LLM-Enhanced Evolutionary Dependency Graphs: A System Approach to Precise Vulnerability Propagation Tracking

VulRESC: A vulnerability detection framework based on risk path extraction and inter-procedural semantic completion

Software vulnerability detection increasingly relies on learning-based models. However, most existing methods analyze individual functions in isolation, making it difficult to capture vulnerabilities caused by cross-function calls; directly introducing complete call chains can also lead to context expansion and noise accumulation. This paper proposes VulRESC, a vulnerability detection framework based on risk path extraction and interprocedural semantic completion. The method first constructs code property graphs and extracts call paths related to external inputs and high-risk operations through a riskpoint- driven pruning strategy. It then uses large language models to generate structured summaries for callees along the sequence in a bottom-up manner and introduces a variable-name alignment mechanism to bridge the semantic gap across function boundaries. Finally, the proposed DualVulBERT dual-stream model is employed to jointly identify vulnerabilities by fusing source code features and inter-procedural semantic summaries through cross-modal attention. Experimental results on real-world C/C++ vulnerability datasets show that VulRESC achieves an accuracy of 68.03% and an F1-score of 69.37%, outperforming representative existing methods.

HSF-Vul: hierarchical semantic fusion for vulnerability detection

Hybrid Vulnerability Detection: Combining Latent Semantic Analysis with LLM-Assisted Fuzzing via Context Distillation

A hybrid pipeline combining static vulnerability candidate selection using Latent Semantic Analysis with dynamic verification through Large Language Model-assisted fuzzing is presented, which reduces manual effort while still requiring human validation.

SemASTer: Cross-Architecture Binary Code Similarity Detection via AST-Driven Semantic Compensation

Binary Code Similarity Detection (BCSD) plays a crucial role in vulnerability discovery and malware analysis. However, it faces two major challenges in cross-architecture scenarios: syntactic variations arising from architectural differences and semantic distortions induced by compiler optimizations. To address these issues, we propose SemASTer, a robust framework for cross-architecture binary code similarity detection that leverages Abstract Syntax Tree (AST) as its semantic backbone. A degradation-aware channel attention mechanism dynamically incorporates compensatory information only when the AST backbone is deemed unreliable due to aggressive optimizations. Furthermore, we introduce two complementary compensation pathways: behavioral semantic compensation to recover lost runtime dynamics, and control-flow compensation to reconstruct disrupted logical dependencies. Experimental results demonstrate that SemASTer achieves an 8.3% improvement in Recall@1 over the current state-of-the-art method in cross-architecture tasks, and an 11.4% gain under fully heterogeneous compilation settings (varying architectures, compilers, and optimization levels). Moreover, its effectiveness and robustness are validated through real-world vulnerability detection scenarios.

Vulnerability Detection Model Based on Clustering-Aware Heterogeneous Code Graphs

Software vulnerabilities pose a severe threat to system security and stability. To reinforce the hierarchical structure of source code, enhance the distinctiveness of embedded features, and reduce feature confusion, we propose CADetect, a vulnerability detection model based on clustering-aware heterogeneous code graphs. First, a hierarchical clustering algorithm is applied to constrain and optimize the embedding vectors. This enhances the model’s ability to differentiate between diverse node and edge types within the heterogeneous code graphs, while streamlining code structures and reducing redundant information. Subsequently, multi-scale convolutions with varying kernel sizes are designed to extract vulnerability features at different granularities, effectively mitigating the information loss commonly caused by single-scale convolutions. Finally, extensive evaluations on the FFmpeg+Qemu, BigVul, and Reveal datasets demonstrate the effectiveness of the proposed model, and ablation studies confirm the specific contributions of the clustering algorithm and multi-scale convolutions.

Commit-Message-Augmented Static Analysis of Cross-Site Scripting Fixes in Java Web Applications

Cross-Site Scripting (XSS) remains a common and high-impact web vulnerability. Detecting XSS with high precision is difficult because exploitability depends on end-to-end data flow (sources, sanitizers, and sinks), framework-specific rendering semantics, and the correctness of context-aware output encoding. Although secure development lifecycles increasingly integrate static application security testing (SAST) into CI/CD pipelines, practical adoption is often limited by false positives and incomplete modeling of project-specific sanitization and templating behavior. In parallel, machine learning (ML) approaches show promise for learning vulnerability-relevant patterns from code, but they depend on the availability of realistic labeled data. This paper studies a low-cost repository-native signal that is routinely available but underused in ML-assisted static analysis: commit messages. We propose a mining and weak-labeling pipeline for Java repositories that identifies candidate XSS-fix commits using message heuristics grounded in OWASP guidance and CWE taxonomy, aligns commit messages with pre-/post-change code contexts extracted from diffs, and trains classifiers under two feature settings: (i) code-only and (ii) code+commit-message fusion. Across three model families (Random Forest, TextCNN, and BiLSTM), commit-message fusion consistently improves F1 and AUPRC and reduces false positives, suggesting that developer intent provides a useful auxiliary signal for security triage.