Advancing AI Security Science

We present a novel constitutional framework for governing multi-agent AI systems. Our approach defines immutable security rules that cannot be overridden by prompt injection, context manipulation, or privilege escalation. We demonstrate 99.97% injection defense rate across 14 million adversarial test cases.

We introduce a formally verified input sanitization protocol that provably prevents all known classes of prompt injection attacks. Our system uses multi-stage validation with cryptographic attestation to ensure no malicious payload reaches the language model inference layer.

Analysis of attack surfaces in multi-agent orchestration systems. We identify 7 novel attack vectors specific to agent-to-agent communication and propose mitigation strategies including context isolation, output verification, and consensus-based action approval.

We describe the Agentic Security Fabric — a novel architecture where 14 specialized security agents operate as an autonomous defense swarm. Each agent has a defined role, operates independently, and contributes to collective threat intelligence through a neural command layer.

Real-world deployment analysis of OmniaGuard SDK across 17 companies with 14 AI agents. Covers challenges of cross-entity governance, data sovereignty, and unified threat response in a multi-company environment.

We release the largest publicly available prompt injection test suite containing 14 million adversarial prompts across 23 attack categories. Includes direct injection, indirect injection, recursive attacks, and novel multi-turn manipulation techniques.