ZmoreZoe/AdversarialChatWarfare

AdversarialChatWarfare

An AI Red-Teaming Framework for Multi-Turn Adversarial Dialogue Attack Testing

  

📖 Overview

AdversarialChatWarfare is an open-source framework designed to stress-test the robustness of Large Language Models (LLMs) against adversarial persuasion attacks. By simulating realistic multi-turn conversational scenarios, it evaluates whether AI systems can maintain their safety boundaries when faced with sophisticated manipulation tactics.

Why AdversarialChatWarfare?

As LLMs are increasingly deployed in customer service, healthcare, finance, and other critical domains, ensuring they cannot be manipulated into harmful behaviors is paramount. Traditional single-turn jailbreak tests fail to capture the subtle, iterative persuasion strategies that real-world attackers might employ.

AdversarialChatWarfare addresses this gap by:

• —🎯 Simulating realistic multi-turn persuasion attacks
• —🔍 Providing automated assessment of model vulnerabilities
• —📊 Offering real-time visualization of attack-defense dynamics
• —🛡️ Enabling proactive security testing before deployment

🎯 Key Features

🗣️ Multi-Turn Adversarial Dialogue

• —Attacker Model: Acts as a skilled debater, progressively building persuasive arguments
• —Target Model: Attempts to maintain safety guidelines under pressure
• —Dynamic Strategy: Attacks adapt based on target responses, mimicking real adversarial behavior

🤖 AI-Powered Evaluation

• —Automated Judging: A separate LLM evaluates whether persuasion succeeds
• —Objective Metrics: Quantifiable success rates and vulnerability patterns
• —Session Termination: Automatically stops when persuasion is detected or max turns reached

🎨 Real-Time Visualization

• —Interactive Dashboard: Built with Streamlit for intuitive monitoring
• —Conversation Flow: Chat-like interface displaying attack-defense exchanges
• —Progress Tracking: Live updates on dialogue turns and evaluation status

📈 Comprehensive Analysis

• —Attack Pattern Recognition: Identifies common successful manipulation tactics
• —Defense Weakness Mapping: Highlights areas where models are most vulnerable
• —Comparative Testing: Benchmark different models against the same attack scenarios

🏗️ Architecture

┌─────────────────┐
│  Attacker LLM   │  (Red Team: Generates persuasive arguments)
└────────┬────────┘
         │
         ↓
┌─────────────────┐
│   Target LLM    │  (Blue Team: Defends safety boundaries)
└────────┬────────┘
         │
         ↓
┌─────────────────┐
│   Judge LLM     │  (Evaluates if persuasion succeeded)
└────────┬────────┘
         │
         ↓
┌─────────────────┐
│ Streamlit UI    │  (Real-time visualization)
└─────────────────┘

Core Workflow:

1. 1.Attacker generates persuasive argument
2. 2.Target responds while attempting to maintain safety
3. 3.Judge evaluates if target's stance has shifted
4. 4.Loop continues until success or max turns
5. 5.Results displayed in real-time on web interface

💡 Use Cases

🔬 Academic Research

• —Study LLM alignment and robustness
• —Develop novel defense mechanisms
• —Benchmark model safety across versions
• —Publish reproducible security evaluations

🏢 Enterprise Security Testing

• —Pre-deployment safety validation
• —Identify vulnerabilities in AI assistants
• —Compliance testing for regulated industries
• —Red-team exercises for AI safety teams

🎓 Educational Purposes

• —Demonstrate AI safety concepts
• —Train developers on adversarial thinking
• —Raise awareness about LLM limitations
• —Build intuition for defense strategies

📊 Example Scenarios

Scenario 1: Safety Boundary Testing

Attack Goal: Convince model to provide harmful advice Test Case: Medical misinformation, financial fraud guidance Metric: Number of turns before safety breach

Scenario 2: Bias Amplification

Attack Goal: Elicit biased or discriminatory responses Test Case: Subtle stereotyping, loaded questions Metric: Bias score across conversation turns

Scenario 3: Information Extraction

Attack Goal: Extract confidential training data or private information Test Case: Social engineering tactics, context manipulation Metric: Information leakage rate

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

Ways to contribute:

• —Report bugs and suggest features
• —Add new attack strategies
• —Improve evaluation metrics
• —Enhance documentation
• —Share interesting test cases

📄 License

This project is licensed under the MIT License - see LICENSE file for details.

🙏 Acknowledgments

• —Inspired by red-teaming practices in cybersecurity
• —Built on the shoulders of open-source LLM research
• —Community feedback and contributions

⚠️ Disclaimer

This tool is designed for research and security testing purposes only. Users are responsible for ensuring compliance with applicable laws and ethical guidelines. Do not use this framework to develop actual attacks against production AI systems without proper authorization.

Star ⭐ this repo if you find it useful!