Combining static analysis and targeted symbolic execution for scalable bug-finding in application binaries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Symbolic execution is an automated technique for program analysis that has recently become practical due to advances in constraint solvers. Symbolic execution eventually enumerates all feasible program executions, check assertions on all values of varaibles in a program path, and can prioritize executions of interest. However, path explosion, the fact that the number of program executions is typically at least exponential in the size of the program, hinders the adoption of symbolic execution in the real world. In this paper, we present a method for generating test-cases using symbolic execution which reach a given potentially buggy target statement. Such a potentially buggy program statement can be found by static program analysis or from crash-reports given by the users. The test-case generated by our technique serves as a proof of the bug. Generating crashes at the target statement have many applications including re-producing crashes, checking warnings generated by static program analysis tools, or analysis of source code patches in code review process. By constantly steering the symbolic execution along the branches that are most likely to lead to the target program statement and pruning the search space that are unlikely to reach the target, we were able to detect deep bugs in real programs. To tackle the memory requirement due to the exponential growth of program paths, we propose a new scheme to manage program execution paths without exhausting system memory. Experiments on real-life programs demonstrate that our tool WatSym, built on selective symbolic execution engine S2E, can generate crashing inputs in feasible time and order of magnitude better than symbolic approaches (as embodied by S2E) failed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it