Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 10·5 min read
Black-Box Red Teaming of Agentic AI: A Taxonomy-Driven Framework for Automated Risk Discovery
TL;DR
A new black-box framework for evaluating agentic AI systems identifies significant vulnerabilities, revealing an average governance risk of 56.25% and privacy risk of 65%. This approach enables automated risk discovery without needing privileged access.
✦ Why It Matters
Implement this framework to proactively identify and mitigate risks in your AI systems today.
Key Takeaways
- Adopt the black-box framework for risk evaluation in AI systems.
- Utilize SAGE-RT for automated adversarial scenario generation.
- Focus on governance and privacy risks in multi-agent configurations.
- Conduct human validation of AI behaviors using LLM judges.
How It Works
The framework employs a seven-domain taxonomy to classify observable behaviors of agentic AI into distinct risk categories. It automates the red teaming process through SAGE-RT, which generates numerous adversarial scenarios, allowing for comprehensive testing of the AI's responses.
Human evaluators, using LLMs, validate the outcomes to ensure accuracy in identifying vulnerabilities.