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April 23, 2026Open Access

Cross-Domain Vulnerability Detection using LLMs: Knowledge Transfer from Contemporary Software to AI/ML-Specific Software

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Authors

MSMiltiadis SiavvasIKIlias KalouptsoglouDKDionysios Kehagias

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Overview

Cross-domain vulnerability detection reveals the importance of domain adaptation in AI/ML software security.

Key Points

  • Examine if vulnerability detection models trained on contemporary software can accurately detect vulnerabilities in AI/ML-specific software.
  • Construct a dataset of vulnerable and non-vulnerable Python functions from contemporary and AI/ML-specific subsets.
  • Fine-tune CodeBERT and CodeGPT on the contemporary subset and evaluate on the AI/ML subset.
  • Perform gradual adaptation by adding varying percentages of target domain samples and analyze representation drift with UMAP.
  • AI-based vulnerability detection models trained on contemporary software performed poorly on AI/ML-specific software without domain adaptation.
  • A moderate amount of domain-specific data (as low as 5%) improved detection accuracy significantly.
  • Unique characteristics of AI/ML-specific software necessitate tailored approaches for effective vulnerability detection.

Cite This Study

Siavvas et al. (2026) studied this question.

synapsesocial.com/papers/69e9bb6285696592c86ed193https://doi.org/10.5281/zenodo.19679663
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  1. 1Large Language Models for Source Code Vulnerability Detection: A DiverseVul Analysis2024
  2. 2Modern Approaches to Software Vulnerability Detection: A Survey of Machine Learning, Deep Learning, and Large Language Models2025
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  4. 4Data and context matter: towards generalizing AI-based software vulnerability detection2026 · 2 citations
  5. 5Large language model based hybrid framework for automatic vulnerability detection with explainable AI for cybersecurity enhancement2025