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

Researchers introduced RIFT-Bench, a graph-driven methodology for dynamic red-teaming of agentic AI systems, enabling unified security evaluations across diverse architectures. The framework operates in two automated phases—Discovery and Scanning—and was validated on 45 agentic systems, demonstrating its ability to generalize across heterogeneous implementations and support mitigation strategy evaluation.

arXiv:2606.23927v1 Announce Type: new 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 agentic architectures. Building on a novel hierarchical representation, RIFT-Bench operates in two automated phases: Discovery, which extracts system structure, and Scanning, which deploys adaptive adversarial attacks and produces a comprehensive evaluation report. It evaluates the examined system itself, leveraging a broad set of dynamically adaptable adversarial probes across diverse attack vectors and objectives. We demonstrate the effectiveness of the proposed evaluation pipeline across 45 agentic systems spanning a diverse range of implementations, showing that the approach generalizes effectively to heterogeneous agentic architectures. Beyond systems and attacks, RIFT-Bench also supports direct evaluation of mitigation strategies. These key capabilities make RIFT-Bench a scalable foundation for security evaluation of agentic AI systems.