Kernel-Safe Data Probes: Context-Aware Address Tagging with On-Demand Translation for OS Modules

• Monisha Rengaraj

  Author

This paper presents a software-only data-access monitoring architecture for operating-system kernels that attaches context to memory references and activates selective instrumentation through on-demand binary translation. The design encodes per-object metadata into pointer space to enable type- and field-aware checks at instruction granularity while keeping common kernel paths free of persistent overhead. A dual execution path resolves watched references either via fast inline masking or via trap-driven translation when events are rare, balancing precision and cost under real kernel workloads. The implementation for x86-64 Linux modules supports practical tools including overflow and bounds checking, read-before-write and lifetime misuse detection, leak discovery with selective shadow state, and field-level policy enforcement for tamper-resistant subsystems. Evaluation with microbenchmarks and storage-stack file operations quantifies baseline translation cost and the incremental overheads of precise bounds and fault-mediated paths, explaining how selective activation and object scoping reduce exception storms in hot kernel code. Results show that the approach yields actionable diagnostics for module reliability and security while preserving predictable performance through configurable granularity and targeted instrumentation.

• PDF

Copyright (c) 2026 International Journal of Intelligent Systems and Data Science

This work is licensed under a Creative Commons Attribution 4.0 International License.