This paper explores the causes of inconsistent vulnerability scanner findings in the open-source ecosystem.

The paper introduces TorchSight, an open-source local system using a fine-tuned Qwen 3.5 27B model that achieves high accuracy (95.0%) in classifying sensitive security documents without relying on ex…

The paper conducts an empirical evaluation of automated vulnerability detection tools across multiple software ecosystems using a curated ground-truth dataset derived from OSV, highlighting systematic…

The paper introduces a novel, large-scale dataset of vulnerable code snippets linked to CAPEC and CWE, generated using advanced LLMs, to improve automatic vulnerability detection.

This systematic mapping survey reviews label-efficient approaches for code vulnerability detection, synthesizing five paradigm families and providing a decision guide to navigate trade-offs.

The paper demonstrates that security patch detection models trained solely on publicly reported vulnerabilities (NVD) perform poorly when tested on real-world, unreported 'in-the-wild' patches, sugges…

The paper introduces RAVEN, a Retrieval-Augmented Vulnerability Exploration Network, which uses LLM agents and RAG to automatically generate comprehensive, structured vulnerability analysis reports fo…

VulGD is a dynamic, open-access graph database that aggregates cybersecurity data from multiple sources and uses LLM embeddings to improve vulnerability representation and risk assessment.

The paper demonstrates that static malware classifiers often rely on superficial artifacts like packing and metadata rather than true malicious semantics, using the TRUSTEE interpretability tool to di…

The paper introduces the first byte-native Large Language Model (LLM) capable of analyzing raw executable binary data, achieving high accuracy in tasks like malware and architecture classification.

The paper proposes VulGNN, a lightweight Graph Neural Network (GNN) model, which achieves vulnerability detection performance comparable to large language models (LLMs) while being significantly small…

This systematic literature review analyzes existing methods, models, and instruments for assessing human vulnerability in cybersecurity, concluding that current approaches are fragmented and lack a dy…

The paper empirically evaluates domain-adapted and general-purpose LLMs for structured threat modelling (STRIDE on 5G security), finding that domain adaptation and model size do not guarantee reliable…

The paper introduces STRIKE, a multi-dimensional structured taxonomy designed to provide a comprehensive and unified framework for classifying the rapidly evolving complexity of modern cybercrimes.

The paper proposes a graph-learning approach to predict multi-vulnerability attack chains within software supply chains, achieving high accuracy on both component classification and cascade prediction…

This paper introduces a machine learning system that detects phishing emails by analyzing contextual features from the entire email body content, achieving 95.41% accuracy using Logistic Regression.

This paper measures the prevalence of recurring vulnerability patterns (variants) across multiple AI infrastructure repositories and proposes INFRASCOPE, a framework to automatically detect these vari…

The paper proposes SAGE, a framework that uses Signal-Amplified Guided Embeddings to overcome 'Signal Submersion' in LLMs, significantly boosting vulnerability detection accuracy across multiple progr…

The paper introduces Symbolicate-Enrich-Sample, a low-cost pipeline that drastically reduces the search space of a whole operating system by prioritizing vulnerable functions, turning millions of pote…

This paper demonstrates that Concept Bottleneck Models (CBMs), despite their interpretability, are highly vulnerable to targeted adversarial attacks that manipulate semantic concepts, and proposes SPE…