The deployment of deep-learning vulnerability detectors in production remains difficult. Models are large, false-positive rates are high, output is opaque, and a persistent gap separates benchmark performance from real-world utility. The objective of this work is to close part of that gap by combining a learned detector with interpretable rules so that accuracy, efficiency, and actionability are achieved together. A hybrid framework is therefore presented in which a lightweight edge-conditioned GNN of 71,810 parameters, named FastVulnGNN, trained in 96.2 s on a single CPU core, is paired with rule-based heuristic detection for six C/C++ vulnerability classes, namely buffer overflows, format-string defects, null-pointer dereferences, double-free errors, integer overflows, and race conditions. On the MegaVul dataset, an accuracy of 71.1%, an F1 score of 0.70, and an AUC-ROC of 0.77 are obtained by the GNN component. On a production codebase of 499 files and 312,758 lines of code, the full hybrid scan completes in 5.5 s, which corresponds to about 57,000 lines per second, without any GPU hardware. Per-file risk tiers and pattern-level explanations are produced, and these are suitable for continuous-integration use. The significance of this work lies in demonstrating that a deployable, explainable detector can be assembled from compact components, and an edge-type ablation study, a cross-dataset evaluation, and a per-vulnerability analysis are reported to characterize the approach.

Software vulnerabilities are a primary cause of security breaches, and their automated detection at scale has therefore become a pressing concern for both industry and academia. Most Graph Neural Network (GNN) approaches to vulnerability detection treat code graphs as homogeneous structures, and the semantic distinctions between Abstract Syntax Tree (AST) edges, Control-Flow Graph (CFG) edges, and data-flow dependency edges are consequently discarded. The main objective of this study is to determine whether explicitly conditioning message passing on edge type yields accurate yet lightweight detection. To this end, an edge-conditioned GNN named FastVulnGNN is proposed, in which the message-passing computation is conditioned on edge-type information drawn from Code Property Graphs (CPGs). FastVulnGNN operates on Joern-produced CPGs that contain 33 node types and 21 edge types, so that the full heterogeneous graph structure is preserved. A multi-scale readout mechanism that combines mean, maximum, and learned attention pooling is employed for graph-level classification, and the training configuration, which combines focal loss, label smoothing, and cosine annealing warm restarts, is individually validated by an ablation of the training objective. On the MegaVul dataset of 1904 balanced C/C++ samples, an accuracy of 71.1%, an F1 score of 0.70, and an AUC-ROC of 0.77 are achieved with only 71,810 parameters. Training completes in under two minutes on a single CPU core, and no GPU resources are required. The significance of this work lies in its demonstration that a compact, edge-aware architecture can match independently reproduced results of far larger models while remaining deployable in resource-constrained settings, such as continuous-integration pipelines and developer workstations. This study is deliberately framed as a controlled and reproducible engineering and evaluation contribution rather than as an architectural advance. An edge-type ablation study, a cross-dataset evaluation, and a per-vulnerability analysis are additionally reported to characterize the behavior and limitations of the model.