RIFT-Bench: Dynamic Red-teaming For Agentic AI Systems
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Researchers have introduced RIFT-Bench, a methodology for dynamic red-teaming that enables unified security evaluations across diverse agentic AI architectures. RIFT-Bench operates in two automated phases to extract system structure and deploy adaptive adversarial attacks. It has been demonstrated to be effective across 45 agentic systems, showing its ability to generalize to heterogeneous architectures.
Researchers have introduced RIFT-Bench, a new graph-based methodology designed to perform dynamic red-teaming and security evaluations for agentic AI systems. The automated framework operates in two distinct phases, starting with discovering system structures and followed by deploying adaptive adversarial attacks to detect vulnerabilities. Tested across 45 different agentic systems, the approach effectively generalizes across diverse architectures and supports the direct evaluation of mitigation strategies.