Benchmark RadarRIFT-Bench
RIFT-Bench is described as a methodology for dynamic red-teaming of agentic AI systems, using a graph representation to enable unified evaluations across diverse agentic architectures. It operates in two phases: Discovery and Scanning, deploying adaptive adversarial attacks and producing evaluation reports.
- Released
- 2026-06-22
- Readiness
- Paper only
- Primary field
- General AI
Why it matters
As agentic AI systems become more autonomous, they introduce new attack surfaces beyond traditional LLM vulnerabilities. RIFT-Bench aims to provide a scalable foundation for security evaluation across heterogeneous agentic architectures, which could aid in comparing the robustness of different systems and evaluating mitigation strategies.
Motivation
Agentic AI systems powered by large language models (LLMs) are rapidly evolving into autonomous decision-making systems, exposing attack vectors beyond those of traditional LLM vulnerabilities.
Primary resources
Benchmark Radar records only publicly supported details and links back to primary sources for verification.