Check Point Research • 1w
The LLM "Benchmark Gap": Addressing Security Risks in Agentic AI Workflows
Current LLM safety benchmarks fail to account for the transition from isolated chatbots to agentic workflows capable of autonomous tool execution. As LLMs are integrated as orchestrators for enterprise databases and external APIs, the attack surface shifts from simple prompt injection to complex indirect injections and unauthorized tool triggering. This "Benchmark Gap" represents the discrepancy between high safety scores in sterile environments and critical security failures in production-grade agents. Bridging this gap requires transitioning from static evaluations to continuous, autonomous red teaming that simulates adversarial behavior within production-mirroring environments to identify "unknown unknowns" in agentic logic.
Links:Check Point Research, Kili-technology, Nhimg, Galileo, Arxiv, Confident-ai •