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
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
📖 Reader Mode
~2 min readAbstract: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.
| Comments: | Preprint |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.23927 [cs.AI] |
| (or arXiv:2606.23927v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.23927 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Roy Betser [view email]
[v1]
Mon, 22 Jun 2026 20:46:56 UTC (4,698 KB)
— Originally published at arxiv.org
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →HOBA: Hierarchical On-Policy Bidding Agents for Adaptive Online Advertising
HOBA (Hierarchical On-policy Bidding Agents) is a novel hierarchical reinforcement learning framework that enhances online advertising bidding systems by improving adaptability and reducing hyperparameter tuning costs. It utilizes a for hyperparameter inference, a SARSA agent for expert model selection, and a dynamic expert pool for bid execution, achieving a +3.6% increase in target cost during large-scale deployment and outperforming state-of-the-art baselines on AuctionNet.