Comprehensive kernel instrumentation via dynamic binary translation
Bibliographic record
Abstract
Dynamic binary translation (DBT) is a powerful technique that enables fine-grained monitoring and manipulation of an existing program binary. At the user level, it has been employed extensively to develop various analysis, bug-finding, and security tools. Such tools are currently not available for operating system (OS) binaries since no comprehensive DBT framework exists for the OS kernel. To address this problem, we have developed a DBT framework that runs as a Linux kernel module, based on the user-level DynamoRIO framework. Our approach is unique in that it controls all kernel execution, including interrupt and exception handlers and device drivers, enabling comprehensive instrumentation of the OS without imposing any overhead on user-level code. In this paper, we discuss the key challenges in designing and building an in-kernel DBT framework and how the design differs from user-space. We use our framework to build several sample instrumentations, including simple instruction counting as well as an implementation of shadow memory for the kernel. Using the shadow memory, we build a kernel stack overflow protection tool and a memory addressability checking tool. Qualitatively, the system is fast enough and stable enough to run the normal desktop workload of one of the authors for several weeks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".