Display benchmark results
Upload ML model to Hugging Face Hub
Evaluate RAG systems with visual analytics
Explore and manage STM32 ML models with the STM32AI Model Zoo dashboard
Browse and submit model evaluations in LLM benchmarks
Analyze model errors with interactive pages
Measure BERT model performance using WASM and WebGPU
View and compare language model evaluations
Benchmark models using PyTorch and OpenVINO
Optimize and train foundation models using IBM's FMS
Evaluate adversarial robustness using generative models
Evaluate code generation with diverse feedback types
Evaluate and submit AI model results for Frugal AI Challenge
Redteaming Resistance Leaderboard is a benchmarking tool designed to evaluate the performance of AI models under adversarial attacks. It provides a platform to test and compare the resistance of different models to red teaming strategies, helping researchers and developers identify strengths and weaknesses in their systems.
• Leaderboard System: Displays rankings of models based on their resistance to adversarial attacks.
• Benchmarking Metrics: Provides detailed metrics on model performance under various red teaming scenarios.
• Customizable Attacks: Allows users to define and test specific types of adversarial inputs.
• Result Visualization: Offers graphical representations of benchmark results for easier analysis.
• Performance Tracking: Enables tracking of model improvements over time.
• Scenario Customization: Supports testing against real-world and hypothetical adversarial scenarios.
1. What does "red teaming" mean in this context?
Red teaming refers to the process of attacking a system (in this case, an AI model) to test its resistance and identify vulnerabilities.
2. How do I interpret the benchmark results?
Benchmark results show how well your model performs under adversarial conditions. Lower scores indicate weaker resistance, while higher scores suggest better robustness.
3. Can I test custom adversarial scenarios?
Yes, the leaderboard allows users to define and test custom adversarial scenarios, providing flexibility for specific use cases.