The paper constructs a large, adversarial malware dataset from real-world binaries, demonstrating high evasion rates and showing that even small amounts of poisoned data can severely compromise malwar…

This paper introduces a novel malware detection system for macOS by utilizing domain-specific static features, achieving state-of-the-art performance and demonstrating strong generalization capabiliti…

This paper addresses the lack of research on adversarial malware generation for Linux ELF binaries by developing a new semantic-preserving generator that achieves a high evasion rate against modern de…

The paper proposes a model-agnostic framework to evaluate combining Active Learning (AL) and Semi-Supervised Learning (SSL) techniques for malware detection, demonstrating that these combined methods…

The paper proposes a unified closed-loop threat taxonomy to systematically analyze and defend foundation models by explicitly framing the bidirectional security interactions between data and models.

The paper proposes a certifiably robust malware detection framework using randomized smoothing and feature ablation to guarantee detection accuracy against metamorphic evasion attacks.

The paper introduces Trident, a novel malware detection system that combines static features, LLM-derived behavioral rules, and direct LLM analysis to achieve superior robustness against concept drift…

The paper proposes a framework to intentionally evade malware detectors by adding a small number of benign API imports, successfully demonstrating targeted misclassification into a chosen benign categ…

AsmRAG is a novel framework that improves malware detection by treating it as an evidence-based retrieval task using a code-specialized LLM, achieving high accuracy while providing transparent forensi…

eDySec introduces a deep learning framework for dynamic behavioral analysis that significantly improves the detection of malicious software packages in the PyPI ecosystem by enhancing stability and ex…

This paper systematically analyzes the high cross-architecture transferability of physical adversarial attacks on Vision-Language Models (VLMs) used in autonomous driving, demonstrating that attacks e…

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…

This paper proposes a hybrid feature fusion and voting-based approach for automated malware detection and classification into specific malware families, achieving high performance metrics like an AUC…

The paper proposes an attestation-aware promotion gate to mitigate supply-chain risks in LLM pipelines by cryptographically verifying and enforcing claims about training and release artifacts before d…

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 introduces ABLE, an LLM-based system that automatically generates YARA rules to bypass malware evasion checks in analysis sandboxes, achieving a 79% bypass success rate.

MalwarePT introduces a novel binary-level foundation model, pretrained on Windows PE code-section bytes using a ModernBERT-style encoder, demonstrating superior transfer learning capabilities across v…

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.

The paper proposes DynaHug, a dynamic analysis technique that uses machine learning to detect malicious pre-trained machine learning models by learning the runtime behaviors of benign models, achievin…

The paper proposes a universal robustification framework to enhance drift-adaptive malware detectors against combined concept drift and adversarial attacks, significantly reducing attack success rates…