From Lab to Reality: A Practical Evaluation of Deep Learning Models and LLMs for Vulnerability Detection
arXiv:2512.10485v2 Announce Type: replace-cross Abstract: Vulnerability detection methods based on deep learning (DL) have shown strong performance on benchmark datasets, yet their real-world effectiveness remains underexplored. Recent work suggests that both graph neural network (GNN)-based and transformer-based models, including large language models (LLMs), yield promising results when evaluated on curated benchmark datasets. These datasets are typically characterized by consistent data distributions and heuristic or partially noisy labels. In this study, we systematically evaluate two repr
The proliferation of complex software and AI models necessitates advanced vulnerability detection, and deep learning and LLMs are the leading candidates for automation in this domain.
This research provides critical validation (or lack thereof) for the real-world applicability of AI-driven vulnerability detection, impacting cybersecurity and software development strategies.
The understanding of how effectively advanced AI models, particularly LLMs, can transition from benchmark performance to practical, real-world vulnerability detection in codebases.
- · Cybersecurity firms developing AI-driven tools
- · Software developers adopting AI for code security
- · AI model developers specializing in code analysis
- · Organizations relying solely on traditional vulnerability detection methods
Improved, automated cybersecurity measures for software development.
A potential acceleration of AI-driven software assurance and auditing, influencing regulatory compliance.
Reduced human effort in security reviews, freeing up cybersecurity experts for more complex threat analysis and strategic defense.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG