ARES documentation

Welcome to ARES – AI Robustness Evaluation System. ARES is a framework developed by IBM Research to support automated red-teaming of AI systems. It helps researchers and developers evaluate robustness of AI applications through modular, extensible components.

Red-team AI systems with ease using 4 key components: target, goals, strategy, and evaluation. Test your AI builds with targeted metrics for security and reliability.

🚀 What Can ARES Do?

ARES enables:

🧠 Automated Red-Teaming: Simulate adversarial scenarios to test AI models.
🛠️ Intent Definition: Create structured red-teaming exercises using YAML configuration.
🔌 Plugin Integration: Extend functionality with custom plugins for attacks, goals (datasets), models, and metrics.
🧬 Custom Pipelines: Chain together plugins to build complex evaluation workflows.

🔗 Plugin Ecosystem

ARES includes a rich set of plugins:

🎯 Attack Plugins: Prompt injection, encoding, gradient-based, and multi-turn attacks.
🔗 Connector Plugins: Interface with models like HuggingFace transformers, OpenAI APIs, and local deployments.
🎓 Goals Plugins: Load and preprocess datasets from HuggingFace, local files, or custom sources.
📊 Evaluation Plugins: Assess model responses using keyform or model-as-a-judge approaches, measuring accuracy, toxicity, privacy leakage, and more.

📦 Each plugin is self-contained. You need to install plugins individually before using them in ARES.

🧭 What’s Next

ARES is evolving to support:

🛡️ OWASP Mapping Intents: Align red-teaming scenarios with OWASP AI Security guidelines.
🧪 Expanded Plugin Library: More attack types, model integrations, and evaluation metrics.
🤝 Community Contributions: Support for external plugin repositories and shared scenarios.
📈 Visualization Tools: Dashboards and reports for scenario outcomes.
📑 Advanced Reporting: Human-readable summaries and machine-readable outputs (JSON, CSV) for downstream analysis.

💙 IBM ❤️ Open Source AI

ARES has been brought to you by IBM.

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