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Improving TTP Alignment in LLM-Based Malware Artifact Generation via Hybrid Verification

DriftGuard-APT: Concept Drift-Aware Unsupervised Graph Learning for APT Detection on Provenance Streams

Action-Graph: A Graph-Aware Multi-Step LLM Pipeline for MITRE ATT&CK TTP Extraction from Cyber Threat Intelligence

Evidence-Preserving Multi-Agent LLMs for SOC Threat Hunting with Different Collaboration and Prompting Strategies

Interpretable Detection and Threat Characterization of Malicious PowerShell Scripts via Multi-Level Representation Fusion and LLM Reasoning

Malicious PowerShell scripts remain a practical challenge for enterprise security because attackers can combine obfuscation, fileless execution, and legitimate administrative utilities to evade detectors…

ContractShield: Bridging Semantic-Structural Gaps via Hierarchical Cross-Modal Fusion for Multi-Label Vulnerability Detection in Obfuscated Smart Contracts

Smart contracts are increasingly targeted by adversaries employing obfuscation techniques such as bogus code injection and control-flow manipulation to evade vulnerability detection. Existing multimodal…

FedAug-CTI: A Federated Framework with LM-Based Cyber Threat Intelligence Generation for Hierarchical Tactic-Technique Mapping

Mapping unstructured Cyber Threat Intelligence (CTI) reports to MITRE ATT\&CK Tactics and Techniques is essential for threat understanding but remains largely manual due to…

SSCFuzz: Combining LLM-Generated Transaction Sequences with Undirected Exploration for Smart Contract Fuzzing

Smart contracts manage substantial financial value yet resist testing: valid inputs follow an application binary interface (ABI), exploitable bugs need multi-step stateful sequences, and…

VulnPrism: Explainable Multi-Agent Vulnerability Detection via Multi-View Security Analysis

Large language models show promise for source-code vulnerability detection, but a reliable verdict also depends on data dependencies, execution paths, and protective conditions that…