What does this research mean for the field?

Encoder-based models trained on high-quality, diverse datasets significantly improve the accuracy and cross-project generalizability of AI-based software vulnerability detection. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To investigate how training data quality, model architecture, and parameter configurations influence the ability of AI-based vulnerability detection systems to generalize to unseen codebases.

August 22, 2026International Journal of Information SecurityOpen Access

Data and context matter: towards generalizing AI-based software vulnerability detection

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Authors

RSRijha SafdarNational University of Sciences and Technology DMDanyail MateenNational University of Computer and Emerging Sciences SASyed Taha AliWestern University

Discussion

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Implication

Empirical evaluation demonstrates enhanced cross-project vulnerability detection in unseen software codebases, highlighting the importance of curated training datasets and encoder-based models.

Key Points

To investigate how training data quality, model architecture, and parameter configurations influence the ability of AI-based vulnerability detection systems to generalize to unseen codebases.
Curated the VulGate dataset by removing mislabeled and duplicate samples, updating vulnerabilities, and adding metadata, hard samples, and dedicated test sets.
Benchmarked multiple encoder-only and decoder-only model architectures across parameter configurations on the BigVul dataset and unseen project codebases.
Encoder-based models achieved superior accuracy and cross-project generalization compared to decoder-based architectures.
The proposed approach demonstrated a 6.8% improvement in recall on the benchmark BigVul dataset and outperformed other models on unseen codebases.

Cite This Study

Safdar et al. (2026) studied this question.

synapsesocial.com/papers/6a895e6bca7ade938187c78e https://doi.org/10.1007/s10207-026-01282-6

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August 22, 2026International Journal of Information SecurityOpen Access

Data and context matter: towards generalizing AI-based software vulnerability detection

View Full Paper

Ask AI

Bookmark

Authors

RSRijha SafdarNational University of Sciences and Technology DMDanyail MateenNational University of Computer and Emerging Sciences SASyed Taha AliWestern University

Discussion

Member takes

Implication

Empirical evaluation demonstrates enhanced cross-project vulnerability detection in unseen software codebases, highlighting the importance of curated training datasets and encoder-based models.

Key Points

To investigate how training data quality, model architecture, and parameter configurations influence the ability of AI-based vulnerability detection systems to generalize to unseen codebases.
Curated the VulGate dataset by removing mislabeled and duplicate samples, updating vulnerabilities, and adding metadata, hard samples, and dedicated test sets.
Benchmarked multiple encoder-only and decoder-only model architectures across parameter configurations on the BigVul dataset and unseen project codebases.
Encoder-based models achieved superior accuracy and cross-project generalization compared to decoder-based architectures.
The proposed approach demonstrated a 6.8% improvement in recall on the benchmark BigVul dataset and outperformed other models on unseen codebases.

Cite This Study

Safdar et al. (2026) studied this question.

synapsesocial.com/papers/6a895e6bca7ade938187c78e https://doi.org/10.1007/s10207-026-01282-6

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