VulPatrol: Interprocedural Vulnerability Detection and Localization through Semantic Graph Learning
Bibliographic record
Abstract
The growing complexity of software systems and the management of large, rapidly evolving codebases necessitate the analysis of immense volumes of lines per day due to code modifications and refactoring. Despite the use of static and dynamic analysis, test coverage, and rigorous code reviews, traditional methods often fail to accurately detect all security vulnerabilities, resulting in significant risks in production software. Recently, deep learning models have shown promising possibilities for improving vulnerability detection. Yet, there remains a clear gap between the abilities of current deep learning approaches and the level of performance required for precise source code vulnerability detection. To bridge this gap, it is crucial to develop enhancements in two fundamental areas: a code representation that accurately captures the semantics of programs and a model architecture with adequate expressiveness to analyze this representation effectively. We introduce VulPatrol, a semantic-aware, deep neural network-based system that constructs LLVM-IR interprocedural code property graphs from C/C++ source code. VulPatrol employs message-passing neural networks to capture complex dependencies and dynamic interactions within the code. As a result, it enhances the model's ability to classify potential vulnerabilities. Furthermore, we generate the first Vulnerability database based on compilable C/C++ open-source software to LLVM-IR, along with an obfuscated version. Our extensive evaluation on different benchmark datasets, including real-world programs, shows that VulPatrol outperforms the state-of-the-art baselines, improving the F1 measure by up to 12% for identifying vulnerable functions. Additionally, we evaluate VulPatrol on obfuscated code, which yields superior results regarding string variation and dissimilarity of the original codebase.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".