Combining Structured Static Code Information and Dynamic Symbolic Traces for Software Vulnerability Prediction
Bibliographic record
Abstract
Deep learning (DL) has emerged as a viable means for identifying software bugs and vulnerabilities. The success of DL relies on having a suitable representation of the problem domain. However, existing DL-based solutions for learning program representations have limitations - they either cannot capture the deep, precise program semantics or suffer from poor scalability. We present Concoction, the first DL system to learn program presentations by combining static source code information and dynamic program execution traces. Concoction employs unsupervised active learning techniques to determine a subset of important paths to collect dynamic symbolic execution traces. By implementing a focused symbolic execution solution, Concoction brings the benefits of static and dynamic code features while reducing the expensive symbolic execution overhead. We integrate Concoction with fuzzing techniques to detect function-level code vulnerabilities in C programs from 20 open-source projects. In 200 hours of automated concurrent test runs, Concoction has successfully uncovered vulnerabilities in all tested projects, identifying 54 unique vulnerabilities and yielding 37 new, unique CVE IDs. Concoction also significantly outperforms 16 prior methods by providing higher accuracy and lower false positive rates.
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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 | 0.001 |
| 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 | 0.001 |
| 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 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".