RIFT-Bench: Dynamic Red-teaming For Agentic AI Systems
Quick Answer
RIFT-Bench introduces a dynamic red-teaming methodology for evaluating agentic AI systems, enabling unified assessments across 45 diverse architectures.
Quick Take
RIFT-Bench introduces a dynamic red-teaming methodology for evaluating agentic AI systems, enabling unified assessments across 45 diverse architectures. It employs a two-phase automated process—Discovery and Scanning—to extract system structures and deploy adaptive adversarial attacks, effectively generalizing across heterogeneous implementations and supporting mitigation strategy evaluations.
Key Points
- RIFT-Bench evaluates 45 agentic AI systems across diverse implementations.
- The methodology includes two phases: Discovery and Scanning.
- It utilizes adaptive adversarial attacks for comprehensive evaluations.
- RIFT-Bench supports direct evaluation of mitigation strategies.
- The approach generalizes effectively to heterogeneous agentic architectures.
Paper Resources
Article Content
From source RSS / original summaryarXiv: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.
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