paperJune 2026Unreviewed

RIFT-Bench: Dynamic Red-teaming For Agentic AI Systems

Yarin Yerushalmi Levi, Roy Betser, Amit Giloni, Lidor Erez, Itay Gershon, Oren Rachmil, Sindhu Padakandla, Roman Vainshtein

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Abstract

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. Existing security evaluations are often tied to specific implementations or domains, limiting unified comparison across heterogeneous systems. To address this gap, we introduce RIFT-Bench, a graph representation-driven methodology for dynamic red-teaming that enables unified evaluations across diverse age

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NIST AI Risk Management Framework

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@misc{levi2026riftbench,
  title = {{RIFT-Bench: Dynamic Red-teaming For Agentic AI Systems}},
  author = {Yarin Yerushalmi Levi and Roy Betser and Amit Giloni and Lidor Erez and Itay Gershon and Oren Rachmil and Sindhu Padakandla and Roman Vainshtein},
  year = {2026},
  month = jun,
  eprint = {2606.23927},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2606.23927}
}