ADS: AN AGENTIC DETECTION SYSTEM FOR ENTERPRISE AGENTIC AI SECURITY

We present ADR (Agentic AI Detection and Response), the first large-scale, production-proven enterprise framework for securing AI agents operating through the Model Context Protocol (MCP). We identify three persistent challenges in this domain: (1) limited observability, as existing telemetry fails to capture reasoning and tool-execution chains; (2) insufficient robustness, given vast, dynamic enterprise contexts and extreme class imbalance; and (3) high detection costs, as LLM-based inference is computationally expensive. ADR addresses these challenges via three components: the ADR Sensor for high-fidelity agentic telemetry, the ADR Explorer for continuous red teaming and hard-example generation, and the ADR Detector for scalable, two-tier online detection combining fast triage with context-aware reasoning. On ADR-Bench (302 tasks, 17 techniques, 133 MCP servers), ADR achieves zero false positives while detecting 67% of attacks—outperforming three state-of-the-art baselines (ALRPHFS, GuardAgent, LlamaFirewall) by 2–4×. On AgentDojo (public prompt injection benchmark), ADR detects all attacks with only three false alarms out of 93 tasks. Over ten months of telemetry, ADR sustained reliable detection in production, uncovering credential exposures and enabling a shift-left prevention layer with 97.2% precision. ADR’s source code and benchmark will be publicly available.