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arXiv CS.AI·1d ago·5 min read
AuthGateBench: Benchmarking LLMs as Authorization Gate
TL;DR
AuthGateBench is a new benchmark designed to evaluate large language models (LLMs) in authorization decision-making under benign requests. It reveals that models often make unstable decisions, blocking authorized actions, but can improve accuracy with additional environmental signals.
✦ Why It Matters
Evaluate your LLM's authorization capabilities using the AuthGateBench framework to enhance decision-making accuracy.
Key Takeaways
- Implement AuthGateBench to benchmark your LLM's authorization performance.
- Analyze failure modes to identify weaknesses in your system.
- Incorporate environmental signals to improve decision accuracy.
- Monitor for scope escalation risks in automated approval systems.
How It Works
AuthGateBench generates contrast pairs of requests with identical actions but different contexts, allowing for an evaluation of whether LLMs consider the context in their decision-making. This approach helps identify how well models can differentiate between authorized and unauthorized actions based on nuanced request details.