Efficient and Precise Dynamic Construction of Control Flow Graphs
Bibliographic record
Abstract
The extraction of high-level information from binary code is an important problem in programming languages, whose solution supports the detection of malware in binary code and the construction of dynamic program slices. The Control Flow Graph is one of the instruments used to represent the structure of binary programs. Most solutions to reconstruct CFGs from binary programs rely on purely static techniques, based either on data-flow analyses, or in type inference. In contrast, in this work we use a purely dynamic approach to such a purpose. Our technique can be used alone, or in combination with static analysis tools. We demonstrate that it is possible to verify completeness in several real-world programs. We also show how to combine our technique with DynInst, the current state-of-the-art static CFG reconstructor. By providing DynInst with extra information, we improve its capacity to deal with indirect jumps. Our dynamic CFG reconstructor has been implemented on top of valgrind. When applied on cBench, this implementation is able to completely cover 36% of all the functions available in that suite. It adds an average overhead of 43x onto the execution of the original programs. Although expressive, this overhead is almost four times lower than the overhead of DCFG, a tool distributed by Intel, and built on top of PinPlay.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".