Modular Intelligence Spaces (MIS): An eBPF-Based Fail-Secure Framework for Autonomous AI Integration

Abstract: The integration of autonomous AI agents into host operating systems creates a critical friction point between the necessity of operational agency and the non-negotiable requirements of system security. Traditional isolation methods—cloud-based execution, high-overhead MicroVMs, or user-space sandboxing—frequently fail to balance privacy, performance, and robust protection against privilege escalation. Modular Intelligence Spaces (MIS) introduces a kernel-native isolation layer utilizing eBPF LSM (Linux Security Modules) hooks to enforce a "default-deny" security posture at the system call level. Unlike existing solutions, MIS addresses the Time-of-Check-Time-of-Use (TOCTOU) vulnerability by implementing verification based on immutable (inode, device_id) pairs rather than volatile file paths. This framework details an architecture for secure, local AI execution featuring: Dual Bloom Filter Policy Engine: Ensuring O(1) access validation with minimal memory overhead. Deterministic Multi-Stream Logging: Providing a verifiable trace for embodied reinforcement learning and post-incident analysis. Trusted Memory Contexts: Leveraging memfd_secret and secure mmap to prevent side-channel data leakage between the agent and the host. Zero-Cloud Dependency: A privacy-first approach utilizing mature Linux primitives (systemd-nspawn, cgroups v2) for deployment on standard hardware. MIS is not merely a sandbox; it is a foundational protocol designed for the secure, autonomous coexistence of human operators and digital agents within a shared computational environment.