NOProbe: A NOP-Based Dynamic Binary Instrumentation Framework Using Binary Rewriting on x86
Bibliographic record
Abstract
Dynamic Binary Instrumentation (DBI) in user space often suffers from low probe insertion success rates and high execution overhead, due to challenges in handling the compact instruction layouts (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lt $</tex-math></inline-formula> 5 bytes) and complex trampoline placement constraints. Existing techniques are either limited in scope, incur high runtime overhead, or rely on heavyweight code relocation. This paper introduces NOProbe, a lightweight, user-space DBI framework that enables safe and efficient probe insertion using two novel strategies. The first strategy locates trampoline sites by leveraging compiler-generated NOP paddings; the second employs pseudo-NOP instructions to support trampoline placement even when instructions overlap. Additionally, we propose a thread-safe patching algorithm, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lock</i>-<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">redirect</i>-<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">load</i>-<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">arm</i>, for safe runtime code modification. Experimental results show that NOProbe achieves 97%-99% probe effectiveness, reduces probe insertion latency, and maintains very low per-probe execution overhead, even under high probe density and multithreaded workloads.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".