RS-2026-099 - Blind Spot Mapping of Autonomous AI Defensive Systems via Live-Fire Adversarial Swarm Instrumentation

We present the first empirical blind spot analysis of an autonomous AI defensive swarm system (M999 SENTINEL Swarm v2.0.0) conducted using a purpose-built adversarial validation instrument (M666 RECOIL). Ten structured attack campaigns comprising 71 discrete attack phases were executed against a live M999 instance under air-gapped laboratory conditions. RECOIL employs five specialised adversarial agents (WRAITH, SPECTER, SHADE, PHANTOM, REVENANT) coordinated by a campaign orchestration engine, with all detections recorded via real HTTP calls (zero simulations, zero stubs). Overall detection rate across 10 campaigns was 15.5% (95% Wilson CI: 8.9%-25.7%); four campaigns achieved 100% evasion across all phases. The core finding is that M999's defensive surface and attack surface are architecturally disjoint, with no content-level intent analysis bridging them. Findings have direct implications for the design of next-generation AI defensive monitoring architectures.