Authentication key for API |
+| `MODEL_NAME` | string | required | Model identifier (e.g., "gpt-4", "claude-3") |
+| `MAX_RETRIES` | int | 3 | Number of retry attempts for failed requests |
+| `TIMEOUT` | int | 30 | Request timeout in seconds |
+| `REQUEST_DELAY` | float | 1.0 | Delay between requests to avoid rate limiting |
+| `LOG_LEVEL` | string | "INFO" | Logging verbosity (DEBUG, INFO, WARNING, ERROR) |
+| `LOG_FILE` | string | "test_results.log" | Path to log file |
+| `ENABLE_TESTS` | list | all tests | List of test categories to run |
+
+---
+
+## Environment Variables
+
+### Using `.env` File (Recommended)
+
+For security, use environment variables instead of hardcoding credentials in `config.py`.
+
+**1. Create `scripts/.env`:**
+
+```bash
+# API Configuration
+API_ENDPOINT=https://api.openai.com/v1/chat/completions
+API_KEY=sk-proj-xxxxxxxxxxxxxxxxxxxxx
+MODEL_NAME=gpt-4
+
+# Test Configuration
+MAX_RETRIES=3
+TIMEOUT=30
+REQUEST_DELAY=1
+
+# Logging
+LOG_LEVEL=INFO
+LOG_FILE=test_results.log
+```
+
+**2. Update `config.py` to load from environment:**
+
+```python
+from dotenv import load_dotenv
+import os
+
+# Load environment variables
+load_dotenv()
+
+# API Configuration
+API_ENDPOINT = os.getenv("API_ENDPOINT")
+API_KEY = os.getenv("API_KEY")
+MODEL_NAME = os.getenv("MODEL_NAME")
+
+# Test Configuration
+MAX_RETRIES = int(os.getenv("MAX_RETRIES", "3"))
+TIMEOUT = int(os.getenv("TIMEOUT", "30"))
+REQUEST_DELAY = float(os.getenv("REQUEST_DELAY", "1.0"))
+
+# Logging
+LOG_LEVEL = os.getenv("LOG_LEVEL", "INFO")
+LOG_FILE = os.getenv("LOG_FILE", "test_results.log")
+```
+
+**3. Add `.env` to `.gitignore`:**
+
+```bash
+echo ".env" >> .gitignore
+```
+
+### Provider-Specific Examples
+
+#### OpenAI
+
+```bash
+API_ENDPOINT=https://api.openai.com/v1/chat/completions
+API_KEY=sk-proj-xxxxxxxxxxxxxxxxxxxxx
+MODEL_NAME=gpt-4
+```
+
+#### Anthropic (Claude)
+
+```bash
+API_ENDPOINT=https://api.anthropic.com/v1/messages
+API_KEY=sk-ant-xxxxxxxxxxxxxxxxxxxxx
+MODEL_NAME=claude-3-opus-20240229
+```
+
+#### 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
+API_KEY=none
+MODEL_NAME=llama-2-7b
+```
+
+---
+
+## 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)