VulANalyzeR: Explainable Binary Vulnerability Detection with Multi-task Learning and Attentional Graph Convolution
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
Software vulnerabilities have been posing tremendous reliability threats to the general public as well as critical infrastructures, and there have been many studies aiming to detect and mitigate software defects at the binary level. Most of the standard practices leverage both static and dynamic analysis, which have several drawbacks like heavy manual workload and high complexity. Existing deep learning-based solutions not only suffer to capture the complex relationships among different variables from raw binary code but also lack the explainability required for humans to verify, evaluate, and patch the detected bugs. We propose VulANalyzeR, a deep learning-based model, for automated binary vulnerability detection, Common Weakness Enumeration-type classification, and root cause analysis to enhance safety and security. VulANalyzeR features sequential and topological learning through recurrent units and graph convolution to simulate how a program is executed. The attention mechanism is integrated throughout the model, which shows how different instructions and the corresponding states contribute to the final classification. It also classifies the specific vulnerability type through multi-task learning as this not only provides further explanation but also allows faster patching for zero-day vulnerabilities. We show that VulANalyzeR achieves better performance for vulnerability detection over the state-of-the-art baselines. Additionally, a Common Vulnerability Exposure dataset is used to evaluate real complex vulnerabilities. We conduct case studies to show that VulANalyzeR is able to accurately identify the instructions and basic blocks that cause the vulnerability even without given any prior knowledge related to the locations during the training phase.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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 it